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1 ASSESSING THE GENERIC COMPETENCES ACQUIRED BY STUDENTS GRADUATING FROM ITALIAN UNIVERSITIES ANVUR’S FINAL REPORT Rome, 11 March 2014 Reviewed and finalized on 30 July 2014
Transcript of ASSESSING THE GENERIC COMPETENCES ACQUIRED BY … · ASSESSING THE GENERIC COMPETENCES ACQUIRED BY...
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ASSESSING THE GENERIC COMPETENCES ACQUIRED BY STUDENTS GRADUATING
FROM ITALIAN UNIVERSITIES
ANVUR’S FINAL REPORT Rome, 11 March 2014
Reviewed and finalized on 30 July 2014
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Table of Contents
1. Introduction with a concluding comment ...................................................... 10
1.1 Pilot test numbers ........................................................................................................................................ 12
1.2 TECO features ................................................................................................................................................ 12
1.3 Results .............................................................................................................................................................. 13
1.4 Conclusions .................................................................................................................................................... 17
2. Reasons and criteria for the TECO pilot test .................................................. 18
2.1 Reasons for the pilot test .......................................................................................................................... 18 2.1.1. Formal reasons .............................................................................................................................. 18
2.1.2. Substantial reasons........................................................................................................................ 20
2.2. Criteria for the experiment ..................................................................................................................... 21
2.3 Cost of the pilot test and its coverage .................................................................................................. 23
3. Processes, timeline and phases of the pilot test on the generic competences of Italian graduating students ........................................................ 25
3.1 Appointment of the Committee of Guarantors and the Working Group ................................ 26
3.2 Creation and completion of the National Project Office (NPO), national “control room” of the experiment ..................................................................................................................................................... 27
3.3 Selection of the Universities participating to the pilot test ......................................................... 27
3.4 Local governance: the Professors - Institutional Coordinators (ICP), the Administrative Institutional Coordinators (ICA) and the Lead Scorers (LSC) ............................................................ 29
3.5 Selection and adaptation of the test ..................................................................................................... 30 3.5.1. The CAE-ANVUR contract .............................................................................................................. 30
3.5.2. The Performance Task (PT) in TECO .............................................................................................. 32
3.5.3. The 20 multiple choice questions or Selected Response Questions (SRQ) in TECO ................ 33
3.5.4. Adaptation of the CLA+ and its transformation into TECO ........................................................... 33
3.6 Presentation of the experiment to the stakeholders via seminars ........................................... 34
3.7 Set-up of the technological platforms and collection of the contextual variables .............. 35
3.8 Translation and conciliation of the texts ............................................................................................ 38
3.9 Focus group and cognitive laboratory at the University of Camerino .................................... 38
3.10 Validation of the translation after the focus group ...................................................................... 39
3.11 Test Administration.................................................................................................................................. 40
3.12 Test administration management by CINECA and CAE, up to the release of individual results ...................................................................................................................................................................... 44
3.13 Training of Lead Scorers (LSCs) and Scorers (SCs), and scoring of the open-response test (PT) .................................................................................................................................................................. 44
3.14 Data checking and cleaning ................................................................................................................... 46
3.15 Public presentation of the outcomes of the experiment ............................................................ 47
4. Main facts emerging from the experiment ....................................................... 48
4.1 The regularity index, R, in University studies ................................................................................... 48
4.2 The TECO participation index, P ............................................................................................................ 51
4.3 TECO passes the feasibility test in Italy ............................................................................................... 53
4.4 Comparability of TECO results and scores between Italian graduating students and similar student populations in the rest of the world ............................................................................. 58
4.5 The specifically Italian problem of the “two cultures” .................................................................. 59
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4.6 The top performers ..................................................................................................................................... 67
4.7 Simple and multiple correlations between TECO results and contextual variables .......... 69
4.8 The influence of the family’s socio-cultural condition .................................................................. 72
4.9 Other social and family information ..................................................................................................... 73
4.10 Supports for studying and individual merit .................................................................................... 77
4.11 Students’ self-assessment of the competences they have acquired ...................................... 79
4.12 Initial estimates and corrections for contextual diversities ..................................................... 82
4.13 Initial estimates and corrections for the self-selection bias ..................................................... 83
4.14 Externalities of merit ............................................................................................................................... 89
4.15 Overview in 20 points of the main outcomes of the TECO pilot test ..................................... 94
4.16 Overview of the outcomes of the TECO pilot test as regards geographic areas ................ 97
5. TECO Test on the generic competences of graduating students ........... 101
5.1 PT module (open-ended response) ................................................................................................... 102
5.2 Excerpt from the SRQ (closed-response items) module ............................................................ 112
6. Index of Tables ....................................................................................................... 115
7. Other Tables ............................................................................................................ 125
8. Index of Annexes available upon request ..................................................... 237
9. Bibliography ........................................................................................................... 238
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This ANVUR Report has been elaborated, based on the data received up to 31 January 2014, by Fiorella
Kostoris Padoa Schioppa with the assistance of Valentina Testuzza (ANVUR), Marzia Foroni (MIUR),
Massimo Carfagna (CRUI) and Paola Costantini (ANVUR), the extraordinary cooperation of Emanuela
Reale, and the high competence and generous help of Alessio Ancaiani, Alberto Ciolfi and Irene
Mazzotta (ANVUR). ANVUR is grateful to all who have contributed to make this Report so informative.
ANVUR also wishes to thank the financial sponsors, guarantors, working group members, experts,
translators, scorers, all those in charge of local governance, and all participants to the seminars on the
TECO test.
Pre-Feasibility Working Group
Guido Franco Amoretti (Univ. of Genoa); Gabriele Anzellotti (Univ. of Trento); Annamaria Poggi (Univ.
of Turin); Emanuela Reale (CERIS, CNR); Roberto Ricci (INVALSI); Paolo Sestito (Bank of Italy);
Vincenzo Zara (Univ. of Salento-Lecce).
Financial sponsors
INVITALIA; Fondazione S. Paolo; Fondazione Cariplo; Fondazione Caripuglia for the University of
Salento; Region of Friuli Venezia Giulia for the University of Udine.
Guarantors
Alfonso Caramazza (Universities of Harvard and Trento); Jan Levy (AHELO, OCSE); Piero Cipollone
(World Bank, Washington DC); Roberto Ricci (INVALSI).
Experts
Claudio Borri (Univ. of Florence); Alfonso Caramazza (Universities of Harvard and Trento); Alberto
Mantovani (Univ. of Milan); Giorgio Parisi (Univ. of Rome La Sapienza); Annamaria Poggi (Univ. of
Turin); Emanuela Reale (CERIS, CNR); Roberto Ricci (INVALSI); Emanuela Stefani (CRUI); Vincenzo
Zara (Univ. of Salento, Lecce); Doris Zahner (CAE, New York); Barbara Frabboni, Alessandro Lodi,
Maurizio Moreo, Mauro Motta and Francesca Pruneti (CINECA).
Institutional Coordinators in each of the 12 Universities participating in the pilot test
Eliana Baici (Univ. of Eastern Piedmont); Carlotta Berti Ceroni (Univ. of Bologna); Carlo Busacca (Univ.
of Messina); Tiziana Catarci (Univ. of Rome La Sapienza); Giuseppe De Luca (Univ. of Milan); Ettore
Felisatti (Univ. of Padua); Stefano Manetti (Univ. of Florence); Riccardo Martina (Univ. of Naples);
Aurelio Simone (Univ. of Rome Tor Vergata); Maurizio Trifone (Univ. of Cagliari); Fabio Vendruscolo
(Univ. of Udine); Vincenzo Zara (Univ. of Salento, Lecce, replaced by Alessandra Chirco on 6/5/2013).
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Administrative Institutional Coordinators in each of the 12 Universities participating in the
pilot test
Clorinda Capria (Univ. of Messina); Vincenzo De Marco (Univ. of Florence); Elena De Sanctis (Univ. of
Bologna), Emanuela Della Valle (Univ. of Milan); Eusebio Giandomenico and Domenico Genovese (Univ.
of Rome Tor Vergata); Gabriella Gianfrate (Univ. of Salento, Lecce); Giuseppa Locci (Univ. of Cagliari);
Alessandra Missana (Univ. of Udine); Rosalba Natale (Univ. of Rome La Sapienza); Cristina Stocco
(Univ. of Padua); Maurizio Tafuto (Univ. of Naples); Andrea Turolla (Univ. of Eastern Piedmont).
Members of the Coordination Committees in the 12 Universities participating in the pilot test
Carmen Aina (Univ. of Eastern Piedmont); Enrica Amaturo (Univ. of Naples); Giuseppe Pio Anastasi
(Univ. of Messina); Marisa Arcisto (Univ. of Eastern Piedmont); Federica Atzeni (Univ. of Cagliari);
Eliana Baici (Univ. of Eastern Piedmont); Riccardo Banfo (Univ. of Eastern Piedmont); Simonetta
Bartolucci (Univ. of Naples); Achille Basile (Univ. of Naples); Marco Antonio Bazzocchi (Univ. of
Bologna); Bruno Bertaccini (Univ. of Florence); Carlotta Berti Ceroni (Univ. of Bologna); Dimitri Boatta
(Univ. of Rome La Sapienza); Renato Brandimarti (Univ. of Bologna); Pierfrancesco Brunello (Univ. of
Padua); Carlo Busacca (Univ. of Messina); Clorinda Capria (Univ. of Messina); Settimio Carmignani
Caridi (Univ. of Rome Tor Vergata); Tiziana Catarci (Univ. of Rome La Sapienza); Marcantonio Catelani
(Univ. of Florence); Alessandra Chirco (Univ. of Salento, Lecce); Giuseppe Cirino (Univ. of Naples);
Sonia Consalvo (Univ. of Rome Tor Vergata); Marcello Corvo (Univ. of Rome Tor Vergata); Edoardo
Matias Diaz Crescitelli (Univ. of Rome Tor Vergata); Giuseppe De Luca (Univ. of Milan); Vincenzo De
Marco (Univ. of Florence); Lucia De Nitto (Univ. of Salento, Lecce); Elena De Sanctis (Univ. of Bologna);
Arturo De Vivo (Univ. of Naples); Stefano Del Giudice (Univ. of Udine); Maria Vittoria Dell'Anna (Univ.
of Salento, Lecce); Emanuela Dellavalle (Univ. of Milan); Paolo Di Francesco (Univ. of Rome Tor
Vergata); Francesca Dragotto (Univ. of Rome Tor Vergata); Carla Faralli (Univ. of Bologna); Silvia
Fedeli (Univ. of Rome La Sapienza); Ettore Felisatti (Univ. of Padua); Patrizio Gabrielli (Univ. of Rome
Tor Vergata); Domenico Genovese (Univ. of Rome Tor Vergata); Marianna Gensabella (Univ. of
Messina); Eusebio Giandomenico (Univ. of Rome Tor Vergata); Gabriella Gianfrate (Univ. of Salento,
Lecce); Stefano Giordani (Univ. of Rome Tor Vergata); Fiorella Giusberti (Univ. of Bologna); Elisa
Latino (Univ. of Salento, Lecce); Giuseppa Locci (Univ. of Cagliari); Mara Lucisano (Univ. of Milan);
Stefano Manetti (Univ. of Florence); Aldo Manzin (Univ. of Cagliari); Marella Maroder (Univ. of Rome
La Sapienza); Federico Masini (Univ. of Rome La Sapienza); Giorgio Massacci (Univ. of Cagliari); Carla
Massidda (Univ. of Cagliari); Marco Mazzotta (Univ. of Rome Tor Vergata); Moreno Meneghetti (Univ.
of Padua); Danilo Merlo (Univ. of Messina); Angela Maria Mezzasalma (Univ. of Messina); Giuseppe
Micheli (Univ. of Padua); Alessandra Missana (Univ. of Udine); Rosalba Natale (Univ. of Rome La
Sapienza); Giuseppe Novelli (Univ. of Rome Tor Vergata); Anna Nozzoli (Univ. of Florence); Pellegrino
Palumbo (Univ. of Naples); Monica Paolini (Univ. of Milan); Mauro Patrone (Univ. of Eastern
Piedmont); Cecilia Pennetta (Univ. of Salento, Lecce); Franco Peracchi (Univ. of Rome Tor Vergata);
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Elio Pietro Perrone (Univ. of Rome Tor Vergata); Domenico Petrazzuoli (Univ. of Naples); Barbara
Pietrobono (Univ. of Rome Tor Vergata); Luciano Pinotti (Univ. of Milan); Flavio Pressacco (Univ. of
Udine); Marina Quartu (Univ. of Cagliari); Maria Carla Re (Univ. of Bologna); Ludovico Rella (Univ. of
Florence); Egidio Robusto (Univ. of Padua); Eugenia Rossi Di Schio (Univ. of Bologna); Filomena Russo
(Univ. of Rome Tor Vergata); Piero Salatino (Univ. of Naples); Francesco Scerbo (Univ. of Rome Tor
Vergata); Maria Eugenia Schininà (Univ. of Rome La Sapienza); Sabina Simeone (Univ. of Rome Tor
Vergata); Aurelio Simone (Univ. of Rome Tor Vergata); Annamaria Spada (Univ. of Milan); Cristina
Stocco (Univ. of Padua); Maurizio Tafuto (Univ. of Naples); Piero Toma (Univ. of Salento, Lecce);
Maurizio Trifone (Univ. of Cagliari); Andrea Turolla (Univ. of Eastern Piedmont); Maria Antonietta
Vanoni (Univ. of Milan); Gabriella Vanotti (Univ. of Eastern Piedmont); Fabio Vendruscolo (Univ. of
Udine); Iacopo Versari (Univ. of Bologna); Cesare Voci (Univ. of Padua); Vincenzo Zara (Univ. of
Salento, Lecce).
Lead Scorers in each of the 12 Universities participating in the pilot test, monitored by the Lead
of Lead Scorers Roberto Ricci and his INVALSI working group coordinated by Cristina Stringher,
and Doris Zahner (CAE) for closed-response scoring and final superscoring
Eliana Baici (Univ. of Eastern Piedmont); Giuseppe De Luca (Univ. of Milan); Silvia Fedeli (Univ. of
Rome La Sapienza); Roberto Giuntini (Univ. of Cagliari); Fiorella Giusberti (Univ. of Bologna); Franz
Heinrich Kohnke (Univ. of Messina); Stefano Manetti (Univ. of Florence), Riccardo Martina (Univ. of
Naples); Vittorio Rocco (Univ. of Rome Tor Vergata, replaced by Gianluca Cubadda on 29/1/2014);
Egidio Robusto (Univ. of Padua); Carlo Sempi (Univ. of Salento, Lecce); Fabio Vendruscolo (Univ. of
Udine).
Scorers
Antonio Acconcia (Univ. of Naples); Giovanna Adinolfi (Univ. of Milan); Ivan Rossano Adorno (Univ. of
Salento, Lecce); Enzo Vinicio Alliegro (Univ. of Naples); Alessandra Allini (Univ. of Naples); Leonardo
Altieri (Univ. of Bologna); Eliana Baici (Univ. of Eastern Piedmont); Cristian Balducci (Univ. of
Bologna); Luciano Maria Barone (Univ. of Rome La Sapienza); Cecilia Bartuli (Univ. of Rome La
Sapienza); Sergio Beraldo (Univ. of Naples); Nicola Boccella (Univ. of Rome La Sapienza); Vanna Boffo
(Univ. of Florence); Maria Broccardo (Univ. of Rome La Sapienza); Maria Fiorenza Caboni (Univ. of
Bologna); Gilberto Calderoni (Univ. of Rome La Sapienza); Paolo Calvosa (Univ. of Naples); Luigi
Campanella (Univ. of Rome La Sapienza); Daniele Cananzi (Univ. of Rome La Sapienza); Enrica Caporali
(Univ. of Florence); Settimio Carmignani Caridi (Univ. of Rome Tor Vergata); Marcantonio Catelani
(Univ. of Florence); Paola Catenaccio (Univ. of Milan); Marta Cavagnaro (Univ. of Rome La Sapienza);
Cristiana Cianitto (Univ. of Milan); Chiara Cini (Univ. of Rome La Sapienza); Paolo Clavenzani (Univ. of
Bologna); Fabrizio Consorti (Univ. of Rome La Sapienza); Antonio Contestabile (Univ. of Bologna);
Stefano Cordiner (Univ. of Rome Tor Vergata); Alessandro Corsini (Univ. of Rome La Sapienza); Marco
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Costa (Univ. of Bologna); Ilaria Cutica (Univ. of Milan); Antonio D'Alessandro (Univ. of Rome La
Sapienza); Marco De Amici (Univ. of Milan); Giuseppe De Luca (Univ. of Milan); Giovanna Del Gobbo
(Univ. of Florence); Maria Vittoria Dell'Anna (Univ. del Salento, Lecce); Paolo Di Francesco (Univ. of
Rome Tor Vergata); Giancarlo Fabrizi (Univ. of Rome La Sapienza); Giuseppe Familiari (Univ. of Rome
La Sapienza); Silvia Fedeli (Univ. of Rome La Sapienza); Fabio Ferlazzo (Univ. of Rome La Sapienza);
Lea Ferrari (Univ. of Padua); Marzia Freo (Univ. of Bologna); Fabiana Fusco (Univ. of Udine); Elisa
Maria Galliani (Univ. of Padua); Dora Gambardella (Univ. of Naples); Roberta Gemmiti (Univ. of Rome
La Sapienza); Marco Gherghi (Univ. of Naples); Francesca Giofrè (Univ. of Rome La Sapienza); Marco
Giunti (Univ. of Cagliari); Roberto Giuntini (Univ. of Cagliari); Fiorella Giusberti (Univ. of Bologna);
Laura Grassini (Univ. of Florence); Valentina Grion (Univ. of Padua); Franz Heinrich Kohnke (Univ. of
Messina); Sandro Landucci (Univ. of Florence); Agostina Longo (Univ. of Rome La Sapienza); Roberta
Maeran (Univ. of Padua); Marco Maffei (Univ. of Naples); Franco Maggi (Univ. of Milan); Elisa Magnani
(Univ. of Bologna); Stefano Manetti (Univ. of Florence); Gianluigi Mangia (Univ. of Naples); Marina
Marino (Univ. of Naples); Riccardo Martina (Univ. of Naples); Marcella Martinelli (Univ. of Bologna);
Barbara Mazza (Univ. of Rome La Sapienza); Dora Melucci (Univ. of Bologna); Manuela Merli (Univ. of
Rome La Sapienza); Nadia Netti (Univ. of Naples); Laura Nota (Univ. of Padua); Carlo Maria Orlandelli
(Univ. of Bologna); Francesco Paoli (Univ. of Cagliari); Guido Parravicini (Univ. of Milan); Esterina
Pascale (Univ. of Rome La Sapienza); Fulvia Patella (Univ. of Rome Tor Vergata); Mauro Patrone (Univ.
of Eastern Piedmont); Elisabetta Petrucci (Univ. of Rome La Sapienza); Maria Cristina Piccirilli (Univ.
of Florence); Luciano Piergiovanni (Univ. of Milan); Bruna Pieri (Univ. of Bologna); Luciano Pinotti
(Univ. of Milan); Flavio Pressacco (Univ. of Udine); Marina Pugnaletto (Univ. of Rome La Sapienza);
Carla Rampichini (Univ. of Florence); Alberto Reatti (Univ. of Florence); Paolo Ricciardi (Univ. of Rome
La Sapienza); Paola Ricciulli (Univ. of Rome La Sapienza); Francesca Ripari (Univ. of Rome La
Sapienza); Egidio Robusto (Univ. of Padua); Vittorio Rocco (Univ. of Rome Tor Vergata); Stefano
Romegnoli (Univ. of Padua); Maria Novella Romenelli (Univ. of Florence); Silvia Salini (Univ. of Milan);
Vincenzo Scalzo (Univ. of Naples); Carlo Sempi (Univ. del Salento, Lecce); Roberto Serpieri (Univ. of
Naples); Teresa Maria Sgaramella (Univ. of Padua); Luca Stefanutti (Univ. of Padua); Alberto
Tamburini (Univ. of Milan); Luca Tardella (Univ. of Rome La Sapienza); Arjuna Tuzzi (Univ. of Padua);
Gabriella Vanotti (Univ. of Eastern Piedmont); Barbara Vari (Univ. of Milan); Fabio Vendruscolo (Univ.
of Udine); Luigi Ventura (Univ. of Rome La Sapienza); Silvia Vida (Univ. of Bologna); Antonio Villari
(Univ. of Messina).
Translators
Maria Alessandra Scalise (INVALSI) and Andrea Ferrari (CAPSTAN, Brussels).
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Speakers at the information seminars (12 seminars between 29 November 2012 and 18
February 2013)
Seminar in NOVARA (Univ. of Eastern Piedmont) on 29/11/2012
Cesare Emanuel (Univ. of Eastern Piedmont); Carmen Aina (Univ. of Eastern Piedmont); Marisa Arcisto
(Univ. of Eastern Piedmont); Eliana Baici (Univ. of eastern Piedmont); Loris Barberis (Univ. of Eastern
Piedmont); Graziella Berta (Univ. of Eastern Piedmont); Giorgia Casalone (Univ. of Eastern Piedmont);
Alberto Cassone (Univ. of Eastern Piedmont); Umberto Dianzani (Univ. of Eastern Piedmont); Giovanni
Fraquelli (Univ. of Eastern Piedmont); Anna Invernizzi (Associazione Industriali Novara); Lucrezia
Songini (Univ. of Eastern Piedmont); Annamaria Torazzo (Univ. of Eastern Piedmont); Andrea Turolla
(Univ. of Eastern Piedmont); Mario Valletta (Univ. of Eastern Piedmont).
Seminar in LECCE (Univ. of Salento) on 10/12/2012
Vincenzo Zara (Univ. of Salento, Lecce); Domenico Laforgia (Univ. of Salento, Lecce); Gabriella
Gianfrate (Univ. of Salento, Lecce); Carlo Margiotta (Univ. of Salento, Lecce).
Seminar in FLORENCE (Univ. of Florence) on 11/01/2013
Alberto Tesi (Univ. of Florence); Marco Bellandi (Univ. of Florence); Bruno Bertaccini (Univ. of
Florence); Marcantonio Catelani (Univ. of Florence); Mario Curia (Confindustria Florence); Vincenzo
De Marco (Univ. of Florence); Stefano Manetti (Univ. of Florence); Anna Nozzoli (Univ. of Florence);
Giacomo Poggi (Univ. of Florence); Emanuela Stefani (CRUI); Vincenzo Zara (Univ. of Salento, Lecce).
Seminar in UDINE (Univ. of Udine) on 18/01/2013
Cristiana Compagno (Univ. of Udine); Marina Brollo (Univ. of Udine); Paolo Ceccon (Univ. of Udine);
Derna Del Stabile (Interna); Francesco Marangon (Univ. of Udine); Alessandra Missana (Univ. of
Udine); Roberto Molinaro (Regione Friuli Venezia Giulia); Roberto Siagri (Eurotech); Andrea Tabarron
(Univ. of Udine); Alberto Toffolutti (Confindustria Udine); Fabio Vendruscolo (Univ. of Udine).
Seminar in MESSINA (Univ. of Messina) on 21/01/2013
Francesco Tomasello (Univ. of Messina); Daniela Baglieri (Univ. of Messina); Ivo Blandina
(Confindustria Messina); Selena Gasperini (Univ. of Messina); Alessandro Italiano (Univ. of Messina);
Maria Enza La Torre (Univ. of Messina); Anna Murdaca (Univ. of Messina); Agatina Scarcella (Univ. of
Messina).
Seminar in ROME (Univ. of Rome La Sapienza) on 23/01/2013
Luigi Frati (Univ. of Rome La Sapienza); Tiziana Catarci (Univ. of Rome La Sapienza); Silvia Fedeli
(Univ. of Rome La Sapienza); Maurizio Flammini (Federlazio); Pietro Lucisano (Univ. of Rome La
Sapienza); Federico Masini (Univ. of Rome La Sapienza); Rosalba Natale (Univ. of Rome La Sapienza).
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Seminar in ROME (Univ. of Rome Tor Vergata) on 24/01/2013
Tiziano Lauro (Univ. of Rome Tor Vergata); Giuseppe Novelli (Univ. of Rome Tor Vergata); Settimio
Carmignani Caridi (Univ. of Rome Tor Vergata); Francesco De Antoni (Univ. of Rome Tor Vergata);
Paolo Di Francesco (Univ. of Rome Tor Vergata); Domenico Genovese (Univ. of Rome Tor Vergata);
Eusebio Giandomenico (Univ. of Rome Tor Vergata); Stefano Giordani (Univ. of Rome Tor Vergata);
Nathan Levialdi Ghiron (Univ. of Rome Tor Vergata); Gustavo Piga (Univ. of Rome Tor Vergata);
Aurelio Simone (Univ. of Rome Tor Vergata); Nicola Vittorio (Univ. of Rome Tor Vergata).
Seminar in MILAN (Univ. of Milan) on 28/01/2013
Gianluca Vago (Univ. of Milan); Daniela Candia (Univ. of Milan); Giuseppe De Luca (Univ. of Milan);
Emanuela Dellavalle (Univ. of Milan); Stefano Forte (Univ. of Milan); Laura Mengoni (Assolombarda);
Alberto Meomartini (Assolombarda); Anna Soru (Chamber of Commerce of Milan); Matteo Turri (Univ.
of Milan).
Seminar in BOLOGNA (Univ. of Bologna) on 31/01/2013
Ivano Dionigi (Univ. of Bologna); Marco Antonio Bazzocchi (Univ. of Bologna); Carlotta Berti Ceroni
(Univ. of Bologna); Renato Brandimarti (Univ. of Bologna); Carla Faralli (Univ. of Bologna); Gianluca
Fiorentini (Univ. of Bologna); Fiorella Giusberti (Univ. of Bologna); Maria Carla Re (Univ. of Bologna);
Eugenia Rossi Di Schio (Univ. of Bologna).
Seminar in PADUA (Univ. of Padua) on 01/02/2013
Giuseppe Zaccaria (Univ. of Padua); Massimo Castagnaro (ANVUR); Ettore Felisatti (Univ. of Padua);
Luciano Galliani (Univ. of Padua); Paolo Gubitta (Univ. of Padua); Giampaolo Pedron (Confindustria
Veneto); Edgardo Picardi (Univ. of Padua); Stefano Romegnoli (Univ. of Padua); Cesare Voci (Univ. of
Padua).
Seminar in CAGLIARI (Univ. of Cagliari) on 11/02/2013
Giovanni Melis (Univ. of Cagliari); Tommaso Ercoli (Univ. of Cagliari); Sergio Lai (RSSE); Maurizio
Trifone (Univ. of Cagliari); Paolo Gubitta (Univ. of Padua).
Seminar in NAPLES (Univ. of Naples) on 18/02/2013
Massimo Marrelli (Univ. of Naples); Simonetta Bartolucci (Univ. of Naples); Achille Basile (Univ. of
Naples); Giuseppe Cirino (Univ. of Naples); Arturo De Vivo (Univ. of Naples); Paola Izzo (Univ. of
Naples); Riccardo Martina (Univ. of Naples); Domenico Petrazzuolo (Univ. of Naples); Piero Salatino
(Univ. of Naples).
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1. Introduction with a concluding comment
Between 2012 and 2013, the National Agency for the Evaluation of Universities and Research
Institutes (ANVUR) carried out an experimental assessment of the generic learning outcomes shown
by students graduating from Italian Universities, by means of the TECO test. This pilot test was
designed taking as a reference point the OECD feasibility study called AHELO-Assessing Higher
Education Learning Outcomes (website http://www.oecd.org/education/skillsbeyondschool/
testingstudentanduniversit yperformancegloballyoecdsahelo.htm) (AHELO, 2013).
ANVUR decided to undertake this pilot test for several reasons.
- Formal reasons: Legislative Decree no. 19 of 27 January 2012 governing the system of Self-
Assessment and Periodic Assessment and Accreditation in higher education (hereinafter referred
to as AVA) provides for the introduction of a system of initial and periodic accreditation of study
courses and Universities; periodic assessment of the quality, efficiency and outcomes of
Universities’ teaching activities; and enhancement of the mechanisms underpinning the self-
assessment of the quality and effectiveness of Universities’ teaching and research activities.
Within this framework, the TECO pilot test has the purpose of supplementing the assessment
process, via indicators that provide an external evaluation and an instrument of self evaluation on
the quality of learning achieved by students during their studies – in terms of the generic
competences they possess on graduating from University.
- Substantial reasons: the principal stakeholders (employers, Universities, students and their
families, taxpayers, and the General Government) are interested in an ever improving quality of
education in our Universities. The TECO test aims to measure cross-disciplinary competences: the
critical thinking needed to solve a problem or to make a decision, the ability to represent and
communicate a given fact, and the ability to learn new knowledge related to areas not necessarily
connected with the particularities of the scientific discipline being studied. These ‘generic’
competences are crucial to ensure individuals’ flexibility and capability to adapt to personal and
professional changes occurring throughout a lifetime. Moreover, these competences are not
monitored, assessed or certified by Universities precisely because they are not the subject of
specific teaching activities; rather, they are part of that intangible baggage that all teachers should
pass on by teaching their subject.
Almost thirty Universities offered to participate in the TECO pilot test. The following twelve (a pre-
defined limit) were selected: Eastern Piedmont (PO), Padua (PD), Milan (MI), Udine (UD), Bologna
(BO), Florence (FI), Rome La Sapienza (RM1), Rome Tor Vergata (RM2), Naples Federico II (NA),
Salento (LE), Cagliari (CA) and Messina (ME), so as to have adequate regional representation (4 from
the North, 4 from the Centre and 4 from the South plus Islands), to exclude non-multidisciplinary
Universities, and to include Universities with a mix of size characteristics.
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The choice was also guided by the preference for Universities with some previous experience in
producing or administering tests used to assess the learning outcomes of University students, as well
as for those presumed to have superior IT equipment and administrative robustness.
In this Report we will refer to Geographic Areas according to this scheme:
Geographic Area University
NORTH PO + MI + PD + UD
CENTRE BO + FI + RM1 + RM2
SOUTH NA + LE + ME + CA
CENTRE + NORTH PO + MI + PD + UD + BO + FI + RM1 + RM2
CENTRE-NORTH PO + MI + PD + UD + BO + FI
CENTRE-SOUTH RM1 + RM2 + NA + LE + ME + CA
ITA12 PO + MI + PD + UD + BO + FI + RM1 + RM2 + NA + LE + ME + CA
In the design of the TECO pilot test, ANVUR established a series of criteria dictated both by the
awareness that it was an experiment (tight deadlines, limited budget, voluntary student participation)
and by the need to collect as much data as possible (contextual variables) for a more complete
understanding of test results:
1. Using the same test for all University courses, to be evaluated in a uniform way with regard to all
students, because generic competences are by their nature independent of the specific field of
study; they depend on how you study, not on what is being studied.
2. Using a test consisting of a) an open-response part that enables a check of reading ability, the
critical analysis of texts and the ability to make coherent decisions therefrom, as well as writing
effectiveness and technique, and b) a closed-response part, regarded as preferable to expose the
quality of scientific-quantitative reasoning.
3. Identifying eligible students (corresponding to the notion of graduating students), i.e. those
entitled to participate in the test if they are in a defined range of progress and maturity along the
study path.
4. Limiting the objective to assessing acquired generic competences (the actual level of learning)
and not the added value of University education. This implies excluding freshmen from the test
but allows providing significant information to the stakeholders with shorter lead-times. In
principle, a longitudinal analysis (on the same people at the beginning and at the end of University
studies) would be the best choice to determine the added value created by Universities, but this
would require a wait of at least 3-4 years.
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5. Using contextual variables, so as to enable filtering out the part of the individual outcomes of the
TECO that depend on both individual characteristics of the student population – for example of a
personal or family nature – and collective characteristics – for example the rate of growth in the
region of origin or the region where the University is located, which induce a more or less high
propensity to rapid and successful completion of studies. This allows a statistical estimate of the
added value, through the analysis of the residuals of various multiple regressions.
1.1 Pilot test numbers
Regarding the administration of the test, it was known that the people entitled to take the TECO were
just under 20% of all students from the third and fourth years, excluding courses for the health
professions, enrolled in the 12 participating Universities, i.e. a population of 21,872 in academic year
2012-2013. In fact, 14,907 people pre-registered for the test – including numerous extraneous persons
not eligible for the test – and, among those eligible and pre-registered, only about 5,900 students
actually came to sit the test (see paragraph 3.11).
Tables 2.1 and 2.4 (see paragraph 4.1) show that the mean proportion of eligible candidates out of
students from the third and fourth year (regularity index, R) and the mean proportion of those who
came to sit the test out of those eligible (participation index, P) range very broadly across the 25
Disciplinary groups and the 12 participating Universities (the set of which is indicated with “ITA12”).
These data pose an initial problem. It is difficult to determine the self-selection bias and, more
importantly, to adequately correct for it. If it is of a positive type (as will be shown later), it becomes
difficult to make certain assertions on the basis of the data observed in the TECO. For instance, the
University of Bologna had greater apparent success in the TECO than Eastern Piedmont. This empirical
evidence could mean a higher level of learning outcomes for Bologna, or it could be due to the self-
selection of students participating in the test: only 13.91% of the Bologna graduating students came to
sit the test, compared with 63.04% for Eastern Piedmont.
1.2 TECO features
As regards its structure, the TECO test consists of two main modules. In the first “Performance Task”
(PT) module, a fact or an act or a circumstance of realistic nature are presented in a central document,
which identifies a theme, with a set of additional, sometimes incoherent empirical pieces of evidence,
often exhibiting varying degrees of robustness. Students are encouraged to take an active role in
addressing the issue, suggesting a solution or recommending the most appropriate intervention or
deciding between several options presenting desirable and less desirable aspects, on the basis of the
information provided. The TECO test does not require any particular knowledge; there are no right or
wrong answers, only better or worse argued ones, coherent or incoherent ones, solid or weak answers
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on a logical or empirical level, and answers described with greater or lesser efficacy and
appropriateness of language. The PT questions are intended to test three aspects:
a) Analysis and Problem Solving (APS),
b) Writing Effectiveness (WE),
c) Writing Mechanics (WM)
Each of the three areas receives a score from 0 to 6: in theory, the minimum score in the PT module is
therefore 0 while the maximum is 18; in fact, 45 students who received an overall PT score of less than
3 were eliminated from the evaluation, as it is believed that in such circumstances the level of their
engagement with the test is so low that such a case is “observationally equivalent" to a case of non-
participation in the test (see paragraph 3.11).
In the second module, called Selected Response Questions (SRQ), 20 questions are proposed with the
aim to assess a set of competences of different nature, predominantly scientific-quantitative. For these
questions, students must choose the correct answer, discarding three distracter answers, on the basis
of the information given or inferred from the documentation provided. SRQ questions are intended to
test three aspects:
a) Critical Reading (CRE) ability;
b) Critique an Argument (CA) ability;
c) Scientific and Quantitative Reasoning (SQR) ability.
Each question receives a 0 score if the answer is incorrect or missing, a 1 score if the answer is correct:
the minimum score in the SRQ module is therefore 0, while the maximum is 20.
1.3 Results
The first element to be underscored when analysing the results is the fact that the TECO seems to have
“made the grade” as regards feasibility, as shown in Table 3.1 (see section 4.3) and in more detail in
the Item Analysis Report by CAE (2014): the density distribution approximates a normal distribution
with a mean of 1000 and a standard deviation of 200. An examination of the frequency distributions of
the scores for the two modules shows some left asymmetry in the PT component, some in the opposite
direction in the SRQ, and a significant difference between males and females (to the detriment of the
latter), in particular in the most scientific and quantitative SQR part of the SRQ module.
In addition, it should be pointed out (see paragraph 4.4) that in the twin CLA+ test, given to 4,380
graduating students of US colleges, the results are virtually identical to our own, for both mean and
quartiles, illustrating superior writing effectiveness and mechanics in young Italians, as well as greater
ability to argue and in critical reading, but lower scientific-quantitative reasoning quality. There is a
possibility to validly compare graduating students from the USA, from Italy and from various countries
in the world, including some that are very different from one another. This is because the generic
competences measured by the TECO pilot test and by the OECD feasibility study (AHELO, 2013) are all
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assessed with the same CLA-type open-ended response test (see paragraph 4.4). The only aspect that
gives cause for concern in the results of the Italian TECO test, compared with the identical American
CLA+ test, is clearly shown in the lower part of Table 3.8 (see paragraph 4.4). The correlation,
individual by individual, between the scores obtained in the “literary” part of the test (PT) and in the
“scientific-quantitative” part (SRQ and particularly SQR) in Italy is half that in the United States. This is
a first sign of the so-called “two cultures” existing in our country. Regardless of the average level of
competences acquired at the end of University studies by our students, they normally show logic
competences that are much more dissociated between the humanistic and scientific domains versus
what is observed elsewhere in the world.
Looking at the TECO outcomes by Disciplinary groups (see paragraph 4.5 ), while keeping in mind the
observations made above as regards the self-selection bias, the best results in the test are obtained in
cases of selection on entry to the University, either with a national admission test (Medicine), or with
local admission tests utilized for all candidate entrants (Psychology), or where there is individual self-
selection (Mathematics-Physics-Statistics), as evidenced by high grades in the school leaving diploma
of those who decide for this study field, known to be stingy when awarding grades. There are six
groups for which the TECO scores are significantly below the mean of ITA12 and, unfortunately, the
minimum is reached in the Education group. Disciplines of high importance in Italy such as Philosophy,
History, Law, and Literature in the humanities-social sciences fields or Biology and Engineering in the
scientific field seem to exceed the national mean and/or median, but not significantly so.
The analysis of the two cultures continues by examining Table 4.6 (see paragraph 4.5). Observation of
how the two parts of the test went – the open-ended, more literary part, and the close-ended, more
scientific and quantitative part – shows that on average the results correlate well in the Disciplinary
groups, with a correlation index of 0.61. Medicine, Mathematics-Physics-Statistics and Psychology are
on average stronger than the others in both aspects, while Education and Sociology are on average
weaker in both components. However, while both parts of the test are well harmonised for Psychology
students (in the sense that the differences in the two test results are not significant), for those in
Medicine and Mathematics-Physics-Statistics there is a clear and strong difference between them, with
a prevalence for scientific-quantitative logic. Unsurprisingly, the same is true in the Engineering,
Architecture, and Chemistry groups. On the contrary, in the humanities, the Philosophy and History
groups – who perform better in the TECO, surpassing (but barely) the national mean and median –
show a balance on average between the two components PT and SRQ, which instead is not seen in the
Arts and Law groups – for which the performance in the first part is significantly higher than in the
second. Unfortunately, this is the case also for the Disciplinary groups with below average success in
the TECO, starting with the Education group.
Lastly, the analysis of the two cultures is concluded with extreme clarity in Table 4.7 (see paragraph
4.5). In the graph, the dotted interpolation line shows the mean correlation between PT and SRQ
scores described above. For each Disciplinary group, a continuous light grey line shows the correlation
15
at individual level between the two components of the test. As can be seen from the gradients of all
these lines, the individual correlation is very low almost everywhere, as the afore-mentioned
comparison between Italy and the United States suggested.
Tables 4.11, 4.12 and 4.13 (see paragraph 4.5) propose a further analysis of the two cultures
displaying the data broken down by University: it can be seen that, in this case, the mean correlation
between PT and SRQ is very strong (0.93), while that at individual level is still very weak.
We now focus on the contextual variables that most seem to 'influence' results on the TECO. The
results in terms of simple correlations are presented in Table 6.1 (see paragraph 4.7). Those obtained
with multiple correlations (a work by Franco Peracchi) are set out in Table 7.1. The two types of
evidence, when the contextual variables considered match, are basically identical – even if sometimes
the simple correlation appears stronger (or weaker) given the multicollinearity between various
regressors (for example between diploma grades and the professional position of the parents, both of
which influence the TECO) and obviously it weakens (or becomes stronger) under the “all other things
being equal” condition adopted in the estimation through multiple regression.
There is a systematic downwards relationship between the TECO result and the variables age, female
gender (versus male) and residence outside the region of the University's location, as well as an
upwards relationship relative to the variables time since diploma obtained, coming from a “classical
studies” high school (compared to other types of high schools), average of diploma and University
grades, being single (versus married), Italian citizenship and Italian spoken at home (versus non-
Italian citizenship and language). Cases where brothers/sisters are also at the University seem instead
to be observationally equivalent to cases where they are not equally educated, while the size of the
family seems to have a negative effect. Students with more technological equipment perform better on
average, as well as those who go on at least one trip per year outside the region.
The influence of parents appears in the sense that an absent mother (not father) lowers the TECO, all
other things being equal, and having a father employed in a management position (but not a mother)
raises it. The effect of the socio-cultural condition is much stronger in simple correlations (see
paragraph 4.7), because in multiple regressions that condition affects the test results also through
diploma and University grades, as well as in the choice of secondary school. It can be seen, therefore,
that some contextual variables – such as, for example, family status – lose value once others are
controlled. This is specifically because family status helps to predict the type of secondary school
diploma, the diploma grade, the type of course of study chosen and the mean University grade – in
addition to directly predicting the results on the TECO test.
Therefore, in simple correlations, a high professional and cultural status of the parents (see paragraph
4.7) strongly correlates with success in the TECO: when the mother is a manager or a white–collar
employee, has a University degree or high school diploma, regardless of the father's position, results
above the mean and median are observed; and this applies equally to the father. The absence of at
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least one parent is obviously a deprivation condition, and the worst one – much worse than the father
or mother being a manual worker, unemployed or unqualified.
The TECO score drops if the student also works and the various types of support for students do not
compensate for the disadvantages of different kinds affecting those students who usually have
recourse to support. The only type of support that helps raise the TECO result seems to be the “student
collaboration contract” (the only one assigned strictly on merit-based criteria and without
consideration for the condition of poverty).
It is of particular interest to examine the connection (or lack thereof) between the tested students’
perception of whether they have acquired adequate competences in the course of their University
studies and their performance on the TECO, by Disciplinary groups and by Universities. There is no
positive correlation between perception that adequate competences have been acquired and test
results, while in the lower two quartiles there is a significant difference in results on the TECO in
favour of students who gave a negative response to the question on competences acquired at
University (see paragraph 4.11). We thus reach the interesting conclusion that students’ perceptions
that they have acquired competences is indicative of the level of “customer satisfaction” (high, as it
turns out, and particularly so in the Southern Universities) but of nothing else of “objective” character.
Carrying on the analysis of the contextual variables, we note a high correlation between quality of the
TECO results and scientific quality of teachers for the corresponding courses of study, as indicated by
R12 derived from the VQR (see paragraph 4.14). It is hardly surprising, ex post, that the quality of the
results of teaching shows a good match with the quality of the results of research.
Finally, it is likely that the self-selection bias is positive, given that the diploma grades (VMD) and
University grades (VME) of students who came to sit the test are significantly higher than those of
eligible students who did not show up and those of ineligible students (see paragraph 4.13). This holds
true for all quartiles of the distribution (see paragraph 4.13). In addition, Tables 8.10 and 8.16 (see
paragraph 4.13) indicate that the differences between students who came to sit the TECO test and
those who did not, in terms of all the contextual variables that are relevant for simple correlations
with results on the TECO, systematically induce a positive self-selection bias. Such variables include
age, citizenship, off-site condition, gender, marital status, student worker situation, language spoken at
home and other languages known. The question hence arises of how much the results of some
Universities with a low P index would go down if the participation rate were to increase and all eligible
students were to sit the test – not just the self-selected ones that cause the TECO results to rise. As a
consequence, in such case, the average University grade or diploma grade would fall to the level
corresponding to that of all eligible students – lower than the level for just those students who came to
sit the test.
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1.4 Conclusions
The 2012-2013 TECO pilot test carried out by ANVUR at 12 Universities was the first ever attempt to
assess the level of generic competences acquired by University students in Italy. The focus was on
examining reading ability, critical analysis, ability to solve new logical, interpretative or scientific-
quantitative problems, and communication capabilities – as exhibited by graduating students from all
study courses. These competences are at least as important as those that are more closely related to
specific courses of study (subject-specific competences). The University has a duty to put generic
competences at the centre of its educational activities, because they are necessary for greater
adaptability to the job market and to present and future life, and therefore essential to increase
employability and personal empowerment.
The results observed for the pilot test are overall comparable to those observed elsewhere in the
world, as summarized in paragraphs 4.15 and 4.16, but they do point to one specifically Italian
weakness: the dissociation in our students between literary and scientific logic, which must be
recomposed and overcome in a sort of new Renaissance. Such a goal corresponds to a trend seen in the
best practices of North and South America and the Far East, and it is strongly advocated in the
Guidelines of the last Council of the 47 European Ministers for Higher Education, meeting in Bucharest
in 2012.
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2. Reasons and criteria for the TECO pilot test
2.1 Reasons for the pilot test
They are of both a formal and substantial nature.
2.1.1. Formal reasons
Since its creation (DPR 76/2010) ANVUR is tasked with assessing not only the processes and the
inputs of the educational offer, but also “the quality of the results and products of management,
teaching and research activities, including technological transfer from Universities and research
agencies, also at the level of the individual structures of these institutions (Art. 3 c.1 (a))”. More
explicitly, Art. 3, c.2 (a) adds “Assessment concerns, among others ... the efficiency and effectiveness of
educational activities on the basis of international quality standards, also with reference to students’
learning outcomes and students’ successful insertion in the world of work”. In the Guidelines set out
by EHEA (European Higher Education Area) in 2005, 2006 and 2008, learning outcomes are broken
down, according to the level of detail, into knowledge, skill, competence, or (in French) savoir, savoir
faire and savoir être. The Italian ‘translation’ was not only late to arrive (the transposition by the MIUR
only took place in December 2010, and the corresponding text – "The Italian Degrees Framework" – is
dated January 2011), but furthermore, it does not correspond to the wording or the spirit of 2008. It
interprets the older, previous formula of the 5 Dublin Descriptors (the first two of a specialist nature,
the last three of a generic nature). Indeed, in the official Italian version of European Recommendation
2008/C111/01, p. 7 (EU, 2008), ‘qualifications’ is translated with the term ‘titoli’ in e. degrees (which
is closer to ‘certificates’) and the three crucial aspects of competences of a generic nature (“critical
thinking, problem solving with decision making, ability to communicate”) are illustrated as follows:
“The first-cycle level certificates may be awarded to students who have the ability to collect and
interpret data… which are deemed useful to reach independent conclusions, including reflection on
social, scientific or ethical issues; who know how to communicate… to specialists and non-specialists;
who have developed those learning competences that are necessary to undertake further studies with
a high degree of autonomy”. The two descriptors of a specialist nature are, on the other hand, more
correctly identified: “The first-cycle level certificates may be awarded to students who have
demonstrated knowledge and understanding at post-secondary level in a field of study… which,
characterized by the use of advanced textbooks, also includes the knowledge of ground-breaking
topics in that field of study; who are able to apply their knowledge… and who possess the appropriate
competences both to conceive and support arguments and to solve problems in their field of study”.
Subsequently, Law 240/2010 (Art. 5, section 3) and Legislative Decree 19/2012 initiate the process
leading to the integrated Self-Assessment, Periodic Assessment and Accreditation system (AVA) laid
down by ANVUR (2013). The initial accreditation requirements include the obligation to describe,
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within the descriptive form drawn up annually for each study course (“Scheda Unica Annuale del
Corso di Studio”, SUA-CdS), among the course objectives, the expected learning outcomes (both
specialized and generic), defined for homogeneous disciplinary areas according to the principles
initially adopted by the Bergen Conference of European Ministers Responsible for Higher Education
(2005). Even more significantly, ANVUR (2013) included in chapter F.2 on the “Periodic accreditation
of University sites and courses of study” a section F.2.4 on “Additional criteria, indicators and
parameters for the periodic accreditation of sites and courses”, including those (F.2.4.1) concerning
“achieved learning outcomes” (p. 36 of ANVUR, 2013). According to the AVA system, therefore, the
results actually achieved by University students in terms of both specialist and generic competences
must not only be compared with the expected ones, but also certified, because, in the future, they will
be considered for the purposes of periodic accreditation and assessment1.
The intention is thus to introduce a novelty within the Italian higher education system, which, thanks
to this initiative, becomes aligned with the best practices underway in most countries of North and
South America, the Far East and, to a more limited extent, also in Europe. In Europe progress has so far
been slower but the calls to accelerate it by the Conferences of the European Ministers Responsible for
Higher Education have been increasingly strong and frequent in the last five years. The most recent
illustration is the Official Document approved on 26-27 April 2012 in Bucharest by 47 Member
Countries of the European Area, and in particular the strategy document “Mobility for better learning”.
Not by chance, the February 2014 draft update of the ENQA Standards and Guidelines (ESG, 2014 and
2014bis) – a document resulting from a consensus between ENQA, ESU, EUA, EURASHE, EI,
BUSINESSEUROPE and EQAR – finally introduces the concept of “student-centred learning”, stating
that it is necessary that “the assessment of students reflects this approach… This means careful
consideration of the design and delivery of study programmes and the assessment of outcome. The
achieved learning outcomes are analysed in relation to the intended outcomes”. This type of assessment
finally shifts the focus from teaching activities to students’ actual learning outcomes (briefly stated,
from teaching to learning), overcoming the traditional approach based solely on identifying the
procedural requirements of a formal nature and the inputs rather than the outputs. Consequently, the
AVA system combines a form of quality assurance which is attentive to the minimum conditions
needed to efficiently foster an educational offer with an innovative approach centred on teaching
effectiveness, as measured by actual results and achieved learning outcomes 2.
1 On the subject of ‘assessing’ students’ learning outcomes, Ministerial Decree MIUR 47 of 30 January 2013 on self-
assessment, initial and periodic accreditation and periodic assessment merely adds a provision concerning distance learning (in Annex C): “The assessment of students, through progress checks, is in any case also carried out at locations other than the legal site of the University, provided that it takes place in the presence of the student before a committee established in accordance with the applicable legislation”. However, this Ministerial Decree does not (yet) set out precise indicators to assess achieved learning outcomes for the purposes of periodic accreditation and assessment of educational activities, because the pilot test, of which this Report gives an account, ended about 13 months later, in mid-March 2014.
2 See also the Council of the European Union (2014), which steps up its conclusions in the direction of learning outcomes.
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2.1.2. Substantial reasons
There are also substantial reasons, no less important than the formal ones, leading ANVUR to give
more importance to assessing achieved learning outcomes. There is increasing pressure for this shift
in focus from all those who are more interested in the results than in the procedures and inputs of the
University system. There are many such stakeholders:
- Employers (including in the financial sector), who require an educated workforce and
increasingly argue that while it is easy to find a good engineer or a good philosopher – because
Italian Universities offer excellent candidates and certify their specialist competences with grades
– it is difficult to find graduates, from any disciplinary field, who have good cross-disciplinary
competences (capacity for critical analysis, decision-making, communication and others). These
generic skills and competences are essential for businesses and nobody in Italy assesses or
certifies them.
- The Universities, which educate our young people and would be keen to improve their diagnostics
so as to increase the quality of the courses offered, but which must “scrape by” as best they can, as
all Universities, even private ones, are supported by increasingly scarce public funds.
- Students and their families, who want to enhance their human capital as a source of cultural
wealth and personal satisfaction as well as an asset for employment and future employability – in
a job market which is constricted locally but extensive globally, in a perspective (also at the
personal level) of unpredictability and extreme volatility.
- Italian taxpayers and the General Government, their agent in the principal-agent relationship, who
quite rightly want to find out about value for money, efficacy in terms of results of the resources
that they contribute to University education, and hence demand that the autonomy of Universities
go hand in hand with responsibility (or better: accountability) and assessment. This especially in
the midst of a crippling crisis, where the public budget is very tight, the burden on those who pay
taxes weighs very heavy, and youth unemployment (also for graduates) is increasingly intolerable.
Each of these stakeholders is interested in knowing the level of cross-disciplinary competences shown
by our University students at the end of their studies. For some, e.g. employers, that information is
sufficient: it is not relevant for them to know when during the journey, the young man or woman
acquired such competences (whether at kindergarten, at home, at school or during higher education).
But for others – families, Universities, taxpayers – it would be helpful to also know what value added is
gained in the final phase of the journey (in that study course or some other? In that University or in
that site?).
For all these reasons, it is crucial for ANVUR to assess and certify especially the generic competences
acquired by University students: their ability to cope with personal and collective problems in socio-
economic and working contexts not known beforehand, making use of previously acquired knowledge,
skills and competences in novel situations. In more detail, these generic competences include knowing
how to read and discuss a text never seen before, applying critical thinking to it, including in the
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presence of simple charts and graphs or quantitative symbols; the ability to solve new problems, to
make decisions quickly and in risky conditions; the ability to communicate effectively orally and in
writing, because work is increasingly carried out with other people: with colleagues by team work or
with competitors, but also with clients, suppliers and public authorities.
Tests to assess these types of generic competences do not exist in Italy at the level of University
studies, but are regularly used on other sections of the population. They demonstrate Italy's well-
known weak points, as is very clear from the evidence produced by the OECD with the PISA tests on
fifteen-year-olds (PISA 2013) on reading and solving simple quantitative problems, and with the
PIAAC tests on the adult population (ISFOL, 2013), where Italy ranks last among the almost 30
countries tested on the literacy scale and penultimate in numeracy. Such tests are an essential
instrument for evaluating generic competences also in the University setting, as demonstrated by their
adoption in a rapidly increasing number of countries around the world. With AHELO (Assessing
Higher Education Learning Outcomes), the OECD is attempting to use a single identical generic skill
test not only for all fields of knowledge, but also for situations as diverse as are those of Colombia,
Egypt, Finland, Korea, Kuwait, Mexico, Norway, Slovakia and the United States – the participants in a
first feasibility study (AHELO, 2013).
2.2. Criteria for the experiment
The Working Group on Pre-feasibility (hereinafter WGP), appointed by the ANVUR Governing Board
on 29 May 2012 with a mandate to give a quick response to the most urgent strategic issues about the
opportunity of providing a test for Italian Universities designed to assess the generic competences of
their graduating students, concluded its mission3 by providing to the Agency a number of suggested
guidelines concerning the general criteria to pursue. Illustrated with a wealth of detail in the paper
“Testing the generic competences achieved by students graduating from Italian Universities: reasons,
criteria and design choices”, published on the ANVUR website on 10 August 2012 (Kostoris Padoa
Schioppa, 2012), these guidelines underpin Resolution No. 65 of 13 August 2012 of the Board of
Directors of ANVUR (see Annex 1), the go-ahead for starting the experiment.
The general criteria listed by the WGP for the test of generic competences are summarised here again
for convenience. They are fully endorsed by ANVUR – as emerges from the same Protocol of 18
December 2012 (ANVUR, 2012)4 – and only in a few points they are subsequently partly rearranged or
reformulated in terms shown in detail on the next few pages.
The main criteria suggested by the WGP are as follows.
3 The WGP started its work on 5 June 2012 and completed it 7 weeks later, in July 2012. ANVUR is very grateful to all
members of the WGP for the high professional quality and perfect timeliness of its contributions. 4
This concerns the procedures, needed actions and consequences resulting from a pilot test to assess the generic competences of Italian graduating students. It includes, among other things, the offer by CINECA (see Annexes 2 and 3) to administer the questionnaire for Italian graduating students in 12 pilot Universities in 2013, at no cost to ANVUR.
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1. Using the same test for all University courses, to be evaluated in a uniform way with regard to all
students, because ‘horizontal’ (generic) competences – the first but by no means the only ones to
be assessed – are throughout the world independent of the specific study paths followed; they
depend by their nature on how you study, not on what you study.
2. Using a test designed to measure not general culture, but the ability to read and critically analyse
texts that may be either exclusively literary or with some quantitative elements, as well as the
ability to make coherent decisions from this analysis and communicate its content in written form.
To this end, among the tests available at international level, the one considered preferable by the
WGP is the Collegiate Learning Assessment (CLA), (www.collegiatelearningassessment.org)
produced by the Council for Aid to Education (CAE), New York, or a derivative thereof. This test
was initially created to provide American colleges – which educate ‘undergraduates’, equivalent to
our students graduating from the three-year first cycle – with a useful instrument to continuously
improve the quality of learning. It was also used in 9 different countries within the framework of
AHELO. Ideally, as stated later by the Committee of Guarantors and Experts of ANVUR, the Italian
generic competences test (henceforth TECO) should include both an open-response and a closed-
response part because only the first allows to assess writing effectiveness and writing mechanics,
while the second is considered preferable to bring out the quality of scientific-quantitative
reasoning.
3. Identifying the University population eligible for TECO in a perimeter defined not by age-related
requirements (as in the case of the PISA and PIAAC tests), but of requirements related to progress
along the study path (as in AHELO). This leads to defining the notion of “graduating students” (in
a broad sense), corresponding to those University students, excluding the ones enrolled in
courses for the health professions, who have acquired all basic and characterising study credits in
a three-year first-cycle course or at least 120 basic and characterising credits in a single-cycle
master course, as required by their study course. In the experimental phase the test should not be
mandatory (although it would be desirable, according to the WGP, that all graduating students in
each course take it) owing to the short period between announcement of the test and the test
sessions in Universities: the recommended date to determine those who meet the requirements
and are therefore eligible for the test (also called graduating or regular students) is 1 April. In the
first few months of 2013, the third criterion was further refined in three aspects:
a) “graduating students” needs to also include those enrolled in the third year of a three-year
first-cycle course, who have passed all the basic and characterising study credits except those
offered in the second semester of the third year. Students in this category, although in a
perfectly regular situation, may not have already completed all the basic and characterising
credits by 1 April;
b) for both cost reasons and the interest in covering regular students who are not too old,
eligibility is limited to students enrolled in the third and fourth year excluding health
23
professions (and not all those who meet the study credits requirements mentioned above), for
both three-year first-cycle and single-cycle master courses, regardless of the year when they
first enrolled;
c) the target in terms of student participation to TECO is lowered, as the authorities in charge of
each of the Universities taking part in the pilot test are not able to commit to more than 50%
participation out of those eligible, for each course of studies.
4. Limiting the TECO test in the pilot phase to graduating students, excluding freshmen. This is for
both budgetary constraints, and so as to be able to provide significant information to the
stakeholders within short lead-times. A longitudinal analysis on the same people at the beginning
and at the end of University studies would be the best choice to determine the value added
created by Universities, but this would require a wait of at least 3-4 years. A cross-section
performed simultaneously on students entering and exiting University (to avoid having to wait
many years for the results) would immediately double the costs of the pilot test and would
anyway be only the second best way to measure value added. Therefore, the objective of the pilot
test is limited to assessing the level of generic competences acquired by graduating students, plus
getting some approximate estimation of the value added obtained in post-secondary studies by
using contextual variables in multiple regressions.
5. Using contextual variables, so as to enable filtering out the part of the individual outcomes of the
TECO that depend on both personal characteristics of the student population – for example of a
personal or family nature – and collective characteristics – for example the rate of growth in the
region of origin or the region where the University is located, which induce a more or less high
propensity to rapid and successful completion of studies. The purpose of using such variables is to
strive to eliminate those observable factors which, in addition to explaining why some students
possess more competences at the end of the study path, would also explain why they would
appear ‘better’ at its start. This allows getting a first, approximate idea of the value added created
by a particular course of studies in a particular University, as the unexplained residual in a
multiple regression using precisely those explanatory variables as regressors.
2.3 Cost of the pilot test and its coverage
From the foregoing, it is already clear that the criterion of cost-effectiveness has inspired and shaped
all phases of the experiment. To be precise, only €200,000 was set aside in the ANVUR budget to be
spent on TECO in 2013. On the one hand, this has meant a cost-saving orientation for several of
ANVUR’s strategic choices, such as the decisions to limit the experiment to students leaving University
(and not also those entering), and to subject only a subset of students from the third and fourth years
to only a generic competences test (and not also disciplinary or subject-specific tests). On the other
24
hand, it led the Agency to carry out intense fund-raising work. The drive to collect additional funds
was remarkably successful thanks to the extraordinary sensitivity and attention to the problem of our
young people’s competences demonstrated by several banking foundations and other public
institutions, such as the Ministry of Cohesion of the Monti Government (via Invitalia, see Annex 4) and
the wonderful Autonomous Region of Friuli-Venezia Giulia, which are all gratefully acknowledged here.
As a result, the €200,000 appropriation in the ANVUR 2013 budget not only seemed sufficient at the
outset to cover all marginal costs of the TECO experiment over a period of 18 months (closing in
March 2014), but even overabundant, as the additional net expenditure for ANVUR was projected at
less than €50,000 – as shown in Table 1.
25
3. Processes, timeline and phases of the pilot test on the generic competences of Italian graduating students
Upon completion of the above described activities of the WGP in July 2012, ANVUR kicked off the
actual testing phase on August 13, 2012 by setting up a Working Group (hereinafter WG) tasked with
implementing a test of generic competences on Italian graduating students, as well as a Committee of
Guarantors (CG) charged with international selection and adaptation for Italy of CAE’s CLA test or a
derivative thereof (Resolution No. 65 of 13 August 2012, Annex 1). ANVUR is extremely grateful to the
members of these two groups, listed in the preamble of this Report.
To understand the main processes implemented in the pilot test, it is worth noting that it is based on
both central governance at the level of ANVUR and local governance at the level of the Universities.
Both are highly complex. The central governance has a sort of technical secretariat, partly relying on
contributions from external collaborators (institutions5 and individuals6), but mostly on some
excellent researchers within ANVUR7. The WG, the CG, the translators, the financial sponsors, the IT
staff at CAE and CINECA also all cooperate with the central governance – thank you all very much. The
local governance at the level of the Universities participating in the pilot test is just as elaborate. Each
one, set up by its Rector, is co-ordinated by a Professor Institutional Coordinator (ICP) assisted by an
Administrative Institutional Coordinator (ICA), operating with a Coordination Group where there are
in principle representatives of staff and students in all disciplinary ‘Macro-groups’. ANVUR is grateful
to all those, listed in the preamble of this Report, for the high quality work they generously offered.
The phases and the timing of the pilot phase are shown in the annexed timescale (see Table 2), which
is almost identical to the one projected before the start of activities (ANVUR, 2012) except for a
technical initial delay of 2 months compared to the original roadmap. This was due, on the one hand,
to the fact that the Board of Directors of ANVUR agreed on the start of the TECO experiment only in
mid-August 2012, based on the guidelines proposed by the Working Group on Pre-feasibility in July,
and, on the other hand, to the summer break in August, still widespread in Italy and often meaning
people can be absent for an entire month. As shown in the timescale diagram, the phases are
summarised into 15 points, while the times fall into the 18 months of the experiment (from mid-
September 2012 to mid-March 2014). These phases are referred to in paragraphs 3.1-3.15 of this
section 3.
5 As regards institutional partners, a special mention goes to the agreement between ANVUR and CRUI (see Annex 5)
stipulated on 19 February 2013: the Foundation provides the Agency, for the time of the pilot phase, with the technical support needed to foster the conduction of the project; ANVUR, in turn, offers the Foundation the knowledge acquired as a result of the project, with a view to improving the quality of teaching in Universities. 6 Among the external collaborators, ANVUR is particularly grateful to Paola Felli for her extraordinary patience, devotion
and professionalism. 7 Of the internal staff thanks especially to Alessio Ancaiani, Alberto Ciolfi and Irene Mazzotta.
26
3.1 Appointment of the Committee of Guarantors and the Working Group
The Working Group (WG) was tasked with monitoring the entire TECO experiment on Italian
graduating students, while the Committee of Guarantors (CG) had to specifically perform a guarantee
function in the international selection of the test, its adaptation and translation, its validation and
statistical-psychometric analysis.
In particular (Minutes of 19 September 2012, see Annex 6):
1. ANVUR asked the CG to collaborate in selecting an international test of generic competences
and ensuring its transformation into an ‘equivalent’ Italian test through adaptation and
translation (point 1.a of Resolution No. 65)
2. ANVUR asked the WG to collaborate on operational criteria and concrete means and processes
for all activities related to the TECO experiment in Italy, starting with the selection of
Universities for the pilot test (point 1.b of Resolution No. 65). Note that while ANVUR for the
moment promoted only a test of generic and cross-disciplinary competences, the WG and the
CG, in a joint session, agreed on their readiness to also proceed to test subject-specific
27
competences, if an interest in doing so should be expressed by the academics and Universities
involved in the experiment8.
3. ANVUR asked the WG to collaborate on the dissemination of information on the test and the
implications arising from its use to all stakeholders: students, teachers, families, businesses,
the public authorities (point 1.c of the Resolution)9.
3.2 Creation and completion of the National Project Office (NPO), national “control room” of the experiment
The National Project Office located in ANVUR, in addition to benefiting from influential members of the
CG and the WG for the strategic collective functions described above and for others individually
assigned, has also benefited from the contributions of a technical secretariat of junior collaborators,
indicated in the preamble of this Report, of which the most important, already mentioned, only joined
between September and December 2013, once released from other institutional activities at the
Agency. The central governance of the experiment interacted for the entire 18 months’ duration of the
project with the local governances at the Universities, because one or the other often had to take and
implement decisions together, with a view to improving the assessment of the learning outcomes of
students in terms of generic competences, and had to do it in a complementary and timely manner.
This happened not without technical difficulties, and sometimes with some tension, always overcome
by the determination to obtain, together, useful results for the Universities’ self-assessment10 (see
Annex 9) as well as for other stakeholders.
3.3 Selection of the Universities participating to the pilot test
The selection of the Universities participating in the TECO experiment was initiated by a preliminary
invitation sent by the President of ANVUR to the Rectors of all Italian Universities at the end of July
8 In the following months, the CBUI (see Annex 7), representing Biology teachers from all the Universities of the pilot
phase that have a Biology department, was very active in this sense and ANVUR confirmed its willingness to introduce a subject-specific test in the TECO programme as long as the test would be produced jointly by the biologists concerned. However, for the moment this test has not yet materialised. 9 At the meeting on 7 November 2012 (Annex 8), the responsibilities of the members of the WG were further clarified, in
their different tasks: each expert was assigned a leadership role with respect to the different stages of implementation of the project and everyone also supports another expert from the Group, in order to problem share. Of particular importance are the activities carried out by four colleagues, members first of the Working Group on Pre-feasibility (WGP) and then of the WG set up in its wake: Anna Maria Poggi, for the great help in contractual relations; Emanuela Reale, who, with extraordinary expertise and commitment, drafted a first part of this Report; Roberto Ricci for translating the texts and monitoring the scoring performed by INVALSI, as well as for his invaluable contribution as a member of the CG; Vincenzo Zara (until his nomination as Rector) for the highly effective coordination of local coordinators. Without the high professionalism of these colleagues and friends, the success of the TECO venture could hardly have been the same. 10 The form attesting the regularity of the CDL was sent to the Directors on 3 October 2013 with a request to return it
filled out by 3 November 2013.
28
2012.11 The invitation underscored on the one hand the relevance of the results potentially obtainable
from the test for the purposes of improving Universities’ educational offer. On the other hand, it
stressed both the total freedom to choose whether or not to participate in the experiment, and, in the
case of assent, the need to take financial responsibility for costs at local level related to test activities,
as well as the commitment to fulfil various functions of an IT and administrative nature12. At the same
time, the President of ANVUR stated that the Agency would make a selection from the applications
received, pursuing objectives of representation and effectiveness and using transparent methods.
The representativeness and effectiveness criteria for selecting the Universities were identified by the
WG in the autumn months (in the meetings on 19 September, see Annex 6, and 7 November, see Annex
8). The WG, in particular, suggested considering the following elements to guide the selection of
Universities for the pilot test:
1. An adequate composition by regional areas (North-West, North-East, Centre, South and Islands),
possibly by selecting 2 Universities in the North-West, 2 in the North-East, 4 in the Centre, 4 in the
South, of which preferably 2 in the Islands.
2. The exclusion of non-multidisciplinary Universities.
3. The preference, once constraints 1 and 2 have been met, for Universities with some previous
experience in producing or administering tests used to assess learning outcomes 13 of University
students or, in the absence of this preferential factor, for those with presumably superior
information technology equipment and administrative robustness.
4. The inclusion of Universities with a mix of size characteristics.
5. A maximum limit of 12 Universities to be involved in the pilot phase, possibly offering those not
selected the opportunity to nevertheless participate in the experiment in some other way – for
example by autonomously developing and administering, in cooperation with other candidate
Universities, a test to assess subject-specific competences, under the coordination of ANVUR14,
however, bearing in such case the corresponding costs15.
11 Letter of 27 July 2012, protocol 938. In fact, some Universities had already applied during the pre-feasibility phase for
participation in a pilot test of assessing actual learning outcomes (University of Bologna, University of Salento, University Federico II of Naples, University of Padua, University of Rome - La Sapienza, and University of Udine). Other Universities had informally expressed an interest in the initiative, requesting more information (among others, the IULM University of Milan, the IUAV of Venice, and the University of Cassino). 12 The University must possess a data warehouse, i.e. a system that allows rapid and efficient querying and
management of data on students, as well as support offices that can assist with test implementation operations, without adding to the burden of tasks which are already assigned to department secretariats and students.
13 In the OECD AHELO feasibility study (AHELO, 2013), the University of Florence had cooperated in the production of the test used in the Engineering Strand, while the Universities of Eastern Piedmont, Udine, Bologna, Rome - La Sapienza, and Naples had their students take part in the Economics Strand test.
14 Regretfully, some excellent Universities, such as, for example, those of Bari, Camerino, Campobasso, Macerata, Perugia and the University for Foreigners of Siena could not be retained to take part in the ANVUR experiment.
15 In fact what happened was that, for example, in the case of Apulia, ANVUR could not take both the Universities of Bari and Salento (both of which had spontaneously applied) into the experiment group, because they both belong to the same region. When, in the spring of 2013, the Caripuglia Foundation decided to allocate an extra €50,000 so that all four Universities in Apulia (including the Polytechnic of Bari and Foggia) – in addition to Lecce which had been selected – could test their students with no additional cost for them, the other 3 Universities renounced the opportunity. ANVUR is nevertheless grateful to President Castorani of Caripuglia for the generous offer of resources for the TECO experiment, even if it could not be used.
29
Almost thirty Universities submitted an application. The following were selected using criteria 1-5
indicated above: Eastern Piedmont (PO), Padua (PD), Milan (MI), Udine (UD), Bologna (BO),
Florence (FI), Rome La Sapienza (RM1), Rome Tor Vergata (RM2), Naples Federico II (NA),
Salento (LE), Cagliari (CA) and Messina (ME)16 . The University of Camerino offered subsequently
to host the focus group and cognitive lab, a crucial step in the conduction of the project (see
section 3.9) and ANVUR gratefully accepted this offer.
3.4 Local governance: the Professors - Institutional Coordinators (ICP), the Administrative Institutional Coordinators (ICA) and the Lead Scorers (LSC)
In the same letters of 12 October 2012, the President of ANVUR invited each participating University
to establish a Coordinating Committee within the University, tasked with ensuring liaison between
ANVUR and the governing bodies of the University, with reference to TECO activities. A Professor
Institutional Coordinator (ICP), assisted by an Administrative Institutional Coordinator (ICA), chaired
the Committee. These persons serving in local governance, mentioned in the preamble of this Report
and appointed directly by the Rector, played an essential role in the success of the TECO. The ICPs had
their first meeting at ANVUR on 16 November 2012; a week later, the first meeting of the ICAs was
held at the Agency.
Each ICP and ICA had several complex tasks (see also Berti Ceroni, 2013), including
- referring problems and difficulties at the local level to ANVUR;
- reporting locally on matters discussed with the other ICPs and ICAs and the decisions taken
together with ANVUR at national level;
- providing empirical information about the number and characteristics of students eligible for the
test, and, more generally, about the contextual variables relevant to the experiment;
- discussing and agreeing on the regulations applicable to conduction of the test, on matters such as
the percentage of eligible students17 in each course that should sit the test, or the period in which
to administer the test18;
- arranging a very detailed analysis, through a specific format provided by ANVUR (see Annex 9), by
those responsible for each class (and if necessary for each individual course) in any University in
16 See the application acceptance letters to the Rectors from the President of ANVUR on 12 October 2012, protocols
1338 to 1349, and the application rejection letters sent to the Rectors from the President of ANVUR on the same date, protocols 1350 to 1355.
17 For example, a majority of the ICPs argued that it was not possible to arrange for all eligible students to sit the test, an
option that the WG and the CG had, on the other hand, recommended to ANVUR to avoid any form of self-selection bias. It was then agreed to abide by the rule of a minimum of 50% of the eligible students in each course, a target which was in fact mostly not met, except at the Universities of Udine and Eastern Piedmont. 18 Initially, ANVUR wanted the test to be held between June and July 2013, whereas the ICPs and the LSCs asked and
obtained to bring it forward, starting from the second half of May (see paragraph 3.11).
30
which very irregular paths emerge or particularly weak participation in the test by students of the
third and fourth year, in order to launch a thorough self-assessment of the University in this regard.
In a letter dated 15 January 2013, Protocol 92, the President of ANVUR asked the Rectors of the twelve
participating Universities to appoint a Lead Scorer (LSC) to whom to entrust the difficult task of
coordinating the scorers of their University and the final scoring of the open-response part of the test,
entrusted to 110 scorers19 (see also paragraph 3.13). Having regard to the strategic role of the LSCs
for the TECO, ANVUR required that they preferably be professors with great academic authority, so
that they could interact effectively with the various institutional components of the University's
teaching activity. The LSC could possibly be the same person as the ICP, but the idea of two different
teachers taking on these responsibilities was certainly not discouraged. The extraordinary ability
demonstrated by the twelve LSCs in the training of the scorers and in scoring the tests will be further
illustrated: ANVUR is very grateful to them for the great professionalism demonstrated.
3.5 Selection and adaptation of the test
3.5.1. The CAE-ANVUR contract
Based on the Working Group for Pre-feasibility’s recommendation to use CAE’s Collegiate Learning
Assessment (CLA) for the pilot test, ANVUR had already implicitly decided in August 2012 in favour of
this US provider. Subsequently, the Committee of Guarantors agreed on the advisability, moreover
attested to in the international literature, to foresee a test for graduating students with a dual
component – open-response and closed-response – so as to more effectively assess diversified aspects
of the ability to read, write, analyse, argue critically, and solve problems featuring both qualitative and
quantitative elements. In a group with a very high level of expertise and professionalism, the
Norwegian Jan Levi, President of AHELO, put his great experience at the service of ANVUR to explain to
his colleagues from the CG (who unanimously agreed) that it was much preferable to select in an
international context a single test, including both components mentioned above (open-response and
closed-response), unlike what unfortunately happened in AHELO. This decision by the CG, notified to
ANVUR in October 2012, on the one hand strengthened the position of CAE as the only provider in the
world to offer such a test, called CLA+ (because it is derived from the CLA). This is all the more so
because the CLA had already been identified by the Working Group on Pre-feasibility as the best
international product for open-response testing. And, on the other hand, it meant that ANVUR did not
have to prepare a call for tenders, but just enter into a private negotiation with the only existing
monopolist, especially since it was planned to not spend more than €200,000.
Then commenced lengthy negotiations between ANVUR and CAE, both face-to-face and at a distance,
leading to a huge reduction of the costs originally requested by CAE and an equally large shift in
19 The scorers, selected by the Universities that participated in the project, are listed and thanked in the preamble to this
Report. ANVUR had calculated the total number needed and the percentage for each site according to the proportion of eligible students in each University on the total number of eligible students for all Italy. The scorers were thus calculated at a ratio of about of 1/200 eligible students in each University.
31
responsibility for activities necessary for the TECO from the Americans to the Italians. Our colleague
and esteemed law expert from the WG, Anna Maria Poggi, worked hard to ensure that ANVUR signed a
balanced contract with CAE: not too ‘invasive’ by the US company, as it wanted in the beginning, and
with sufficient provisions, wanted by ANVUR, in terms of control over the product and ownership of
the process. CINECA cooperated perfectly in this sense (thanks particularly to Mauro Motta) and
designed a ‘redirect’ computerised process both during test administration and after, which effectively
guaranteed this ownership20. Meanwhile Alfonso Caramazza, Piero Cipollone and Roberto Ricci –
members of the CG and high-level experts in neuroscience or psychometric assessment through
testing, devoted much time and energy to examining several examples of tests produced by CAE, for
both the open-response and multiple choice parts, and discussing at length with CAE on modifications
and adaptations to the test and in the scoring methodology21.
The contract between ANVUR and CAE was finally signed on 15 February 2013 after four months of
negotiations on various fronts (see Annex 10). Unlike the original CLA test, the CLA+ in the Italian
TECO version consists not only of an open-response part but also includes 20 closed-response
questions with 4 response options each (the key and 3 distracters). It lasts 90 minutes in total (60
minutes for the open-response part and 30 minutes for the closed-response questions). The open-
response part is called Performance Task (PT), while the multiple-choice questions are called Selected
20 Notwithstanding, the ANVUR-CAE contract provided for signing a confidentiality agreement (see Annex 11) related to
the use of the tests, which left little margin of freedom for ANVUR. An obligation concerning “confidential information” was established whereby, according to the words of the contract,
“Confidential Information” shall mean any confidential or proprietary information, as determined by CAE, that CAE may disclose to the Consultant, orally or in writing, in connection with Consultant’s employment, including, without limitation, any test results and data obtained therewith, trade secrets, methods, software and associated documentation, business plans, source code, inventions, processes, designs, drawings, engineering or hardware configuration information, know-how, or any other proprietary or business information. By way of example, the methods employed to create the CLA tests are considered trade secrets of CAE and must be treated as Confidential Information.
Ownership of Work Product All work performed by the Consultant for CAE is owned by CAE and is considered Confidential Information. There
shall be no dissemination or publication of any work or information developed during Consultant’s employment without the prior written approval of CAE.
Permitted Use The Consultant shall use Confidential Information only during Consultant’s period of employment and solely for the
purpose of providing services to CAE in accordance with the terms of this Agreement, the policies and procedures of CAE, and any employment agreement that may be in effect. The Consultant shall not use any mentally-retained recollections of Confidential Information to copy, reproduce, summarize, disclose, or make use of the contents or substance of Confidential Information. For example, the Consultant shall not use or replicate, in whole or part, any of the methods used to create the CLA tests. Confidential Information shall, as between the Consultant and CAE, remain the property of CAE”.
Similarly, all those who were able to examine the contents of the CLA+ or the TECO had to sign a Confidentiality Agreement. As it turned out, against every possible expectation, it happened that CAE, and not ANVUR, breached confidentiality for a few hours, without any consequences. Taking into account all the foregoing, ANVUR, with the consent of CAE, is able to publish the entire open-response part of the TECO test and also an excerpt of the closed-response questions, so that Italian stakeholders may better understand the characteristics of the test (see the Annex to this Report).
21 In addition to providing the test, CAE committed also to the following activities as per the terms of the contract with ANVUR. Training the two Leads of the LSCs, Roberto Ricci and Fiorella Kostoris, and, subsequently, the twelve LSCs appointed in each of the participating Universities. Offering a technical support manual for learning the scoring method of the performance test, which the LSCs used in turn to teach the scoring method to the Italian scorers (see Annexes 12, 13, 14 and 15). Writing a brief introductory text to explain to Italian students and teachers the principles and rationale of the CLA+ test, to be used also in the pre-test phase (see Annexes 16 and 17). Scoring the closed-response tests (SRQ) received in anonymous form. Collaborating in the first statistical analysis of test results by drafting a specific Item Report with indicators on the statistical reliability of the answers from the sample of students who took the TECO (see Annex 18).
32
Response Questions (SRQ). Performing well in the test does not require any specific knowledge in any
particular field. Conversely, both in the PT and in the SRQ the student must take account only of the
information contained in the documents included in the test, not from any other external sources –
although everyone must obviously rely on his own so-called “personal encyclopaedia”.
3.5.2. The Performance Task (PT) in TECO
In the PT module of the TECO, a ‘stimulus’, i.e. a fact or an act or circumstance, of a realistic nature, is
presented in a central document, which identifies a theme, with a set of additional, sometimes
inconsistent pieces of empirical evidence, exhibiting varying degrees of robustness (totalling six
documents). Students are encouraged to take an active role in tackling the issue, suggesting a solution
or recommending the most appropriate intervention or deciding between several options presenting
desirable and less desirable aspects, based on the information provided in the “Document Library”.
There are no right or wrong answers, but only better or worse argued ones, coherent or incoherent
ones, solid or weak answers on an empirical level, and answers described with greater or lesser
efficacy and appropriateness of language. The Document Library normally includes several sources of
information, which may comprise technical reports, data tables, graphs, newspaper articles, memos, e-
mails or similar documents.
PT questions are intended to test three aspects:
a) Analysis and Problem Solving ability (APS), demonstrated by students in interpreting, analysing,
and evaluating the quality of the information and data presented to them. They must, among other
things, identify ideas or facts in the documents that are relevant to a problem being discussed,
present related or conflicting information, detect faults in the logic and/or questionable
assumptions, explain where and how it can be determined that the evidence is credible, weak,
unreliable, inconsistent or incomplete, and weigh up information from various sources to make a
decision or reach a logical conclusion, underpinned by a coherent analysis of the information
provided.
b) Writing Effectiveness (WE), shown by the students by communicating their arguments in written
form.
c) Writing Mechanics (WM) used by students, with regard to the basic rules of the language in which
they are expressing themselves.
Each of the three areas receives a score from 0 to 6: the minimum score in the PT module is therefore
0 while the maximum is 18. However, students who receive an overall PT score of less than 3 are
eliminated from the assessment, as it is believed that in such circumstances the level of their
engagement with the test is so low that such a case is “observationally equivalent” to non-participation
in the test (see paragraph 3.14).
33
3.5.3. The 20 multiple choice questions or Selected Response Questions (SRQ) in TECO
The 20 multiple choice questions have the aim to assess a set of competences of different,
predominantly scientific-quantitative nature. For these, students must choose the key (the correct
answer), discarding the three distracters, on the basis of the information given or inferred from the
documentation supplied (this also includes letters, dialogues, tables, photographs, graphs, newspaper
articles or similar).
SRQ questions are intended to test three aspects:
d) Critical Reading ability (CRE) – of a short text, usually accompanied by a graph or other simple
quantitative analysis instrument.
e) Critique an Argument ability (CA) – by selecting, for example, the most convincing position from
several expressed by different people and explaining why.
f) Scientific and Quantitative Reasoning ability (SQR) – in the face of information and pieces of
evidence of both qualitative and quantitative nature.
Each question receives a 0 score if the answer is incorrect or missing, a 1 score if the answer is correct.
Therefore the minimum for the SRQ module is 0 and the maximum is 20.
3.5.4. Adaptation of the CLA+ and its transformation into TECO
It is not enough to translate a test into Italian, it also needs to be localized (‘Italianized’) if we want to
offer our post-secondary students a set of documents consistent with the culture, history and context
of their country, giving them a test which, mutatis mutandis, truly is identical to that sat by their
colleagues at American Universities (or other Universities throughout the world, e.g. those of the nine
countries participating in the Generic Skills Strand of OECD-AHELO). Adaptation was performed by
members of the CG with professionalism, both by examining various open-response and closed-
response test options from those existing at CAE, with a view to choosing the most appropriate ones,
and by asking the American producer to make changes necessary for an Italian environment.
Therefore, for instance, the CG discarded in the field of open-response tests those for which cultural
references were impossible to reproduce in Italian. As an example there was a great PT test where the
‘stimulus’ (the central document of each open-ended question) illustrated the painting The Fall of
Icarus by Bruegel the Elder exhibited in the Museum of Brussels. Of course it was not required that
students have knowledge about Flemish painting or the Greek myth, but the accompanying documents
(always existing in every PT) were taken from English literature, with poetic passages by Auden and
Elliot. These were not only difficult to translate, but deemed impossible to Italianize by the ANVUR
experts, as they were unable to find ‘corresponding’ Italian writers and poets commenting the same
episode of the story of humanity. Again, for instance, in the domain of closed-response tests, those
where the theme seemed prurient (linked to the sexual behaviour of young people or feminist
struggles) were discarded. Likewise, those for which the statistical characteristics of results already
obtained in American colleges did not seem sufficiently robust (for example, because the percentage of
34
correct answers was too small and, of the few who answered well, the individual correlation with the
rest of the correct answers in other parts of the test was too low).
Next, the Italian adaptation consisted of a) reducing the number of accompanying documents in the PT
test, based on the assumption (which regretfully proved to be founded) that Italian youngsters are not
used to fast and accurate reading, and b) limiting the SRQ part to 20 questions instead of 25 as in the
equivalent CLA+ test sat in the USA. Thus the TECO as a whole could be kept down to 90 minutes (with
30 minutes for the SRQ part), to avoid too much concentration time on the test22.
Further, the adaptation of the test to Italy consisted of deleting from the SRQ those elements of the
questions that could appear too mathematical to our students, based on the hypothesis (this also
unfortunately proved correct) of a literary focus mainly still dominant in the culture of Italy. For
example, the Committee of the Guarantors decided that the questions could not refer to more than one
graph or table per topic, and that there should be no references to statistical indicators (such as the t-
statistic), which in Italy are largely unknown in all disciplines other from those closely related to the
‘hard’ sciences. The Committee of the Guarantors decided it was nevertheless unable/unwilling to
change too radically, in transforming the original CLA+ into the TECO, a test that has been validly
tested elsewhere in the world. This is both because it considered that the target audience of Italian
students (and their families, teachers and society) should begin to adapt to international standards,
and because these provide the benchmark of comparison, absolutely essential for us to arrive at
proper evaluations of the results observed in Italy. Therefore, the CG and ANVUR concluded that in
adapting the test, excessive deviations between the CLA+ and the TECO should be avoided.
3.6 Presentation of the experiment to the stakeholders via seminars
The administration of the test on the generic competences of Italian graduating students was preceded
by an intense phase of presentation and discussion of the initiative with the twelve Universities
involved. The participating Universities organised meetings with ANVUR from the end of November
2012 until mid-February 201323. There were many participants and high interest from academic and
administrative staff, students and sometimes potential employers (thanks to the framework that the
twelve Universities gave to the seminars). The presentation of the initiative at local level was a good
opportunity to explain the reasons, criteria and methods of the TECO pilot test, as well as to hear the
views of stakeholders both internal and external to the Universities. The latter were expressed in
22
In the original CLA+, initially used in the United States, the PT module alone generally lasted 90 minutes, with 9 documents. Subsequently, a PT module lasting 60 minutes and with 6 documents, as in the TECO, was also proposed in the USA within the CLA+.
23 The dates of the seminars were: 29 November 2012 at the University of Eastern Piedmont; 10 December 2012 at the University of Salento; 11 January 2013 at the University of Florence; 18 January 2013 at the University of Udine; 21 January 2013 at the University of Messina; 23 January 2013 at the University of Rome La Sapienza; 24 January 2013 at the University of Rome Tor Vergata; 28 January 2013 at the University of Milan; 31 January 2013 at the University of Bologna; 1 February 2012 at the University of Padua; 11 February 2012 at the University of Cagliari; 18 February 2012 at the University of Naples Federico II.
35
numerous interventions, observations, questions, comments, and constructive suggestions – except
marginal episodes which showed some opposition on the part of the students or teachers24. Of
particular importance were the comments made by those who operate in the job market, and are thus
aware of career opportunities for graduates (companies, banking foundations, the General
Government, etc.). In any case, much time was set aside for debate, avoiding compressing it with
lengthy interventions by the speakers. Among the issues most often the subject of questions, were
those on the ‘fallout’ of the test and on the operational procedures with which actual learning
outcomes are assessed. The seminars helped to convey to listeners (also via streaming), together with
more information, a certain level of enthusiasm for the initiative, avoiding falling into the trap of the
pilot test being seen either as additional bureaucratic burden by the administrative staff, or as
dangerous interference by the teaching staff, or as just additional exams by the students. The teachers
were made aware of the strategic significance for the University of the outcomes of the experiment, as
the results obtained will be able to contribute, among other things, also to a significant improvement
of teaching methodologies currently used, following a thorough self-assessment.
3.7 Set-up of the technological platforms and collection of the contextual variables
Carrying out the TECO required the use of two technological platforms: those of CINECA and CAE.
CINECA began working with ANVUR and with the twelve Universities participating in the pilot test, on
the one hand, as well as with CAE, on the other hand, in November 2012, because matters needed to be
agreed on several fronts. In the first place, CINECA and ANVUR needed to agree on a system for the
pre-registration of eligible test candidates, on the form with the questions they would need to fill in,
and on how to collect all contextual variables concerning non-sensitive data about students and
Universities. In cases where students and/or Universities would fail to provide this data to the Agency
in timely and accurate manner, CINECA committed to submit any empirical evidence collected to
ANVUR, in the manner and time required.
CINECA also had to agree with CAE the process for reproducing the test electronically for test takers,
so that, for example, the screens on the PC would be the same as those seen by students tested
elsewhere in the world and that testing times would be equally strictly observed (60 minutes for the
24 As an example of the former, some young people intervened to say they are tired that exams never seem to end for
them (obviously not understanding ANVUR’s intent of “kicking the dog and meaning the master”). A case of real antagonism occurred in one University, when some students said: “We will boycott and will ask our fellow students to boycott the test because we do not believe that you want to assess our critical analysis ability – all you have ever wanted from us is one-track thinking.” As an example of the latter, there was criticism of the inappropriateness of assessing teaching quality externally or doing so via a test, and rather about the opportunity to do so with a disciplinary test, or to do it but to make the individual results available to the Universities in a non-anonymous form. On the first points we refer to Kostoris Padoa Schioppa (2012) for our replies. On the last one, we wish to officially state that ANVUR agrees that, in the future (once rolled-out), the TECO shall be implemented with full disclosure of results through a coordinated but firm discussion with the Privacy Guarantor, which so far has prevented this, perhaps even resorting if necessary to some changes in legislation.
36
PT, 30 for the SQR). CINECA committed to ensuring that the scoring of the PT could be done online
with the same characteristics normally used by CAE, but also taking into account certain ANVUR
requirements (for example, leaving space also for a short comment in addition to the score), allowing
the opportunity to review the judgment after a first formulation, offering the LSC the opportunity to
check the scorers' work, and allowing the Leads of the LSCs to monitor, from INVALSI, each individual
scorer. CINECA also had to agree with CAE on the methods for redirecting a student about to take the
test to their platform, once his/her identity is released to CINECA, made anonymous and ready to pass
on to the American platform. It also had to agree on the return stage from CAE to CINECA, upon
completion of the 90 minutes testing time, of the complete string of information with the answers
given by each tested student. In addition, CINECA had to agree on a series of elements with staff in the
data centres and administrative offices of the twelve participating Universities, in order to help them
to extract information on students eligible for the test together with their contextual variables.
The graduating students who intended to sit the test had to pre-register by completing a CINECA
online form. Sometimes this was combined with another online form from their University (Felisatti,
2013): the first form was an application to sit a given session of the TECO test, while the second form
provided information on the details of the site or times at which the student was admitted to sit the
test. Thus, when pre-registering on the CINECA platform, the student provided his/her basic
information (see Annexes 19 and 20), starting with name and ID. Later, at the time of testing, he/she
would add more information, such as parents’ profession and level of education, perception of the
competences acquired on the course, attendance regularity, as well as a waiver for ANVUR concerning
sensitive data. Table 3 on contextual variables shows the different degree of existing information
about the students enrolled in the third and fourth year in the twelve universities – depending on
whether they came to sit the TECO, they pre-registered but did not show up for the test, they did not
pre-register despite being eligible, or they turned out to be ineligible.
37
BO-PD BO-PD
N NN
ID 5853 3272 12702 45 3394 5524 213 99530 23799
BIRTH DATE S* 5853 3272 12702 45 3394 5524 213 75731 0
BIRTHPLACE (MUNICIPALITY) S* 5853 3272 10568 45 1260 5524 213 75731 0
GENDER S* 5853 3272 12702 45 3394 5524 213 75731 23799
MARITAL STATUS S** 5853 3272 0 45 0 5524 213 0 0
PROVINCE OF RESIDENCE S* 5851 3270 12693 45 3394 5397 213 89538 13807
MUNICIPALITY OF RESIDENCE S* 5853 3272 12702 45 3394 5524 213 75731 0
YEAR DIPLOMA OBTAINED ANS 5840 3259 12641 45 3356 2114 211 89340 13628
SCHOOL LOCATION (PROVINCE) ANS 5601 3044 8898 43 0 2000 202 72667 0
SCHOOL LOCATION (REGION) ANS 5601 3044 8898 43 0 2000 202 72667 0
SCHOOL LOCATION (COUNTRY) ANS 5601 3044 8898 43 0 2000 202 72669 0
UNIVERSITY NAME (CITY) ANS 5853 3272 12702 45 3394 5524 213 99530 23799
COURSE U 5853 3272 12702 45 3394 2123 213 99530 23799
DM
509/99
DM
270/04
DISCIPLINARY GROUP ANV 5853 3272 12702 45 3394 1548 213 99530 23799
MACRO-GROUP MIUR 5853 3272 12702 45 3394 2123 213 99530 23799
GEOGRAPHIC AREA U 5853 3272 12702 45 3394 5524 213 99530 23799
GDP GROWTH RATE IN REGION WHERE UNIVERSITY IS LOCATED ISTAT tot tot tot tot tot tot tot tot tot
YOUTH UNEMPLOYMENT RATE IN REGION WHERE UNIVERSITY IS LOCATED ISTAT tot tot tot tot tot tot tot tot tot
GDP GROWTH RATE IN REGION OF BIRTHPLACE ISTAT tot tot tot tot tot tot tot tot tot
YOUTH UNEMPLOYMENT RATE IN REGION OF BIRTHPLACE ISTAT tot tot tot tot tot tot tot tot tot
NUMBER OF MEMBERS OF HOUSEHOLD S 5743 3205 0 45 0 5136 208 0 0
NUMBER OF SIBLINGS WHO ARE STUDENTS S 5853 3272 0 45 0 5524 213 0 0
LANGUAGE SPOKEN AT HOME S 410 318 0 4 0 931 34 0 0
CITIZENSHIP S* 5853 3272 10569 45 1261 5524 213 75731 0
OFF-SITE (distance place of residence - place of study > 20 km) S 5853 3272 0 45 0 5524 213 0 0
MEAN TRAVEL TIME UNIVERSITY-RESIDENCE S 5853 3272 0 45 0 5524 213 0 0
WORKING STUDENT (as reported by student)**** S 5853 3272 0 45 0 5524 213 0 0
WORKING STUDENT AS PER ANS (information from ANS) ANS 2876 1280 9308 23 0 1351 135 49195 0
OWNS PC S 4715 2583 0 35 0 3497 168 0 0
OWNS TABLET S 434 220 0 3 0 362 18 0 0
OWNS SMARTPHONE S 1630 863 0 7 0 903 61 0 0
ATTITUDE TO TRAVEL S 5838 3272 0 45 0 2382 130 0 0
AVERAGE NUMBER PER YEAR TRIPS OUTSIDE REGION S 5853 3272 0 45 0 5524 213 0 0
AVERAGE NUMBER PER YEAR TRIPS ABROAD S 5853 3272 0 45 0 5524 213 0 0
FATHER’S PROFESSION S 5848 0 0 45 0 0 0 0 0
FATHER’S EMPLOYMENT CONTRACT S 5848 0 0 45 0 0 0 0 0
FATHER’S STUDY QUALIFICATION S 5848 0 0 45 0 0 0 0 0
MOTHER’S PROFESSION S 5848 0 0 45 0 0 0 0 0
MOTHER’S EMPLOYMENT CONTRACT S 5848 0 0 45 0 0 0 0 0
MOTHER’S STUDY QUALIFICATION S 5848 0 0 45 0 0 0 0 0
SCHOLARSHIP S 871 499 0 8 0 331 40 0 0
STUDENT RESIDENCE S 70 36 0 0 0 28 4 0 0
MEAL VOUCHERS S 87 30 0 0 0 31 3 0 0
STUDENT COLLABORATION CONTRACTS S 125 83 0 0 0 24 4 0 0
OTHER S 77 32 0 1 0 19 3 0 0
NATIONAL ADMISSION TEST O 5853 3272 11441 45 2133 1545 212 84022 13061
LOCAL ADMISSION TEST O 5853 3272 11441 45 2133 1545 212 84022 13061
FOREIGN LANGUAGES KNOWN S 5605 3010 0 42 0 3753 213 0 0
NUMBER OF COURSES FOLLOWED IN FOREIGN LANGUAGE, IN ITALY S 5853 3272 0 45 0 5524 213 0 0
NUMBER OF COURSES FOLLOWED IN FOREIGN LANGUAGE, ABROAD S 5853 3272 0 45 0 5524 213 0 0
NUMBER OF MONTHS ERASMUS OR OTHER PROGRAMME S 5853 3272 0 45 0 5524 213 0 0
HIGH SCHOOL DIPLOMA GRADE ANS 5516 3014 12345 43 3353 2042 195 97202 23567
HIGH SCHOOL TYPE ANS 5550 3001 12028 41 3295 1970 197 86893 13240
AVERAGE UNIVERSITY GRADES (EXAMS SAT SO FAR) ANS 5816 3238 12566 44 3386 1634 212 96065 23374
NUMBER OF UNIVERSITY EXAMS SAT SO FAR ANS 5816 3238 11415 44 2125 1634 212 86126 13405
TOTAL CREDITS ACQUIRED ANS 5835 3253 9252 45 0 1722 212 74024 0
QUALITY OF EDUCATIONAL ENVIRONMENT - VQR R12 ANV tot tot tot tot tot tot tot tot tot
SELF-ASSESSMENT OF EDUCATIONAL ENVIRONMENT - EXPECTED COMPETENCES (‘SUA’
FORM) ANV tot tot tot tot tot tot tot tot tot
QUALITY OF STUDENT ENVIRONMENT - M INDEX ANV tot tot tot tot tot tot tot tot tot
SELF-ASSESSMENT OF STUDENT ENVIRONMENT - ATTENDANCE REPORTED AS REGULAR S 5817 8 0 44 0 0 0 0 0
SELF-ASSESSMENT OF STUDENT ENVIRONMENT - STUDENT PERCEIVES COMPETENCES
ACQUIRED IN UNIVERSITY AS ADEQUATE FOR TECOS 5817 8 0 44 0 0 0 0 0
TABLE 3: Contextual variables in the 12 participating universities, for eligible and ineligible students
UNIVERSITY
NPRN NN
PERSONAL
NUMBER OF INELIGIBLE
STUDENTS
PRV PRN N PRVA PRE
Variable Name Source
NUMBER OF ELIGIBLE STUDENTS
5853 3272 12702 45 3394
(**) Although the source is the students, for the categories N and NN the source for the variable MARITAL_STATUS is the National Student Register (ANS) For Bologna and Padua, refer to the note (***).
213 99530 23799CLASS (270 + 509)
(***) For the Universities of Bologna and Padua, the source of the data for the categories N and NN (columns BO-PD_N and BO-PD_NN) are the universities themselves, which provided the data in anonymous form. As can be seen,
these Universities have provided data on fewer student characteristics, particularly as regards ineligible students.
(****) The information can be different from what is found in ANS, since it was provided directly by the student when pre-registering. In this case, by “working student” we mean a student with any type of occupation (even a
precarious one) which is systematic and remunerated.
(*****) The variables under “External merit” exist for different aggregations of “University” variables. See TABLE 3b for more details on the “University” contextual variables
EXTERNAL
MERIT (*****)
Source Acronym
S: Student
U: University
ANS: National Student Register - MIUR (ANS)
(*) Although the source is the students, for the categories N and NN the source for the variables BIRTHDATE, MUNICIPALITY_BIRTHPLACE, GENDER, MARITAL_STATUS, PROVINCE_RESIDENCE, CITIZENSHIP is the University (U) For Bologna
and Padua, refer to the note (***).
INDIVIDUAL
MERIT
SOCIAL
SUPPORTS
FOR STUDYING
FAMILY
ENVIRONMENT
2123
Tested, Pre-registered, Not pre-registered (eligible and ineligible) students
Pre-registered (eligible and ineligible) students
Tested students
Type of existing information per student category:
O: Educational Offer - MIUR (OFF.F)
ANV: ANVUR
M: MIUR
38
There are numerous statistical sources for such information: the MIUR Database on the educational
offer, the MIUR National Students Register, the students themselves, the Universities, and the National
Statistics Institute (ISTAT).
Some vectors were then identified, for homogeneous groups of contextual variables. This includes
‘objective’ individual data, such as those of a demographic nature (e.g. gender, marital status, age, time
since diploma obtained), whose influence on the TECO cannot be imagined ex ante. Likewise ‘objective’
family data (e.g. number of household members), with the same uncertain expected effect on the TECO.
Differently for i) social demographic data (e.g. citizenship, language spoken at home, the condition of
living off-site or being a working student, parents’ social or cultural class, ownership of IT equipment,
attitude to and frequency of travel), ii) individual meritocratic data (diploma grade and type,
University grades, admission test passed at national or local level, knowledge of foreign languages or
courses abroad), and iii) collective meritocratic data concerning externalities related to the study and
academic environment. For these there are instead expected effects on TECO (considering the plentiful
existing studies on the economics of education), which, however, must be empirically checked through
analysis of the pilot test results.
3.8 Translation and conciliation of the texts
The translation of the PT and SRQ modules of the test was conducted in the first instance
independently and excellently by two organisations, that we would now like to thank most sincerely,
INVALSI who did it for free (with Maria Alessandra Scalise) and the Company CAPSTAN of Brussels
(with Andrea Ferrari), specialising in translation/validation of tests materials in the domain of
education. The two translations were then compared and problems of inconsistency or differences
between the two versions were resolved through a conciliation process, carried out by CAPSTAN,
under the supervision of ANVUR and with the approval of CAE. The entire complex operation took
about a month. In addition to the test itself, various other documents required for test administration,
explanation, scoring, etc., were also translated, for example, the Scoring Manual (see Annex 12). This
Report is also translated into English25.
3.9 Focus group and cognitive laboratory at the University of Camerino
The actual administration of the TECO was preceded by a pre-test phase carried out at the beginning
of the spring (5 April 2013) at the University of Camerino. Forty-four students, evenly distributed by
type of study course, attended the focus group. This amounts to 21.78% of the 202 eligible students, a
25 ANVUR is grateful to the two financial sponsors – Caripuglia and the Friuli-Venezia Giulia Region University of
Salento– which, via the University of Salento and Udine, should cover all translation costs.
39
percentage, as will be seen, only a little lower than the mean for the twelve Universities participating
in the pilot test. Forty-two students completed the test, while two withdrew (see Annex 26). The focus
group was used, as always in these circumstances, to detect the presence of printing errors in the test
and assess the appropriateness of the translation from English – for the purpose of getting a good
understanding of any possible residual problems, as well as to fill some of the gaps concerning
background information, sometimes asked to the students themselves – for the purpose of better
identifying the contextual variables that characterise them.
These evaluations were carried out with the “cognitive laboratory” technique, on the basis of a guide
produced by CAE (see Annex 20). Given that how students reason when answering the tests and their
thought processes cannot be directly observed, inferences need to be made on their verbal responses.
The laboratory therefore uses the “think aloud” method: the participating young people are asked to
think aloud while they solve the problem posed by the question and no one, at this stage, interrupts
them with explanatory or corrective interventions (some questions, intended to collect further
elements, are posed at the end of the test session). The cognitive laboratory run by ANVUR had the
special objective of checking that the Italian translation did not alter the original test constructs, that
questions were interpreted by our students with the original meaning they had in English, that they
were not, in general, more difficult to read or understand than if they had been written for Italians
from the outset.
The pre-test showed reading without difficulty by all and the same for Italian students with respect to
what has emerged from similar tests in other countries of the world, even very different ones like the
nine participants in the AHELO feasibility study on generic competences. The pre-test was in any case
quite useful. Thanks to the contribution of one student, it brought to light a couple of errors in the
content of the documents that accompany the stimulus in the open-ended part, and it was
instrumental for suggesting small lexical improvements for easier and more unambiguous
understanding of the texts.
3.10 Validation of the translation after the focus group
The errors and minor translation issues brought to light by the focus group at the University of
Camerino were then corrected and resolved, thus leading to a final, validated translated test.
The TECO ‘package’ also included forms (see Annex 19) to be filled in by students who pre-registered
and then for those who came to sit the test. Students were asked to provide personal information, as
already indicated in Table 3, including: demographic data on the student and on the composition of the
household, family socio-economic status, off-site or working status, any form of support for studying
received, individual data of meritocratic nature (diploma and University grades, admission test passed
at national or local level, their perception of whether they have acquired competences in their course
40
of study, attendance regularity). Also, all pre-registered candidates who came to the test had to sign a
waiver for the purpose of using their data, as required by the Privacy Guarantor.
3.11 Test Administration
The test was administered in the twelve selected Universities over the period from 27 May to 4 July
2013: the dates vary from one University to another, according to choices made by the Universities
themselves (see Annex 21). In any case, continuity in test administration over a defined period was
ensured, so as to ensure homogenous data collection. In some cases, as in the University of Udine, the
test sessions were limited to a 3-day testing window (from 17 to 20 June); in others, such as the mega
Universities of Bologna and Milan, the sessions were extended over a testing window of more than a
month (from 3 June to 4 July).
With the data available on Italian Universities as a whole and on the twelve participating Universities
(Tables 4 and 6), it was known that students eligible to sit the TECO would be just under 20% of all
students enrolled in the third and fourth years of all study courses excluding those for the health
professions, i.e. a population in the academic year 2012-2013 of 21,872 persons. For the above-
mentioned reasons, it was expected that no more than 10-11,000 students would take the test. As it
turned out, 14,907 students pre-registered for the test – a number which includes many ineligible
‘extraneous’ students, who were not admitted to the test – whereas of the eligible and pre-registered
students, only about 5,900 students actually showed up to sit the test (Table 7).
41
42
Each University prepared a participation certificate to be given to students upon completing the test,
drawn up according to an agreed uniform template (see Annex 22). The students were also offered, as
already mentioned, the possibility of obtaining from ANVUR, upon individual request, a certificate
with the test result. About three months down the line, 3/5 of the tested graduating students have
already asked to know their results (see Annexes 23, 24, 25).
All the TECO test sessions were administered online in a supervised environment. In any case, it is not
easy to cheat (as might happen in a test in school) on the PT part of the test, by its very nature.
Likewise for the SRQ part, as the 20 questions were randomly distributed to students, so that the first
question for one student could match his/her neighbour's last one. It was therefore not considered
necessary to apply methods to estimate any “cheating effect” – which does nevertheless exist where
the above precautions are lacking, for example in a number of paper-and-pencil tests organised by
INVALSI.
The summer period was chosen to administer the test, leading to some complaints by the Universities
concerning e.g. the reduced participation of off-site students with respect to pre-registration, owing to
the temporary absence of young people from the city where the University is located (lessons are over
for the academic year and the TECO test session does not coincide with the exams session). However,
on the contrary, other students (presumably those not off-site) justified their non-participation in the
43
test precisely with the opposite reason, i.e. with the excessive overlap of the TECO period with that of
the examinations. Both positions are in fact weak, judging from Table 8. It is observed that off-site
students are often more present among those who came to sit the test versus those who pre-registered
but who did not then show up for the test. On-site students, on the other hand, were more often
frequent among those who did not show up than among those who did come to sit the test, proving
that non-participation is only weakly dependent on being off-site. There are about the same (or
slightly less) off-site students among those tested than among those eligible and pre-registered who
did not come to sit the test26 (except at the University of Rome Tor Vergata, where off-site students
were much more numerous among those who in the end did not sit the test, and except in Bologna and
Padua, where they were instead fewer).
These and other factors potentially influenced the characteristics of the sample of pre-registered
students and of those who actually sat the test with respect to the set of all eligible students, creating a
possible distortion due to self-selection (positive if the worst students self-exclude themselves,
26 It is certainly true, however, that, if the TECO test had taken place on 1 November instead of 1 April 2013, the number
of eligible students would have increased to 16,637, excluding those (who number 12,177 – a sizeable figure) who do not comply with the requirements for basic or characterising credits on 1 April, but who graduated in any case between 1 April and 1 November 2013 (see Table 1.1 shown later).
44
negative if the opposite happens). The available data allow the problem to be assessed with some
approximation, but do not allow the self-selection bias to be eliminated.
3.12 Test administration management by CINECA and CAE, up to the release of individual results
Two technology platforms were used to manage the TECO, CINECA’s and CAE’s. The former was used
to collect and score test answers on the open-response part, as well as to process all the data
characterising (graduating) students in the twelve participating Universities, while the latter was used
to process the multiple choice questions and the related scoring. Once the PT module was scored in
Italy, CINECA sent the scoring data back to CAE after making it anonymous also as regards the
scorers27, but the last transfer from CAE to CINECA took place after the ‘superscoring’ by CAE, which
put together the scoring of the PT module and that of the SRQ module for each graduating student.
As regards privacy regulations, the constraints that emerged made it so that the Universities were not
authorised by the Guarantor to receive information on the (non anonymous) individual TECO
performance of their graduating students. This meant they were precluded from using this type of
parameter for any form of incentive or reward for better-performing students. In view of this, ANVUR
decided, via CINECA, to provide to those who sat the test, upon request, a certificate with their results
for each of the six aspects of the assessment, both in absolute terms and in relation to various
benchmarks. Starting from 11 March, ANVUR deliberated (on 17 February 2014) that, in any case, for
transparency purposes, Universities could access all the anonymous individual data not only of their
own tested graduating students, but also of those in all twelve Universities participating in the pilot
test.
3.13 Training of Lead Scorers (LSCs) and Scorers (SCs), and scoring of the open-response test (PT)
The process of scoring the open-response component of the TECO was a delicate matter, requiring
adequate preparation from a methodological point of view and specific training of the Italian teachers
who would carry out this task. Scoring the 20 closed-response questions (the SRQ component) was
instead carried out directly by CAE, who received the answers in strictly anonymous form: SRQ does
not require any discretional or judgemental assessment, so the scores are assigned by a computer.
Scorer training for the PT component of the TECO took quite some time. For most or even all the
distinguished teachers who underwent this training, it was their first experience of this kind, never
27 In the test administration phase, the transfer from the CINECA platform to the CAE platform took place via an internal
redirecting operation by CINECA, without the testees being interrupted in the completion of the TECO, indeed without them even noticing it. After the test was finished, a reverse transfer from CAE to CINECA for the PT part took place.
45
previously received in their study and work life. Such experience would always be desirable: while
competence and intellectual honesty are a necessary condition for a fair assessment of the student
being observed, they are hardly sufficient to guarantee a neutral rating, in the sense of being
independent of the order in which the test or exams are rated, and also of being replicable by other
members of the same scoring committee.
Two people (Fiorella Kostoris and Roberto Ricci) had two days of online training and two days of face-
to-face training, courtesy of CAE expert Doris Zahner (whom we would like to thank for her great
helpfulness and the contribution of very high scientific value that she offered in this training – and
even more so later, in the statistical analysis of the results, see Annex 18 and Zahner, 2014).
Roberto Ricci, a psychometrician from INVALSI then performed the role of Leader of the twelve LSCs.
The scorer training session, to which only the twelve LSCs from the participating Universities were
invited, was held at ANVUR on 24 and 25 June 2013 and led by Doris Zahner. The LSCs, after going
through this training from the American company, in turn proceeded to train the scorers (SC) at their
respective Universities.
The training for scoring the PT component, carried out in Italy, was accompanied by content analysis
tables in support of the scoring, specifically drafted with the help of a CAE Manual (see Annex 12) and
a further Scorer’s Guide, skilfully written by Fabio Vendruscolo, ICP and LSC of the University of Udine
(Vendruscolo, 2013; see also Annexes 13, 14, and 15), providing detailed instructions for each of the
areas covered by the test (APS, WE, and WM). CAE itself rated this Guide as excellent.
A subset of 20% of the questions scored by each Italian SC was also scored by another SC, and all
scorers were assigned questions to be scored anonymously and randomly, i.e. without knowing who
they were rating or whether it was their own or someone else’s student. For each double-scored
answer, if the scoring by the two SCs involved was not consistent, i.e. if the difference between the
scores assigned by the two scorers was greater than 2 for each of the three areas of assessment,
INVALSI intervened to carefully monitor the situation.
In fact, the quality control process over the scoring, entrusted to INVALSI, was made easier by the fact
that 20% of the answers were scored by two independent scorers selected at random and thus
constituting variable pairs. In this phase, SCs that made rating decisions that had no solid foundation
were immediately identified, because their ratings emerged as outliers with respect to those
formulated by all other scorers. One evidence of the successful scoring operation carried out in Italy on
the TECO is the very small number of outliers among scorers: in all, 3 out of the 110 persons involved
in scoring the test. The ratings carried out by these three scorers of less high quality were
subsequently checked and revised by INVALSI. In this respect, we would like to warmly thank Cristina
Stringher and her monitoring group.
Each SC in each University could do the scoring on his/her computer, online, directly using the CINECA
platform. Each SC could be (and had to be) monitored directly by his/her LSC. The scoring task was
46
completed within 31 August 201328, so that the Lead LSC, Roberto Ricci, with his INVALSI group, could
review the ratings assigned by the SCs as needed. So:
- The final score in the PT module (minimum 0, maximum 18) was obtained by combining the
separate ratings provided by two independent scorers on the three different areas (problem
analysis and solving, writing effectiveness, writing mechanics), after reviewing and validating
these ratings.
- The score in the SRQ module (minimum 0, maximum 20) is simply the number of correct answers
in the 20 closed-response questions.
Lastly, the overall TECO result i.e. scaled score for each student – to be distinguished from the raw
score talked about so far – was calculated as the arithmetic mean of appropriate transformations of
the scores designed to change them (respectively in the intervals 1-6 and 0-20) into homogenous
scales (see Annex 18 and Zahner, 2014).
CAE's ‘superscoring’ work, with the production of the TECO result was completed as expected on 30
September 2013, so that it is now available for each tested student for each of the six components of
the test.
3.14 Data checking and cleaning
Checking and cleaning the data with the TECO result was carried out by ANVUR in collaboration with
CAE (see Annex 18) and with CINECA, also making use of information offered by the twelve
participating Universities. The main effects of this cleaning are described in Table 7. For example, the
test was cancelled for 45 students who sat the test but achieved a PT score of less than 3, a clear sign of
their lack of engagement, so that they are considered comparable to pre-registered eligible students
who did not sit the test. On the other hand, data checking and cleaning revealed that, in very few cases,
Universities allowed some students who did not meet the requirements to sit the test. Considering that
“once it's done, it’s done”, all the more if done by others, ANVUR decided to count these students as
eligible for all effects, as if the Universities could never be mistaken and therefore had possibly been
mistaken previously, when sending in their lists with the identification of requirements for eligible
and ineligible students.
Lastly, data checking and cleaning brought to light the case not only of a couple of hundred students
from the third and fourth year (excluding courses for the health professions), who pre-registered for
the test while not meeting the requirements, but also of 5,524 ‘extraneous’ students (enrolled in years
other than the third and fourth year, or enrolled in courses for the health professions, or ineligible for
other reasons) who pre-registered for the test, obviously in eagerness to put themselves to a test. If
28 The scoring, being double-blind, could not begin until the last University in order of time had finished the test sessions.
In total, it lasted less than 2 months.
47
the number of pre-registered students can be a proxy for the TECO’s ‘approval rating’, we are pleased
to note that this number reached as high as 14,907 students, equal to more than 2/3 of those eligible
for the test – whereas those who actually sat the test were close to 5,900.
3.15 Public presentation of the outcomes of the experiment
During the 18 months of the pilot test phase there were numerous contacts (face-to-face, at a distance,
by letter, etc.) between the various collaborators in the TECO project, Italians and Americans,
computer technicians and translators, persons responsible for central or local governance, etc. – but
the presentation of the results to the external public began only in mid-December 2013. First of all
ANVUR informed and consulted with the then Minister of Education, Maria Chiara Carrozza, receiving
her support and intention to institutionalize, past the experimental phase, the practice of assessing the
generic competences attained by Italian graduating students. Encouraged by this engagement
expressed from the top, which ANVUR trusts will be confirmed also with Minister Stefania Giannini,
new head of the Ministry, the Agency, after debating within the Board of Directors and with the expert
Emanuela Reale about the different outcomes of the pilot test, began to disclose the major aspects in a
first seminar given in English at the University of Bolzano (see Kostoris Padoa Schioppa, 2014) and on
its website. The elements emerging from the TECO pilot test and the first simple correlation and
multiple regression analyses (mostly the work of the econometrician Franco Peracchi, whom we
would like to thank warmly here) were revealed in greater detail in January and February 2014 to the
majority of speakers expected at the Conference on 11 March. Lastly, just prior to this Conference, on
3 March 2014 the ANVUR Advisory Committee received a Report on the principal outcomes from
TECO (see paragraph 4.15).
48
4. Main facts emerging from the experiment
4.1 The regularity index, R, in University studies
The Italian University system has many known problems, as shown also by international comparison:
fewer young people coming out of high school enrol in University, compared to their peers in OECD
countries; too many drop out (and not only in the first few years); too many graduate late with respect
to the normal duration of studies; and after graduating a majority remains for a long time in
temporary employment or unemployed (see also ANVUR, 2014A).
It also suffers from a problem that has so far been little known even to the Universities, as shown by
the excerpt of their self-assessments in Table 9.1: the study career of those who graduate, even of
those who graduate on time, is irregular (according to meaning that this experiment gives to the word).
In the twelve Universities of the experiment only 14 – 19% of the students of the third and fourth year
of a three-year cycle (depending on whether you look at the problem before or after the summer
exams session) complete all of the basic and characterising study credits required by their study
course by the end of it. Only about 18 - 21% of the students of the third and fourth year of the three-
year first-cycle courses and single-cycle master courses are in a regular situation. Therefore, in our
terms, there are few graduating or eligible students, i.e. students who are entitled to sit the TECO test.
As a result, it happens (and it is a mixed blessing) that as much as nearly 2/3 of graduates within the
third year of the three-year course (bureaucratically and officially defined as ‘regular students’)
achieve their University degree without having completed the basic and characterising courses since
at least one semester (see Table 1.1).
49
50
Table 2.1 shows that the percentage of regular students enrolled in the third and fourth year
(regularity index, R) ranges very widely across Disciplinary groups. The best are those who must pass
a national admission test (Medicine, Architecture, Veterinary Medicine and Dentistry) or a local
admission test with 100% of entrants tested (Psychology) or some with a local admission test for a
majority of entrants (e.g. Pharmacy). However, two Disciplinary groups where there is no admission
test in any of the Universities of the pilot are also in an excellent position (Philosophy and Law).
In the breakdown by Universities, the highest regularities are those of Rome La Sapienza and Eastern
Piedmont (see Table 2.4)29.
29
Note that all of the graphs where ITA12 is set as the origin (e.g. those related to Tables 2.1 and 2.4) show the distances from ITA12 of respectively Disciplinary groups and Universities in terms of R and P – i.e. of the regularity index and participation index of eligible students – thus indicating the differences compared to the mean for the sample of the twelve participating Universities.
51
4.2 The TECO participation index, P
Table 2.1 and following are also interesting to examine because they illustrate the participation index,
P, of graduating students who came to sit the TECO, next to the regularity index, R. On average, about
27% of eligible students attended the test voluntarily (just over half of those desired/expected from
the twelve Universities). Hence the percentage (Q) of the students of the third and fourth years
excluding health professions whose test results we know is a mere 5%. Another potentially more
serious problem is related to the participation index shown in Tables 2.1 and 2.4 respectively. This
index varies greatly between Disciplinary groups and between Universities: it is almost twice in
52
respect of ITA12 for Mathematics, Physics and Statistics, while it is 10 points lower than the Italian
mean in the Art Group, followed by Psychology, Dentistry, Medicine and Languages. In general, as
Table 2.3 shows in summary form, the P index is high only in the Scientific Macro-group, while the R
index is high only in the Health Macro-group and, to a much more limited extent, in the Social Sciences
Macro-group. The Humanities Macro-group does not exhibit a good level of regularity, R, nor much
desire to sit the test, P. It is thus in the south-west quadrant, where unfortunately the Universities of
Bologna, Rome Tor Vergata and Naples are also found, whereas the Universities of Udine and Eastern
Piedmont are in the north-east quadrant (63-64% of eligible students sat the test, exceeding by far, at
least on average, the 50% target).
The fact that the participation index, P, is low on average is not of particular concern, because in a
following phase of TECO, when the administration of the test on generic competences will presumably
become a requirement for students who graduate on time, the P index will rise drastically to almost 1.
Furthermore, the main problem caused by not only low but also much differentiated participation
rates will become insignificant. When this happens, as in the TECO pilot phase, it is difficult when first
analysing the data, as we are doing in this Report, to adequately identify and especially correct the
self-selection bias. If this bias is positive, as we will strive to show later, i.e. if the tested students are
probably better than the other eligible students who did not come to sit the test, it becomes difficult to
53
make certain assertions on the basis of the data observed in the TECO – either raw data or data in
which other contextual conditions are filtered out through appropriate multiple regressions. For
instance, the University of Bologna had greater apparent success in the TECO than Eastern Piedmont.
This empirical evidence could mean a higher level of learning outcomes for Bologna, or it could be due
to the self-selection of students participating in the test: only 13.91% of the Bologna graduating
students came to sit the test, compared with 63.04% for Eastern Piedmont. Rather, it would seem
easier to conclude, on the basis of the information on the TECO and on the type of self-selection bias
discussed later on in this Report, that the University of Udine probably performed better than Eastern
Piedmont and than the five Universities with levels of tested generic competences that are higher than
the Italian mean (see Tables 4.11 and 7.1 shown below). Likewise, that the University of Bologna
presumably performed better in the TECO than the University of Naples, all with a virtually identical
low test participation index.
4.3 TECO passes the feasibility test in Italy
Before making such evaluations, it is worth noting that the TECO certainly seems to have “made the
grade” as regards feasibility in Italy. This was the main objective of the pilot phase and it was achieved,
as illustrated with a wealth of detail also in the CAE's Item Analysis Report (see Annex 18).
We only need to look at Tables 3.1 - 3.5 as evidence of this. The density distribution approximates a
normal distribution with a mean of 1000 and a standard deviation of 200. An examination of the
frequency distributions of the scores for the two test modules shows some left asymmetry in the PT
component, some in the opposite direction in the SRQ component, and a significant difference between
males and females (to the detriment of the latter), in particular in the most scientific-quantitative SQR
and CRE aspects, and therefore in SRQ (see Tables 3.6 and 3.7). On its own, this Gaussian-type
distribution function suggests that the TECO passes the feasibility test in Italy.
54
55
56
57
58
4.4 Comparability of TECO results and scores between Italian graduating students and similar student populations in the rest of the world
In addition, it should be pointed out (Table 3.8) that in the twin CLA+ test, given to 4,380 graduating
students of US colleges, the results are virtually identical to our own, for both mean and quartiles
(except in the highest quartile, which is higher in the US), illustrating superior writing effectiveness
and technique in young Italians, as well as greater ability to argue and in critical reading, but lower
scientific-quantitative reasoning quality.
There is a possibility to validly compare graduating students from the USA, from Italy and from
various countries in the world, including some that are very different from one another (Table 3.9).
This is because the generic competences measured by the TECO pilot test and by the OECD feasibility
study (AHELO, 2013) are all assessed with the same CLA-type open-ended response test30.
30
For reminder, the CLA test, unlike the CLA+, comprises only the performance task, PT, and not the SRQ as well, as in the CLA+.
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4.5 The specifically Italian problem of the “two cultures”
The only aspect that gives cause for concern in the results of the Italian TECO test, compared with the
identical American CLA+ test, is clearly shown in the lower part of Table 3.8. The correlation,
individual by individual, between the scores obtained in the “literary” part of the test (PT) and the
“scientific-quantitative” part (SRQ and particularly SQR) in Italy is half that in the United States. This is
a first sign of the so-called “two cultures” existing in our country. Regardless of the average level of
competences acquired at the end of University studies by our students, they normally show logic
competences that are much more dissociated between the humanistic and scientific domains versus
what is observed elsewhere in the world. An esteemed mathematician colleague suggests calling this
the “Croce-Gentile effect” (with uppercase C and G, or perhaps lowercase letter should be used?),
indicating that the problem stems from way back, from Italian history and cultural roots, and it is
certainly not attributable to the faults of our University system.
Our Universities, however, could do more to compensate for the disparity in our students’ logic
competences, nowhere else observed. It would help greatly if Italian Universities were to apply an
enlightened Decree from the Ministry for Education, Universities and Research (Ministerial Decree of
22 October 2004, No. 270), which is 10 years old but has so far largely remained unapplied. The
Decree concerns "course admission requirements", and reads as follows in Art. 6, Par. 1: "To be
admitted to a degree course … the University's educational regulations… require … the possession or the
acquisition of suitable initial preparation. To this end, the same educational regulations define the
knowledge required for admission and determine the assessment procedures, including at the term of
60
preparatory learning activities. … If the assessment shows an unsatisfactory level, specific additional
educational obligations must be met in the first year of the course. These additional educational
obligations are also assigned to students of courses for which there is an admission test, and who were
admitted but with a lower grade than a predetermined minimum grade.”
It is clear that, if this regulation were made operational, the assessment of requisite competences
would be done with some kind of instrument such as the TECO, and the assessment would be followed
up in the first academic year by the obligation to pass any educational debits, for example through
forms of mathematical ‘zeroing’ for those with a more classical training and literary ‘zeroing’ for those
who have instead a more technical-scientific training.
The problem of the two cultures becomes even more noticeable when analysing the TECO outcomes
by Disciplinary group (Table 4.1) or by University (Table 4.11). Without prejudice to the above-
mentioned ‘disclaimer’ as regards the self-selection bias, the best results in the test are obtained in the
Medicine group, followed closely by Mathematics-Physics-Statistics and Psychology, where, however,
the P index for the participation of the second group is 47%, compared to 17-19% for the other two.
The difference versus the national TECO mean is significant for these three groups only31. The entry
selection mechanism is somehow related to this result, with national admission tests (Medicine) or
local admission tests extended to 100% of young people (Psychology), or very high self-selection
(Mathematics-Physics -Statistics), as evidenced by high grades in the school leaving diploma of those
who decide for this study field, known to be stingy when awarding grades (Table 4.1, and Table 6.3.6
shown below).
31
The Political Science Group is also added to the multiple regression shown in Table 7.1 with a more or less weak significance depending of the regressors used.
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62
There are six groups for which the TECO scores are significantly below the mean of ITA12 and,
unfortunately, the minimum is reached in the Education group. Disciplines of high importance in Italy
such as Philosophy, History, Law, and Literature in the humanities-social sciences field or Biology and
Engineering in the scientific field seem to exceed the national mean and/or median, but not
significantly so. Therefore overall (see Table 4.8) only the Health Macro-group is solidly above the
national mean and median, while Humanities unfortunately is solidly below.
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The analysis of the two cultures continues by examining Table 4.6. Observation of how the two parts of
the test went, the open-ended, more literary part, and the close-ended, more scientific and
quantitative part, shows that on average the results correlate well in the Disciplinary groups, with a
correlation index of 0.61. Medicine, Mathematics-Physics-Statistics and Psychology are on average
stronger than the others in both aspects, while Education and Sociology are on average weaker in both
components. However, while both parts of the test are well harmonized for Psychology students (in
the sense that the differences in the two test results are not significant), for those in Medicine and
Mathematics-Physics-Statistics there is a clear and strong difference between them, with a prevalence
for scientific-quantitative logic. Unsurprisingly, the same is true in the Engineering, Architecture, and
Chemistry Groups. On the contrary, in the humanities, the Philosophy and History groups – who
perform better in the TECO, surpassing (but barely) the national mean and median – show a balance
on average between the two components PT and SRQ, which instead is not seen in the Arts and Law
groups – for which the performance in the first part is significantly higher than in the second.
Unfortunately, this is the case also for the Disciplinary groups with below average success in the TECO,
starting with the Education group.
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Lastly, the analysis of the two cultures is concluded with extreme clarity in Table 4.7. In the graph, the
dotted interpolation line shows the mean correlation between PT and SRQ scores described above. For
each Disciplinary group, a continuous light grey line shows the correlation at individual level between
the two components of the test. As can be seen from the gradients of all these lines, the individual
correlation is very low almost everywhere, as the afore-mentioned comparison between Italy and the
United States suggested. It is tempting to state, put simply, that Italian graduating students who
perform well either know how to write or how to count; and those who have acquired few
competences, generally neither know how to write nor how to count – in any case they can do one of
the two either much worse or much better.
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Tables 4.12 and 4.13 propose a further analysis of the two cultures displaying the data for the
Universities. Also in this case the mean correlation between PT and SRQ is very strong (0.93), while
that at individual level is very weak. It should be noted, keeping in mind the already mentioned
caveats, that the University of Cagliari appears to be relatively successful: on average it performs
better than the Centre-South (see Table 4.14) and not significantly below the national ITA12 mean
(see Table 4.11 previously shown). Again put simply, and ignoring the self-selection problem, it could
be said that when it comes to generic competences the South begins in Rome, but does not include
Cagliari.
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4.6 The top performers
From what has been already said about results above the median (those marked with a + sign), it can
be understood that the same segments exhibiting these results include those that would be entitled to
a ‘super bonus’ award, if the rules described in the paper by Fiorella Kostoris Padoa Schioppa (2012)
were followed (see Table 5.5 and following in part 7: Other Tables).
The requirement to be defined top and high performers is even stricter: from Table 5.1 it is clear that
the highest proportion of top performers (students with a test result above the national mean of the
10th decile) is found in Mathematics-Physics-Statistics (males and females with 7.73%) and even only
among females (with 7.09%), while the highest proportion of high performers (students with a test
result higher than the mean for the fourth quartile) is found in Medicine (males and females with
18.32%); the proportion is higher for males in this group (20.74%), whereas female high performers
are more frequent in the Mathematics-Physics-Statistics group, where they even outperform the males.
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M + F F M M + F F M M + F F M
mat.fis.stat (SC)(+)(*) 1041,43 7,73 7,09 8,10 15,46 16,31 14,98 6,70 7,09 6,48med (SAN)(+)(*) 1072,25 7,12 6,83 7,45 18,32 16,10 20,74 3,82 3,90 3,72
sto (H)(+)(*) 1011,02 7,02 0,00 10,81 10,53 5,00 13,51 10,53 10,00 10,81bio (SC)(*) 1006,43 6,64 5,92 8,05 11,33 12,43 9,20 8,98 6,51 13,79
filo (H)(+)(*) 1018,20 4,63 4,23 5,41 7,41 8,45 5,41 7,41 4,23 13,51soc (SOC) 958,04 3,80 4,17 0,00 7,59 8,33 0,00 13,92 15,28 0,00
giu (SOC)(+)(*) 1009,73 3,66 2,78 5,37 9,27 6,60 14,43 9,04 9,03 9,06ing (SC)(*) 1001,20 3,02 3,94 2,68 7,99 8,66 7,74 7,78 10,24 6,85
polit (SOC)(+)(*) 1006,18 2,99 0,88 5,68 9,95 9,73 10,23 10,95 11,50 10,23arch (SC)(*) 1005,94 2,94 2,94 2,94 7,35 7,06 7,84 4,41 4,12 4,90agr.al (SC) 984,01 2,88 3,51 2,44 6,47 3,51 8,54 12,95 7,02 17,07
cult (H) 977,99 2,82 1,82 6,25 3,52 2,73 6,25 8,45 8,18 9,38econ (SOC) 991,15 2,80 2,45 3,18 7,74 5,71 10,00 11,18 14,29 7,73farm (SAN) 975,45 2,79 1,74 5,66 7,61 6,25 11,32 12,69 11,11 16,98lett (H)(*) 1012,51 2,63 1,42 6,12 7,89 7,09 10,20 6,32 7,80 2,04
ling (H) 985,38 2,60 2,65 2,38 6,06 5,82 7,14 8,66 8,99 7,14odon (SAN)(+)(*) 1015,30 2,27 6,67 0,00 11,36 13,33 10,34 11,36 13,33 10,34vet (SAN)(+)(*) 1004,11 2,15 0,00 7,69 10,75 11,94 7,69 9,68 7,46 15,38psic (SOC)(+)(*) 1029,75 2,09 2,52 0,00 8,90 9,43 6,25 2,09 2,52 0,00
chim (SC) 995,45 1,89 0,00 3,28 5,66 2,22 8,20 8,49 13,33 4,92terr (SC) 935,70 1,53 1,97 1,06 4,35 4,93 3,72 20,20 17,73 22,87
comun (SOC) 977,81 1,53 2,53 0,00 6,11 5,06 7,69 12,98 11,39 15,38form (H) 903,28 0,78 0,81 0,00 2,34 2,42 0,00 21,88 22,58 0,00art (H) 965,03 0,00 0,00 0,00 3,13 4,17 0,00 15,63 18,75 6,25
geo (SOC) 933,96 0,00 0,00 0,00 3,77 5,13 0,00 20,75 20,51 21,43
ITA12 999,53 3,55 2,91 4,50 8,85 7,69 10,55 9,81 9,93 9,62
Source: See TAB A-5.1
High performer: student with a test result above the national average of the 4th quartile (> 1196,71)
Low performer: student with a test result below the national average of the 1st quartile (< 793,24)
(**): Percentages calculated on respective total students (M+F, F, M). Upward and downward arrows indicate, respectively, the best and
the worst percentage in the column.
(+): Disciplinary groups with a TECO median higher than the ITA12 TECO median
(*): Disciplinary groups with a TECO mean higher than the ITA12 TECO mean
Top performer: student with a test result above the national average of the 10th decile (> 1270,77)
TAB 5.1: Top, high e low performers per Disciplinary groups, broken down by Gender
Disciplinary groups
(Macro-group)TECO
% Top performers (**) % High performers (**) % Low performers (**)
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4.7 Simple and multiple correlations between TECO results and contextual variables
We now focus on the contextual variables that most seem to ‘influence’ results on the TECO (the
quotation marks are purposeful, as this is not a matter of causation). A summary of results in terms of
simple correlations is presented in Table 6.1. Another one in terms of multiple correlations (a work by
Peracchi, 2014) is set out in Table 7.1. The two types of evidence, where the contextual variables
considered are identical32, are always consistent – even if sometimes the simple correlation appears
stronger (or weaker) given the multicollinearity between various regressors (for example between
diploma grades and the professional position of the parents, both of which influence the TECO) and
obviously it weakens (or becomes stronger) under the “all other things being equal” condition adopted
in the estimation through multiple regressions.
There is, thus, a systematic downwards relationship between the TECO result and the variables age,
female gender (versus male) and residence outside the region of the University's location, as well as an
upwards relationship relative to the variables time since diploma obtained, coming from a “classical
studies” high school (compared to other types of high schools), mean diploma and University grades,
Italian citizenship and Italian spoken at home (versus non-Italian citizenship and language).
From the single or multiple correlation analysis, it can be seen that many of the variables considered
have different effects on different percentiles of the distribution of the scores. The negative coefficient
associated with age is always statistically significant, but tends to become weaker when rising through
the percentiles. The negative coefficient associated with the indicator of female gender tends instead
to grow stronger and to become statistically more significant when rising through the percentiles. On
the contrary, the negative coefficient associated with coming from a technical or professional institute
becomes weaker in the higher percentiles, for which in general, the effect of the type of high school
attended is smaller. The negative coefficient associated with distance from the University site tends to
behave in a very similar manner, i.e. it is quite significant for the lower percentiles, but weakens,
ceasing to be statistically significant, in the higher ones. The fixed negative effects of the Disciplinary
groups Education and Art, and the positive one of Mathematics-Physics-Statistics, are particularly
pronounced in the higher percentiles.
32 One example of variables that do not match, in the two types of analysis, is the case of marital status, examined only
from the indicator of simple correlation: table 6.1 shows that being unmarried always improves the TECO results compared to being married, at least in the North and Centre.
70
NORTH CENTRE SOUTH ITA12
AGE (-) hardly s igni ficant (-) hardly s igni ficant (-) hardly s igni ficant (-) hardly s igni ficant
GENDER not s igni ficant M > F M > F M > F
MARITAL STATUS unmarried > married unmarried > married nd unmarried > married
REGION OF RESIDENCE not s igni ficantres idence in the Region > res idence
outs ide the Regionnot s igni ficant
res idence in the Region > res idence
outs ide the Region
TIME SINCE DIPLOMA OBTAINED (-) hardly s igni ficant (-) hardly s igni ficant (-) hardly s igni ficant (-) hardly s igni ficant
UNIVERSITY NAME (CITY) MI, PD, UD > PO BO, FI > RM2 > RM1 CA > NA, LE, MEMI, PD, UD, BO, FI > PO, RM2, CA > RM1,
NA, LE, ME
DISCIPLINARY GROUP**
arch, bio, farm, giu, mat.fi s .s tat, med,
odon, ps ic, s to, vet > agr.a l , chim,
comun, cul t, econ, fi lo, ing, lett, l ing,
pol i t, terr > form
mat.fi s ., s tat, med, ps ic > arch, ing, giu,
agr.a l , art bio, chim, comun, cul t, econ,
fi lo, lett, pol i t, soc > farm, l ing, terr,
form
med > ing, bio, l ing > giu, econ, pol i t,
farm, terr
mat.fi s .s tat, med, ps ic > arch, art, bio,
chim, comun, cul t, econ, fi lo, giu, ing,
lett, l ing, odon, pol i t, s to, vet > agr.a l ,
farm, form, geo, soc, terr
MACRO-GROUP SAN, SC, SOC > H SAN > SC, SOC > H SAN = SC = SOC = H SAN > SOC, SC > H
NORTH > CENTRE > SOUTH;
CENTRE-NORTH > CENTRE-SOUTH
NUMBER OF MEMBERS OF HOUSEHOLD not s igni ficant not s igni ficant not s igni ficant up to three > more than three
NUMBER OF SIBLINGS WHO ARE
STUDENTSnot s igni ficant not s igni ficant not s igni ficant not s igni ficant
LANGUAGE SPOKEN AT HOME Ita l ian > non-Ita l ian Ita l ian > non-Ita l ian not s igni ficant Ita l ian > non-Ita l ian
CITIZENSHIP Ita l ian > non-Ita l ian Ita l ian > non-Ita l ian Ita l ian > non-Ita l ian Ita l ian > non-Ita l ian
OFF-SITE (distance place of residence -
place of study > 20 km)not s igni ficant not s igni ficant not off-s i te > off-s i te not s igni ficant
MEAN TRAVEL TIME UNIVERSITY-
RESIDENCE (in minutes)up to 15, 16-90 > over 90 not s igni ficant up to 15, over 90 > 16-90 up to 15 > 16-90 > over 90
WORKING STUDENT non working s tudent > working s tudent non working s tudent > working s tudent not s igni ficant non working s tudent > working s tudent
USE OF TECHNOLOGICAL DEVICES two or more > none or one two or more > none or one two or more > none or one two or more > none or one
MEAN NUMBER OF TRIPS OUTSIDE
REGION PER YEARat least one > none at least one > none at least one > none at least one > none
MEAN NUMBER OF TRIPS ABROAD PER
YEARat least one > none at least one > none at least one > none at least one > none
FATHER’S PROFESSION
manageria l/profess ional or white-
col lar worker > labourer or
unemployed, no father
manageria l/profess ional or white-
col lar worker > labourer or
unemployed > no father
manageria l/profess ional or white-
col lar worker > labourer or
unemployed, no father
manageria l/profess ional or white-
col lar worker > labourer or
unemployed > no father
FATHER’S EMPLOYMENT CONTRACTpermanent, sel f-employed> fixed-term,
none
permanent, fixed-term, sel f-employed
> nonenot s igni ficant
permanent > sel f-employed > fixed-
term, none
FATHER’S STUDY QUALIFICATIONdegree or diploma > primary or lower
secondary school , no father
degree or diploma > primary or lower
secondary school > no father
degree or diploma > primary or lower
secondary school , no father
degree or diploma > primary or lower
secondary school > no father
MOTHER’S PROFESSION
manageria l/profess ional or white-
col lar worker > labourer or
unemployed, no mother
manageria l/profess ional or white-
col lar worker > labourer or
unemployed, no mother
manageria l/profess ional or white-
col lar worker > labourer or
unemployed > no mother
manageria l/profess ional or white-
col lar worker > labourer or
unemployed > no mother
MOTHER’S EMPLOYMENT CONTRACT not s igni ficantpermanent > fixed-term, sel f-employed
> none
permanent > fixed-term, sel f-
employed, none
permanent > fixed-term, sel f-employed
> none
MOTHER’S STUDY QUALIFICATIONdegree or diploma > primary or lower
secondary school , no mother
degree or diploma > primary or lower
secondary school , no mother
degree or diploma > primary or lower
secondary school > no mother
degree or diploma > primary or lower
secondary school > no mother
SCHOLARSHIP not s igni ficant non beneficiary > beneficiary not s igni ficant non beneficiary > beneficiary
STUDENT RESIDENCE nd not s igni ficant nd not s igni ficant
MEAL VOUCHERS not s igni ficant nd not s igni ficant non beneficiary > beneficiary
STUDENT COLLABORATION CONTRACTS not s igni ficant under contract > no contract nd under contract > no contract
COURSE WITH ADMISSION TEST
National admiss ion test or loca l
admiss ion test for 100% of s tudents >
no admiss ion test, loca l admiss ion
test
National admiss ion test or loca l
admiss ion test for 100% of s tudents >
no admiss ion test > loca l admiss ion
test
National admiss ion test or loca l
admiss ion test for 100% of s tudents >
no admiss ion test, loca l admiss ion
test
National admiss ion test or loca l
admiss ion test for 100% of s tudents >
no admiss ion test > loca l admiss ion
test but for less than 100% of s tudents
FOREIGN LANGUAGES KNOWN at least one > none at least one > none at least one > none at least one > none
NUMBER OF UNIV. COURSES FOLLOWED
IN FOREIGN LANGUAGE, IN ITALYnone > at least one none > at least one none > at least one none > at least one
NUMBER OF UNIV. COURSES FOLLOWED
IN FOREIGN LANGUAGE, ABROADnot s igni ficant not s igni ficant at least one > none not s igni ficant
NUMBER OF MONTHS ERASMUS OR
OTHER PROGRAMMEnot s igni ficant at least one > none at least one > none at least one > none
HIGH SCHOOL DIPLOMA GRADE (+) medium (+) medium (+) medium-low (+) medium
HIGH SCHOOL TYPE
class ica l or scienti fic lyceum, other
insti tute > other lyceum > technica l or
vocational insti tute
class ica l or scienti fic lyceum > other
insti tute > other lyceum, technica l or
vocational insti tute
class ica l lyceum > scienti fic lyceum,
technica l or vocational insti tute, other
lyceum, other insti tute
class ica l or scienti fic lyceum > other
insti tute > other lyceum, technica l or
vocational insti tute
MEAN GRADE IN UNIVERSITY EXAMS SAT
SO FAR(+) medium-low (+) hardly s igni ficant (+) medium-low (+) hardly s igni ficant
QUALITY OF EDUCATIONAL
ENVIRONMENT - VQR R12(+) medium (+) medium (+) medium-low (+) medium
SELF-ASSESSMENT OF EDUCATIONAL
ENVIRONMENT - EXPECTED
COMPETENCES (‘SUA’ FORM)
QUALITY OF STUDENT ENVIRONMENT - M
INDEX(+) medium (+) medium (+) medium-low (+) medium-low
SELF-ASSESSMENT OF STUDENT
ENVIRONMENT - ATTENDANCE REPORTED
AS REGULAR
not s igni ficant not s igni ficant not s igni ficant not s igni ficant
SELF-ASSESSMENT OF STUDENT
ENVIRONMENT - COMPETENCES
ACQUIRED AT UNIVERSITY PERCEIVED AS
RELEVANT FOR THE TEST BY STUDENT
not s igni ficant not s igni ficant not s igni ficant
competences perceived as not relevant
> relevant, but di fference not
s igni ficant
<: TECO result s igni ficantly di fferent versus ITA12 (95% confidence interva l ) i f sample s ize > 30.
not s igni ficant: no s igni ficant di fference between TECO scores (95% confidence interva l ) i f sample s ize > 30.
nd: Number of s tudents who answered or who are included in the condition of the variable being higher or equal to 30.
(**): The columns NORTH, CENTRE and SOUTH show only those Discipl inary Groups for which more than 30 s tudents participated in the pi lot test. Al l Groups that are not shown had less than 30 s tudents , except
the Artis tic Group, which was not present in the SOUTH.
Source: See TABLE 3
Variables for which the source i s the s tudent are shown in grey background.
SUPPORTS
FOR
STUDYING
INDIVIDUAL
MERIT
EXTERNAL
MERIT
(+): Pos i tive s imple correlation with TECO ca lculated on the means per Discipl inary Group, except for the variables “AGE” and “TIME SINCE DIPLOMA” for which i t i s ca lculated on raw data. Correlation levels are
categorized as fol lows (in ascending order): barely s igni ficant (0 – 0.20); medium-low (0.21 – 0.40); medium (0.41-0.60); medium-high (0.61-0.80); high (0.81-1.00).
(-): Negative s imple correlation with TECO ca lculated on the means per Discipl inary Group, except for the variables “AGE” and “TIME SINCE DIPLOMA” for which i t i s ca lculated on the raw data. Correlation levels
are categorized as fol lows (in ascending order): barely s igni ficant (0 – 0.20); medium-low (0.21 – 0.40); medium (0.41-0.60); medium-high (0.61-0.80); high (0.81-1.00).
>: TECO result s igni ficantly di fferent versus ITA12 (95% confidence interva l ) i f sample s ize > 30
FAMILY DATA
SOCIAL DATA
TABLE 6.1: Contextual variables and TECO results per Geographic Area and ITA12
Contextual variablesCorrelations with TECO
PERSONAL
DATA
UNIVERSITY
DATA
GEOGRAPHIC AREA
71
72
4.8 The influence of the family’s socio-cultural condition
The influence of parents appears in the sense that an absent mother (not father) lowers the TECO, all
other things being equal, and having a father employed in a managerial/professional position (but not
a mother) raises it. The effect of the socio-cultural condition is much stronger in simple correlations,
because in multiple regressions it is also exercised through diploma and University grades, as well as
in the choice of secondary school. It can be seen, therefore, that some contextual variables – such as,
for example, family status – lose value once others are controlled. This is specifically because family
status helps to predict the type of secondary school diploma, the diploma grade, the type of course of
study chosen and the average University grade, in addition to predicting the result on the TECO test.
As shown in Table 7.1, the sign of the coefficients on these variables is as expected, but their statistical
significance disappears both individually (except for the cases cited above), and jointly (the F tests for
the inclusion of the group of variables related to parents’ study qualification or profession do not
refute the null hypothesis of their absence of additional explanatory capacity).
On the other hand, in simple correlations, a high professional and cultural status of the parents (see
Table 6.1.12) strongly correlates with success in the TECO: when the mother has a
managerial/professional position or a white-collar job, has a University degree or high school diploma,
regardless of the father's position, results above the mean and the median are observed; and this
applies equally to the father. The absence of at least one parent is obviously a deprivation condition,
and the worst one, much worse than having a father or mother who is a manual labourer, unemployed
or unqualified.
73
4.9 Other social and family information
The decisive ‘superiority’ of simple correlations with respect to multiple regressions is seen only
where the latter are missing for some of the contextual variables, perhaps because these regressions
are too parsimonious or because for some of the data the number of observations is considerably
lower (as shown in Table 3), so that more sophisticated analysis is not recommended. The
examination of simple correlations between all contextual variables and the result on the TECO or on
its two components, complemented at times by looking at indirect correlation (e.g. with diploma and
University grades), yields some broad generalizations, not necessarily applicable to all the
geographical macro-areas of the country (see Table 6.1 already shown). Looking at the variables for
family data, it is somehow surprising that the cases where there are siblings at the University or not
are observationally equivalent (see Table 6.1.2) and likewise for living off-site with respect to the
University or not (see Table 6.1.5). The size of the family seems instead to have a negative effect (see
Table 6.1.1), and likewise for the travel time required to reach University (see Table 6.1.8).
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75
76
Students with more technological equipment on average perform better (see Table 6.1.9), as well as
those who go on at least one trip per year outside the region (see Table 6.1.10) or abroad (see Table
6.1.11); this does not seem to influence the mean diploma grade, but it does influence the mean grade
on University exams sat so far.
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4.10 Supports for studying and individual merit
Not surprisingly, the TECO score drops if the student also works (see Table 6.1.15) and the various
types of support for studying do not compensate for the disadvantages of different kinds affecting
those students who usually have recourse to support (see Tables 6.1.16 - 6.1.18). The only type of
support that helps raise the TECO result seems to be the “student collaboration contract” (see Table
6.1.19). This is the only one assigned strictly on merit-based criteria (without consideration for the
condition of poverty) but unfortunately it also concerns few students.
78
All the meritocratic-type contextual variables tend, in general, to be significant on the TECO result and
scores. There is instead no significant difference between tested students who passed a University
admission test versus those who did not (see Table 6.2.1 and following). However, this only depends
on the fact that this distinction as such is not fully meaningful For example, among the best in the
absolute sense in the TECO are Medicine (with a National Admission test) and Psychology students
(among whom those who sat an admission test at the time of entry represent 100% of the population).
This prevents a comparison with others who accessed the same courses without an admission test.
Vice versa, in the case of Mathematics-Physics-Statistics, among those tested only 26 sat an admission
test, a number so insignificant as to make the comparison with graduating students who did not sit an
admission test of little interest.
Ultimately, the results in the TECO seem to be, on average, better for those who, among the graduating
students, passed a national admission test or a local one for 100% of entering students. In second
place, the TECO performance seems higher for those, among third and fourth year students, who
belong to disciplines with no admission test (e.g. History, Philosophy and Law). Those who came last
are the students enrolled in ‘hybrid’ disciplines, with or without a local admission test, where there are
‘normal cases’, such as Chemistry (for which the graduating students who had to pass an admission
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test were better than those who did not) as well as ‘paradoxical cases’, such as Engineering (for which
the graduating students who had to pass an admission test performed on average worse than those
who did not).
4.11 Students’ self-assessment of the competences they have acquired
Of particular interest is the examination of the connection or lack thereof between the level of generic
competences acquired during University studies (as perceived by the tested graduating students) and
the level of performance on TECO. In fact, there is no positive correlation between students’
perception that they have acquired competences and their results (see Table 9.5 and following).
The best students are the ‘Socratic’ ones, who know that they do not know more than the average
Italian and in reality obtained the highest scores (in the North-West quadrant of the graph on the left),
for example the eligible students in Medicine and Psychology, while the more ‘arrogant’ are those who
think they know and are right, such as students of Mathematics-Physics-Statistics (in the North-East
quadrant). The most incoherently satisfied with their course of study are the ‘pre-Socratic’ students
(e.g. those in Sociology) who do not know that they do not know (in the South-East quadrant): they
claim to have the competences whereas in reality they perform poorly. Conversely there are ‘modest’
students who coherently know that they do not know and in reality do not know, such as those in
Education. It would thus seem legitimate to conclude that students’ perception that they have acquired
competences (quite high in Italy, expressed by a sizeable 80.45% of the tested graduating students) is
indicative only of high ‘customer satisfaction’ (particularly in the southern Universities) but of nothing
else of ‘objective’ character (see Table 9.5).
80
81
It only rarely happens, and only for those in the highest quartiles at ITA12 (see Table 9.9), that there is
coherence with a low statistical significance between self-assessment of competences and
measurement of these competences via TECO.
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4.12 Initial estimates and corrections for contextual diversities
It is relevant at this stage, on unpolished data such as these, to consider two different questions. Firstly,
whether the better performing Universities would maintain their lead after correcting the results on
the TECO for various differences in the characteristics of students as from their entry to the University.
Secondly, and more importantly, what would happen if the current (positive) self-selection bias were
corrected – for example, the participation rate of Bologna, the smallest one registered, is 50
percentage points away from that of Udine. What would the results have been, in terms of assessing
the generic competences of graduating students, if only 13.9% of those eligible in Udine had turned up
or if the test had been administered to 64.1% of those eligible in Bologna?
The answer to the first question lies in the econometric analysis by Peracchi (2014) (see Table 7.1
mentioned above). Once the condition of all other things being equal is met via multiple regression
(except for the self-selection bias evidenced by substantial heterogeneity in the P index), the fixed
effect is maximum for the University of Udine and solidly positive for the Universities of Bologna,
Milan, Padua, Florence, with the addition of Eastern Piedmont, which like Udine registered the highest
participation in the test. The examination of the fixed ‘University’ effects confirms a clear distinction,
which is already evident in simple correlations, between on the one hand the Universities of Central-
Northern Italy, excluding those in the Rome area (Bologna, Florence, Milan, Padua, Eastern Piedmont
and Udine), and on the other hand the Universities of Central-Southern Italy (Naples, Lecce, Messina,
83
Cagliari, Rome La Sapienza and Rome Tor Vergata)33. For the first group, the fixed effects are always
positive and statistically significant, while for the second one they are negative and statistically
significant in the case of Naples, Lecce and Messina.
4.13 Initial estimates and corrections for the self-selection bias
Turning to the second question, to give an idea of what would happen for example in the comparison
between Universities or between Disciplinary groups if the self-selection bias were removed, we first
demonstrate that this bias is positive. Indeed, all variables positively correlated with the TECO result
are higher among eligible students who sat the test versus those who did not – and vice versa for the
negatively correlated variables, which are lower among eligible students who sat the test. Therefore, if
the self-selection bias were corrected the comparative advantage would increase for those realities
where the participation index, P, in the test is higher. For example, the analysis depicted in Table 8.1
indisputably shows that the diploma and University grades of students who sat the test are
significantly better than those of eligible students who did not sit the test and those of ineligible
students.
For this aspect, therefore, the self-selection bias is presumably positive and this is so in almost all
Disciplinary groups (see Table 8.2) and almost all Universities (see Tables 8.4 - 8.6).
33
Moving up through the TECO percentiles, the relative advantage (positive fixed effect) of the Universities of Bologna, Eastern Piedmont and Udine, and the relative disadvantage (negative fixed effect) of the Universities of Naples and Tor Vergata diminishes, while the relative disadvantage of the University of Messina increases. Finally, as regards the comparison between the two Universities in the Rome area, the relative disadvantage of Tor Vergata decreases and ceases to be statistically significant when moving up through the percentiles.
84
85
This distortion occurs equally in all or almost all distribution quartiles (see Table. 8.7). The question
hence arises of how much the results of some Universities with a low P index would go down if the
participation rate were to increase, with the inclusion of pre-registered students or eligible students
who did not show up for the test. For example, looking at Table 7.1, all other things being equal, the
TECO score seems to decrease by 15.89 points for every reduction of 1 point in the mean grade in
University exams. Therefore, if the decrease was 0.17 points (as indicated by Table 8.1 in the
comparison of means between eligible students who came to sit the test and those who did not), all
things being equal, the TECO score would thereby change from the current mean level of 999.53 to the
potential level of 996.83 (∆= -2.70)34.
34
The distortion in the TECO results, implicit in the self-selection of students who sat the test with respect to all other eligible students, will certainly disappear once, as ANVUR recommends, the generic competences test becomes standard practice for all graduating students and all Universities. Passing the test will not be a requirement for periodic accreditation, but sitting it before the degree will be required. Of course, in the future other changes of great significance for assessing the path of studies in University may be introduced, affecting students that have been enrolled for three or four years. Such changes could include combining disciplinary tests with generic tests, assessing competences upon both entering the University and exiting from it, and overcoming the opposition from the Guarantor of Privacy through appropriate regulatory or legal changes, so that Universities may receive the individual results of their students in non-anonymous form.
86
More generally, the self-selection bias is confirmed to be positive for all other contextual variables (age,
citizenship, non-Italian language, working student, female gender, residence off-site, etc.) that are
significantly correlated, positively or negatively, with the TECO result. The eligible students who came
to sit the test are on average systematically ‘better equipped’ than those who did not, i.e. they show a
higher presence of success factors and/or a lower presence of failure factors (see Tables 8.10 and
8.16).
87
88
89
4.14 Externalities of merit
As partially already shown above, simple correlation analyses are useful for examining three issues: 1)
to check the influence of some contextual variables of sociological nature, previously indicated with
reference to social data, which econometricians are usually not very sensitive to; 2) to offer a few
preliminary elaborations on the sign and amount of the correction for the self-selection bias, which
makes it difficult to compare results between Disciplinary groups or Universities as well as between
different countries35; 3) finally, to examine some of the factors concerning the influence on the TECO
results of externalities due to the student and academic environment.
On this latter aspect, it appears that the quality of the student environment, approximated by the merit
index M (which increases with the mean diploma grade, VMD, and decreases with the mean grade in
University exams, VME), is correlated, albeit weakly, with the TECO result in the various Disciplinary
groups (see Table 6.3.6). However, this correlation becomes negative with respect to the Universities
(see Table 6.3.10) because of the “inflation grading” existing in both diploma and University grades
particularly in the South (it should be noted that Cagliari is an exception; it should also be noted that
Rome 1 is conversely very rigorous in this respect).
35
Not forgetting that the sample which sat the CLA+ in the United States is not larger than the sample which sat the TECO in Italy, but the college students who sat the CLA+ are not self-selected: they are selected at random or not selected at all.
90
91
The positive externality on the TECO created by a high-profile academic environment seems to be
instead much stronger. There is an excellent correlation (0.6-0.8) between the TECO results and the
scientific value of the teachers involved in teaching courses or in the Universities of the pilot phase, as
indicated by R12, derived from VQR (see Tables 6.3.1 and 6.3.3): ex post, the quality of teaching
outcomes has a very good match with the quality of the research results of University teachers.
92
An equally good match also exists between the teaching outcomes shown by TECO and the
engagement shown by teachers in a form called “SUA” as regards setting objectives in terms of
expected results in generic competences. This is demonstrated in Tables 9.2 and 9.3. First of all, it
emerges that the more frequent evaluations are B for TECO and A for the ‘SUA’ form, so that the
combination (B;A) occurs in 42% of the cases examined. On the whole, it should be pointed out that in
one third of the cases36 there is total concordance between the qualitative ratings of the results
achieved ex post in the TECO in different classes in different Universities, and of the ex ante
formulations of expected outcomes in the ‘SUA’ form. Also, the cases of strong discrepancy between
these two forms of evaluation are rare, amounting to less than 8.5%.
36 The cases examined do not include those in which one of the values is "null" or the ‘SUA’ rating is not univocally
defined (for example, cases of a ‘B/C’ rating).
93
# evaluations # evaluations # evaluations # evaluations # evaluations # evaluations # evaluations # evaluations # evaluations # evaluations # evaluations # evaluations
L-25
Agricultural and
forestry science and
technology
29 (B;B) 22 (B;A) 4 (B;A) (null;A) 21 (B;A) 4 (B;A) 2 (C;null)
L-26 Food science and
technology5 (A;A) 8 (B;A) 3 (B;A) (null;A) 14 (B;B) 8 (C;B) (null;A)
L-38
Livestock rearing
science and
technology
8 (B;A) 8 (B;A) (null;A) (null;A) 1 (A;A) 2 (C;B)
arch LM-4 C.U.
Architecture and
construction
engineering/archite
cture (5-year
course)
38 (A;A) (null;A) 50 (B;A) 14 (B;A) 139 (B;A) 9 (B;A) 22 (B;A) (null;A)
art L-3
Visual arts, music,
performing arts and
fashion
9 (B;B) 7 (B;B) 5 (A;B) 5 (A;A) 31 (B;B) 7 (B;null)
L-13 Biology 15 (B;A) 33 (B;B) 17 (A;A) (null;B) 15 (B;B) 35 (B;B/C) (null;A) 11 (B;A) (null;B) 7 (C;A) 2 (B;B)
L-2 Biotechnology (null;A) 37 (B;B) 5 (A;A) 3 (A;B) (null;A) 13 (B;A) 10 (C;B) 3 (C;B) 11 (B;A) (null;B) (null;A) 1 (C;B)
L-22 Physical education
and sports18 (B;A) (null;A) 14 (B;A) (null;A) 2 (C;B) 2 (C;B) (null;A) 2 (B;A)
chim L-27 Chemistry and
chemical technology5 (C;A) 36 (B;A) 9 (B;B) 1 (A;A) 1 (B;A) 44 (B;B) 3 (C;B) 7 (B;A) (null;A) (null;A)
comun L-20 Communication
science20 (B;A) 3 (B;null) 36 (B;A) 12 (B;B/C) 4 (B;A) 41 (B;A) 2 (B;A) 1 (C;B) 2 (C;A) 10 (B;A)
L-1 Cultural Heritage 2 (B;null) 33 (B;C) 3 (A;B) 3 (B;A) 2 (A;A) (null;B) 37 (B;C) 2 (C;B) 12 (B;null) (null;B/C) 1 (B;null) 2 (B;B)
L-1 Archaeology 1 (C;C) 10 (A;null) 13 (B;A) 9 (B;C) 3 (C;B) 3 (A;null)
L-43
Diagnostics for the
conservation of
cultural heritage
4 (B;A) 1 (A;null) (null;A) (null;B) 1 (C;null)
L-18
Economics and
business
administration
71 (B;B) 14 (B;B) 48 (B;A) 35 (A;A) 36 (B;A) 77 (C;B) 8 (B;B) 19 (B;B) 1 (A;A) 21 (C;A) 10 (C;A)
L-33 Economics 3 (C;A) 9 (C;A) 3 (B;A) 23 (B;A) 20 (B;A) 40 (B;A) 17 (B;B) 6 (C;B) 2 (B;A) 2 (A;A) (null;A)
L-29
Pharmaceutical
science and
technology
10 (B;B) 2 (B;B) (null;A) 1 (A;B) 7 (B;A) 4 (C;B)
LM-13
Pharmaceutical
science and
technology
13 (B;B) 54 (B;C) 21 (B;B) 4 (B;A) 7 (B;B) 13 (C;B) 17 (B;B) 8 (C;A)
LM-13 Pharmacy 26 (B;B) 43 (B;C) 31 (B;B) 11 (B;A) 13 (B;A) 38 (B;B) (null;B) 47 (C;B) 19 (C;A) 5 (B;B)
fi lo L-5 Philosophy 14 (B;B) 19 (B;B) 9 (B;B) 8 (A;B) 18 (B;A) 19 (B;A) 10 (C;A) 6 (C;B) 2 (C;A) (null;B) 3 (B;A)
form L-19 Education science 48 (B;A) 2 (A;A) 30 (B;A) 21 (B;A) (null;B) 8 (B;A) 17 (B;B) 2 (B;A)
L-15 Tourism 12 (B;B) 5 (A;B) 3 (B;B) 3 (B;null) 10 (B;A) 3 (B;A) 1 (C;A) 1 (C;null)
L-6 Geography 4 (B;null) 6 (B;B) 5 (B;A)
L-14 Science of legal
services20 (B;null) 7 (C;B) 12 (B;B/C) 1 (A;B) 2 (A;B/C) (null;C) (null;A) (null;A) 1 (C;B) 2 (B;A)
LMG/01 Master’s degree in
law44 (B;B) 184 (B;C) 62 (B;B) 46 (B;B) 53 (B;A) 119 (B;B) 99 (B;B/C) 24 (C;B/C) 59 (B;B) 86 (B;A) 34 (C;B) 19 (B;B)
L-8 IT Engineering 7 (B;A) 4 (B;A) 4 (A;A) 33 (B;A) 113 (B;A) (null;A) 25 (B;A) (null;A) (null;A) 6 (C;A)
L-9 Industrial
Engineering37 (B;A) 3 (C;A) 2 (A;A) 35 (B;A) 95 (B;A) 35 (B;A) 42 (B;A) 20 (B;A) (null;A) 2 (B;A)
lett L-10 Arts 15 (B;B) 38 (B;B) 12 (B;B/C) 13 (B;B) 12 (A;B) 13 (B;A) 52 (B;B) 14 (B;B/C) 15 (B;B) 3 (C;C) 1 (A;C) 2 (C;A)
L-11 Modern languages
and cultures13 (B;C) 17 (B;B) 7 (B;C) 4 (A;A) 3 (B;A) 7 (B;A) 50 (B;A) 17 (C;B/C) 25 (B;B) (null;B) 3 (B;null)
L-12 Linguistic
mediation3 (C;C) 7 (A;B) 40 (B;B/C) (null;A) 20 (B;A) 3 (B;B) 12 (B;null)
L-30 Physics science and
technology3 (B;B) 2 (B;B) (null;B) 29 (B;A) 56 (B;B/C) 8 (B;B/C) 3 (C;B) (null;B) (null;A) 1 (B;B)
L-31 Computing science
and technology2 (B;B) 7 (B;A) 1 (A;B) 4 (C;A) 2 (A;A) 9 (B;B) 48 (B;A) 1 (A;A) 9 (B;null) (null;A) 2 (B;C)
L-35 Mathematics 8 (B;null) 20 (A;A) 5 (B;B) 7 (B;A) (null;B) 23 (B;B) 53 (B;B) 10 (B;A) (null;B) 2 (C;B) 1 (A;A)
L-41 Statistics 14 (B;A) 9 (A;B) 2 (B;B) 44 (B;A) 3 (A;A)
med LM-41 Medicine and
surgery27 (B;A) 30 (B;A) 14 (B;A) 32 (B;A) 49 (B;A) 32 (B;A) 84 (B;A) 1 (A;A) 95 (B;A) 6 (B;A) 23 (B;A)
odon LM-46 Dentistry and
Dental Prosthetics12 (B;A) 1 (B;B) (null;A) 1 (B;A) 3 (B;A) 6 (C;A) 20 (B;A) (null;A) 1 (B;A)
L-16
Administration and
organization
science
12 (B;null) 12 (B;A) 11 (A;B) (null;A) 2 (C;B) 5 (C;null) 1 (A;A) 4 (B;C)
L-36
Political science
and international
relations
4 (B;null) 38 (B;B) 9 (A;A) 9 (B;A) 19 (B;A) 32 (B;B) 18 (B;A) 5 (C;B) 1 (B;B) 6 (B;null)
L-37
Social sciences for
cooperation,
development and
peace
1 (A;B) 2 (C;A) 3 (A;A) 7 (B;A)
psic L-24
Psychology and
psychological
technique
54 (B;A) 19 (B;A) 31 (B;B) 61 (B;A) 12 (B;A) 11 (B;A) 1 (C;A) 2 (C;B)
L-39 Social services 8 (B;A) 7 (B;A) 4 (A;A) 9 (B;B) 4 (B;B) (null;B) 1 (C;B) 1 (C;A)
L-40 Sociology 3 (B;A) 6 (A;A) 1 (A;null) 13 (B;A) 22 (B;A) (null;A)
L-42 History 16 (B;A) 5 (B;null) 10 (C;A) 2 (B;A) 17 (B;A) 6 (B;A) 1 (C;null)
L-17 Architecture 3 (B;A) 12 (B;A) 47 (B;B) 5 (A;A) (null;A)
L-21
Land-use, urban,
landscape and
environmental
planning
5 (B;A) 19 (B;A) 20 (B;null) (null;A)
L-23
Construction
science and
technology
(null;B) 11 (A;null) 19 (B;A) (null;A) 1 (C;A)
L-32
Environment and
nature science and
technology
4 (B;null) 15 (B;B) 5 (B;A) 6 (B;A) (null;A) 2 (B;A) 22 (B;A) 1 (C;A) (null;A) (null;A) (null;A)
L-34 Geology (null;A) 1 (B;B) 1 (A;A) 3 (A;A) 46 (B;A) (null;A) 1 (A;A) (null;B)
L-4 Industrial design (null;A) 10 (B;B) 53 (B;A)
L-7
Civil and
environmental
engineering
5 (B;null) (null;B) (null;A) 38 (B;A) 8 (B;A) (null;A) 22 (C;B) 4 (B;A) (null;B) 2 (B;A)
vet LM-42 Veterinary medicine 37 (B;A) 11 (A;A) 28 (B;B) 16 (C;A) 1 (C;A)
soc
terr
The light-grey shading indicates cases for which the evaluations differ by two levels (C;A or A;C)
The dark-grey shading indicates cases in which one of the two evaluations is missing (null), i .e. either there are no tested students or the SUA form is missing. In the brackets, the first value is the TECO result, the second value is the SUA evaluation
polit
l ing
mat.fis.stat
farm
geo
giu
ing
CA
agr.al
bio
cult
econ
FI RM1 RM2 NA LE ME
TABLE 9.2: Analysis of the correlation between TECO results (effective learning outcomes) and the formulation of expectations in the SUA forms for academic year 2012-2013 (expected learning outcomes),
per class in the 12 Universities participating in the TECO pilot test
Disciplinary
GroupClass Code Class Description
Number of students tested with TECO (#) and evaluations (TECO;SUA FORMS)
PO MI PD UD BO
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4.15 Overview in 20 points of the main outcomes of the TECO pilot test
Finally, the outcomes of the pilot test can be summarised in the following 20 points.
1. Study careers, even for students who graduate without lagging behind, are mostly irregular.
Almost two thirds of students graduating within the 3rd academic year of a three-year first-
cycle degree course do so without managing to complete the required number of basic and
characterising study credits at least 6 months before graduation. In the 12 Universities
participating in the pilot phase, only about 18 to 21% of students of the 3rd and 4th year
(depending on whether the problem is considered before or after the summer exams session)
complete within this session all the basic and characterising study credits required in the first
3 years of their course (which can last 3 years or more, depending on whether it is a first-cycle
course or a single-cycle master course). Only this low proportion of students is in a ‘regular’
situation (as defined for present purposes).
2. The ratio of regular students to all students enrolled in the 3rd and 4th years (regularity rate
or index, R) ranges very widely across Disciplinary groups. The best are those for which
students must pass a national admission test (Medicine, Architecture, Veterinary Medicine and
Dentistry) or a local admission test with 100% of entrants tested (Psychology) or some with a
local admission test for a majority of entrants (e.g. Pharmacy). The regularity index (R) is
markedly higher in the Health Macro-group versus all other Macro-groups.
3. The participation rate or index (P), i.e. the ratio of students who came to sit the TECO test
versus all those eligible, is low: on average, slightly less than 27% of eligible students came
voluntarily to sit the test, which is slightly more than half the number anticipated by the 12
participating Universities. As a result, TECO scores and results are known for a mere 5 percent
(Q) of 3rd and 4th year students excluding those enrolled in courses for the health professions.
The participation index (P) is markedly higher in the Scientific Macro-group versus all other
Macro-groups.
4. The frequency distribution of TECO scores and results approximates a normal Gaussian-type
distribution, suggesting that the TECO passes the feasibility test in Italy.
5. The results obtained by Italian students on the TECO are perfectly comparable with those
obtained by American students in the ‘twin’ CLA+ test. More specifically, young Italians show
an identical mean level and the same variance as regards overall performance, with superior
writing effectiveness and technique as well as greater ability to argue and in critical reading,
but lower scientific-quantitative reasoning quality.
6. In the breakdown by gender, female students show on average a higher regularity index, a
lower participation index and a lower performance level, especially as regards the scientific-
quantitative component.
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7. The correlation, individual by individual, between the scores obtained in the “literary” part of
the test (PT) and the “scientific-quantitative” part (SRQ and particularly SQR) in Italy is low,
amounting to less than half that in the United States. This is the only aspect for which a
difference is observed between the results of the pilot test in Italy and the results of the CLA+
in the USA States – and it is a sign of the so-called “two cultures” existing in our country since
the Renaissance: a marked dissociation between the humanistic and scientific domains. The
mean correlation for Disciplinary groups and Universities is instead high: on average, the best
as well as the worst performing students are consistently so, for both tested components.
8. Mean TECO results are significantly higher than the ITA12 mean for only 3 of the 25
Disciplinary groups defined in this Report (Mathematics-Physics-Statistics, Medicine and
Psychology), whereas they are significantly lower for 6 groups; the lowest result is observed
for the Education group.
9. The percentage of tested students scoring under the pass grade is 25.22%. Good students
(those in the top quartile of the distribution of scores for ITA12) amount to 33.66% of tested
students.
10. For Italy as a whole, the percentages of top performers (students scoring higher on the TECO
than the national mean of the 10th decile), high performers (those scoring higher than the
mean of the fourth quartile), and low performers (those scoring lower than the mean of the
first quartile) are respectively 3.55%, 8.85% and 9.81% of tested students. The highest
proportion of top performers is observed in Mathematics-Physics-Statistics, with 7.73% for
both genders and even 7.09% for female students, which in this group outnumber the male
students as regards high performers (16.31% versus 14.98%). The highest percentage of high
performers is observed in Medicine (18.32% for both genders).
11. As regards the contextual variables related to personal data, there is a systematic downwards
relationship between the TECO result and the variables age, female gender (versus male) and
residence outside the region of the University's location, as well as an upwards relationship
relative to Italian citizenship, Italian spoken at home (versus non-Italian citizenship and
language), and time since diploma obtained. For the contextual variables related to merit,
there is a positive correlation between the TECO result and coming from a “classical studies”
high school (compared to other types of high schools), mean diploma grade, and mean
University grades.
12. A high professional and cultural status of the parents correlates strongly with success in the
TECO: when the mother is a management-level or a white–collar employee, has a degree or
high school diploma, regardless of the father's position, results above the mean and median are
observed; and this applies equally to the father. The absence of at least one parent is the worst
condition for the level of generic competences assessed by the TECO.
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13. The results in the TECO also seem to be, on average, better for those who, among the
graduating students, had to overcome some initial selection, a national admission test
(Medicine) or a local one for 100% of entering students (Psychology), or had to exercise a form
of self-selection by opting for a study course characterised by low mean University grades
(Mathematics-Physics-Statistics), after leaving school with, on average, a very high diploma
grade. By itself, however, passing a University admission test is not a guarantee of success in
the TECO.
14. For the above-mentioned variables, the observations from analysing simple correlations
between TECO results and contextual variables are generally confirmed by more sophisticated
multiple correlations (linear regression models) based on the “all other things being equal”
hypothesis. For the contextual variables discussed below, only simple correlations with TECO
results have been analysed.
15. The various types of support for studying do not seem to compensate for the disadvantages of
different kinds affecting those students who usually have recourse to support. Moreover,
students with more technological equipment on average perform better on the TECO, as well
as those who go on at least one trip per year outside their region or abroad.
16. A majority of tested students (80.28%) consider that they have acquired in the course of their
studies the competences needed to perform well in the TECO, but those who are more self-
satisfied perform on average worse than their more pessimistic colleagues. This holds true
everywhere except in the highest quartiles.
17. There is a high correlation (0.6 to 0.8) between the scientific quality of teachers involved in the
study courses and Universities concerned (as measured by the R12 index based on the VQR
ratings) and the quality of learning outcomes in the corresponding structures (as measured by
the TECO results).
18. The quality of the student environment, approximated by the merit index M (which increases
with the mean diploma grade, VMD, and decreases with the mean grade in University exams,
VME), is correlated, albeit weakly, with the TECO result in the various Disciplinary groups.
However, this correlation becomes negative with respect to the Universities because of the
“inflation grading” existing in both diploma and University grades particularly in the South.
19. A good match is observed between the TECO learning outcomes of teaching structures and the
engagement shown by teachers in those structures as regards setting objectives in terms of
expected results in generic competences (as set out in the SUA-CdS forms). In one third of the
cases there is total concordance between the results achieved ex post in the TECO in different
classes in different Universities and the qualitative ratings of the ex ante formulations of
expected outcomes set out by the teachers of those same classes in the SUA-CdS forms. Also,
the cases of strong discrepancy between these two forms of evaluation are rare, amounting to
less than 8.5%.
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20. There is a self-selection bias, of positive type, which in some cases complicates the
comparative assessment of Disciplinary groups or Universities characterized by very different
participation indices and TECO results. For the time being it is not possible, for example, to
assert that – under the condition of all other things being equal as regards all contextual
variables – the University of Bologna had greater mean success in the TECO than Eastern
Piedmont. The mean TECO results, after filtering out the multiple regression fixed effects of
contextual variables, are respectively 46.869 and 26.088. However, the participation rate was
only 13.91% for Bologna versus 63.94% for Eastern Piedmont. On the other hand, Milan
University appears to perform better than Bologna University given the almost identical fixed
effects and more than double participation rate (31%). For the same reasons Udine, with a
fixed effect which is the highest of the 12 participating Universities (51.395) and with the
highest participation rate (64.06%), can surely be considered the top performing University.
4.16 Overview of the outcomes of the TECO pilot test as regards geographic areas
In this section we summarise the main results of the pilot test as regards the geographic areas North,
Centre and South. For a more detailed analysis on the Southern Universities, see the INVITALIA Report
on the diagnosis and assessment of achieved learning outcomes as regards generic competences acquired
by graduating students in the Universities of Naples Federico II, Lecce, Messina and Cagliari (ANVUR,
2014B).
1. In the South, in the pilot testing phase, the students enrolled in the 3rd and 4th year of a three-year
first-cycle course or single-cycle master course, excluding courses for the health professions, are
35,196 (29% of ITA12). Of these, the students eligible to sit the TECO test are 4,436 (20% of
ITA12), and those who did sit the test are 1,001 (17% of ITA12). In the Centre+North, the
corresponding numbers for the 2012-2013 pilot testing phase are 86,419 (71%) enrolled
students, 17,436 (80%) eligible students, and 4,852 (83%) tested students (see Tables 10 and 1.1).
2. The regularity index (R) is particularly low in the South: the Southern eligible students, i.e. those
who have acquired the number of basic and characterising study credits required by the 3rd or 4th
year, are merely 12.5% of students enrolled in the 3rd and 4th year, whereas this percentage
reaches 20.1% in the Centre + North (see Table 2.5).
3. In the Centre + North, 64.19% of students graduating from three-year first-cycle courses without
lagging behind do not manage to complete the required number of basic and characterising study
credits at least 6 months before graduation. This percentage reaches a high 82.67% in the South,
with dire consequences on the effectiveness of study careers (see Table 1.1).
4. The index of participation to TECO (P) is on average low in the South: fewer Southern students
(only 22.57% of those eligible to do so) take up the opportunity of participating to this type of test
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and voluntarily come to sit it. In the Centre + North, the corresponding figure is 27.83% (see Table
2.5). There are nonetheless Universities in the South with high participation rates, e.g. Messina
(36.59%), and Universities in the Centre + North with low participation rates – with Bologna
showing the overall lowest rate.
5. The mean TECO results in the South are significantly lower than the mean for all Universities
participating in the pilot phase, despite the fact that the participation index is lower in the South
and the self-selection bias, which is of positive type, drives results upwards in the case of lower
participation rates (see Tables 4.11 and 4.14).
6. The lower mean results in the South are due to a composition effect (the percentage of tested
students coming from stronger Disciplinary groups such as Mathematics-Physics-Statistics is low,
amounting to 2.1% of all tested students versus 7.56% for the Centre + North) as well as to a level
effect (for example, the mean TECO result for Mathematics-Physics-Statistics students is 1,023.19
in the South versus 1,042.47 in the Centre + North) (see Tables 4.3-4.5 and TAB 4.1 of ANVUR,
2014B).
7. The Disciplinary groups that score significantly higher in the TECO than the mean for ITA12 are 9
in the North, 3 in the Centre and only 1 (Medicine) in the South (see Tables 4.3-4.5).
8. The gender gap (to the disadvantage of female students) is particularly marked in the South, as
regards both participation to the test and results on the test. Vice versa, female students are
better everywhere as regards regularity (see Tables 3.6, 3.7 and 2.5).
9. Mean results are better on the literary component of the test versus the scientific-quantitative
component in the South, whereas the opposite is observed in the Centre + North, although not
very significantly in both cases. The mean results on both components are lower in the South
versus the Centre + North, but the difference is not significant for the literary component whereas
it is significant for the scientific-quantitative component (see Tables 4.15 and 4.2 of ANVUR,
2014B).
10. The percentage of tested students scoring under the pass grade is 34.07% in the South versus
23.29% in the Centre + North. Good students (those in the top quartile of the distribution of
scores for ITA12) amount to 25.47% of tested students in the South versus 35.35% in the Centre +
North (see Table 3.6 of ANVUR 2014B).
11. Although they are less numerous, the good tested students in the South are comparable as regards
the TECO level to all others: 23 Southern students, equal to 2.3% of tested students, score 30 or
higher on the TECO (see Table 3.6 of ANVUR 2014B).
12. The mean grade in University exams sat so far by Southern graduating students is very high
(26.91 out of 30 versus 26.53 out of 30 in the Centre + North) and the variation coefficient is
particularly low (0.0663 versus 0.0737 for the Centre + North). Conversely, the variation
coefficient of TECO results for Southern students is particularly high (0.169 versus 0.156 for the
Centre + North). This points to the difficulty of identifying first-rate graduating students in the
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South on the basis of University grades – whereas TECO does allow this identification, although
the selection via the two methods coincides only partly (see Table 3.7 of ANVUR 2014B).
13. In the South, the percentages of top and high performers (respectively 2.3% and 5.9%) are lower
than the corresponding Italian means, while the percentages of low performers are higher (as
high as 14.19%) (see Table 5.4).
14. The South has the highest proportion of students who consider that they have acquired in the
course of their studies the competences needed to perform well in the TECO (81.31% versus
80.28% for ITA12), whereas they perform worse than those graduating students who are less
self-satisfied: in the Southern Universities, the TECO mean result is 956.15 for those who answer
positively as regards their perception of acquired competences, versus 976.62 who answer
negatively. The University of Cagliari is an exception in this respect: students who are positive
about their acquired competences (who are less numerous than elsewhere in the country)
achieve on average better results than their more pessimistic colleagues (see Tables 9.7 and 9.8).
15. In the South, no matter how high parents’ occupational status and educational qualifications may
be, the mean TECO results are always lower than the mean for all participating Universities.
Conversely, of the 10 combinations of parents’ occupational status and educational qualifications
that were analysed, 4 seem to lead to results above the mean in the Centre and 9 in the North (see
Tables 6.1 and 6.14-6.17 of ANVUR 2014B).
16. The 4 Southern Universities obtain lower VQR 2004-2010 ratings versus the other ITA12
Universities: the R12 indicator is 0.88 in the South versus 1.01 in the Centre and 1.14 in the North.
This goes hand in hand with lower performance in the TECO (see Table 6.3.4). Nevertheless, the
correlation between the scientific quality of teachers (R12 index) and the quality of learning
outcomes in the corresponding University structures (TECO) is weaker in the South
(medium/low) versus the Centre (medium) and North (medium) (see Table 6.1).
17. Looking within, the South is not all the same, as is not the rest of the country: the University of
Cagliari shows characteristics that generally belong more to the Centre + North than the South,
while the two Rome Universities are for many aspects comparable to the Southern ones (see
Tables listed in the preceding points).
18. The self-selection bias is of positive type, which complicates the comparative assessment of
individual Universities. It is not possible, for example, to assert that – under the condition of all
other things being equal as regards contextual variables – Cagliari shows a better mean
performance than Messina. The mean TECO results, after filtering out the multiple regression
fixed effects of contextual variables, are respectively -22.597 (not significantly different from
zero) and -65.926; however, there is a large difference in participation rate (23.58% for Cagliari
versus 36.59% for Messina). On the other hand, Cagliari does appear to perform better than
Naples as shown by the higher fixed effect (-55.633) together with a higher participation rate
100
(19.62%). For the same reasons, Messina appears to perform better than Lecce: the fixed effects
are virtually identical while the participation rate for Lecce is markedly lower.
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5. TECO
Test on the generic competences of graduating
students
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5.1 PT module (open-ended response)
Introduction to the TECO test The TECO test is designed to be completed in 90 minutes. The test includes the “Parks” task (open-ended
response task) lasting 60 minutes and a set of multiple-choice questions lasting 30 minutes.
Instructions for the “Parks” task
Task scenario Teroli is a city located near a national park. The City currently funds two programs for middle school students. One program, “Forest Adventures”, is a summer camping program. The other program, “Sports & School Experience”, combines academic tutoring and sports. Teroli can no longer afford to fund both programs at their current levels. At tonight’s City Council meeting, the council members are going to discuss whether the city should fund only one program. Imagine that you work for the City Management. The City Manager, Cristina Diliberti, has asked you to help prepare for the meeting by reviewing the documents provided in the Document Library. Your final task will be to write a report for Ms. Diliberti that analyzes the two programs and makes a recommendation about how the City should fund the middle school programs. You have 60 minutes to complete this entire task.
Final essay prompt Your task is to write a report for Ms. Diliberti that analyzes the two programs and answers the question, “If Teroli cannot afford to fund the Forest Adventures and the Sports & School Experience programs at their current levels, what should the City do?” You could recommend funding only one program, modifying the program(s), or something else. In your report, support your recommendation with information found in the Document Library and explain why other possible recommendations are not as good. You could answer by indicating the main arguments in favour and against the Forest Adventures and the Sports & School Experience programs. There is no "correct" answer. Your report should clearly describe all the details necessary to support your position. Your report will be judged not only on the accuracy of the information you provide, but also on how clearly the ideas are presented, how thoroughly the information is covered, how effectively the ideas are organized, and how well your writing reflects the conventions of the Italian language. While your personal values and experiences are important, please answer all of the questions in this task solely on the basis of the information provided above and in the Document Library. Type your response in the space provided. Write as much as you want; you are not limited by the size of the response area on the screen.
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Document Library Contents Document 1 – Youth Programs Website
Document 2 – Hospital Data Report
Document 3 – Letter from Oliviero Sansoni
Document 4 – Letter from Pietro Rossi
Document 5 – Newspaper Article
Document 6 – Email from School Principal Aroldi
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Document 1 – Youth Programs Website
Teroli Department of Parks & Recreation Youth Programs
Along with our regular activities, the City currently offers two exciting programs for the youth of Teroli. These programs are intended to promote our adolescents’ academic and personal growth.
SPORTS & SCHOOL EXPERIENCE
For boys and girls in middle school, this program offers a variety of sports activities, in conjunction with an innovative academic tutoring service, provided largely by student-athletes from the local University. The Sports & School Experience program is offered year-round at the Colle di Teroli Recreation Centre. Sports activities included in the program are “hot ball”, soccer, baseball, volleyball, gymnastics, and tennis. Tutors are largely college-aged students who provide athletic training, academic support, and an invaluable mentoring experience to program participants. All program staff members are certified in CPR (cardio-pulmonary resuscitation) and first aid. Most of the tutors are themselves athletes in University teams, which is a very big draw for our students.
Student fee: €25/year
FOREST ADVENTURES WILDERNESS EXPEDITION
The Forest Adventures organization has been taking individuals on wilderness expeditions since 1962. Annually, over 10,000 students across the nation gain a deeper knowledge of themselves and the world in which they live, through adventure-based wilderness courses that include activities such as hiking, rock-climbing, sea kayaking, and sailing.
For several years, the City has sponsored two-week backpacking expeditions into wilderness areas adjacent to Teroli. The curriculum emphasizes personal growth through mastering new skills, working with a diverse group of peers, and purposefully tackling obstacles, rather than avoiding them. Forest Adventures’s teams of staff provide all of the equipment, wilderness expertise, and experience to guide participants through this life-changing experience.
Student fee: €250/expedition
Financial assistance for all programs promoted by the City of Teroli is available to qualifying families. For more information, please call the Youth Programs office between 8:30 a.m. and 5:00 p.m., Monday through Friday.
http://co.ter.it.gov/parchi/gioventù/0004214
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Document 2 – Hospital Data Report
SIOT – Teroli Hospital Information System Report Search (1 of 2):
Admitted to Emergency Room E age>9 E age<19 E diagnosis=fracture O diagnosis=dislocation O diagnosis=rupture O diagnosis=laceration O diagnosis=abrasion E insured=Forest Adventures E year=current SEARCH Report Results: 10
Admitted to Emergency Room: 10 Referred directly to Operating Room: 1
Treated in Emergency Room: 9 Returned after admission: 0
------------------------------------------------------------------------------------------------------------ Report Search (2 of 2):
Admitted to Emergency Room E age>9 E age<19 E diagnosis=fracture O diagnosis=dislocation O diagnosis=rupture O diagnosis=laceration O diagnosis=abrasion E insured=Sports&School E year=current SEARCH Report Results: 46
Admitted to Emergency Room: 46 Referred directly to Operating Room: 22
Treated in Emergency Room: 24 Returned after admission: 0
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Document 3 – Letter from Oliviero Sansoni Dear Ms. Diliberti,
Twenty years ago I opened Centrosport di Teroli, the largest sporting equipment store in our city. For many years I have supplied sports equipment to our city’s young athletes at the start of each new season, and enjoyed watching Teroli’s youth develop into fine young citizens. I continue to see our city’s sports programs as a good investment in our future. I do not share the same feeling about the Forest Adventures program, especially considering the risk of injury in the activities they promote, and all at substantially greater expense!
In all this time, I have never heard of anyone getting attacked by a boar, getting lost, or breaking a leg, while walking across the ball fields at the recreation center. The same cannot be said about hiking in the wilderness, as we have learned from the recent incident with the Forest Adventures program where a student broke a leg while hiking.
While I’m sure there are some fine things to be said about the Forest Adventures program serving youth from low-income, inner-city families, I can say that the marvellous basketball, handball, football, tennis, soccer, and gymnastics programs run by Teroli’s Department of Parks and Recreation have served our city’s youth, including my own children, very well over the years. Furthermore, the Sports & School Experience program, which is also run by Teroli’s Department of Parks and Recreation, does a great job of motivating students to study while also providing them with an opportunity to work with coaches and athletes from the local University.
Maria Spilimberghi, a customer and parent of a Teroli Middle School student, told me that her daughter loves the sports and the wonderful staff of college students assembled for the Sports & School Experience program. In fact, her only complaint is that she wishes the program started a little later so that her daughter could attend the After-School Program (for required tutoring) and not miss the beginning of the Sports & School Experience program.
Another customer, Gianna Corelli, also has a son in the Sports & School Experience Program. For the most part, she is a fan of the program, but when talking to her the other day, she mentioned that the coaches and tutors keep changing and that sometimes the tutors leave early and are unable to tutor their students. I told her this was most likely due to the fact that the coaches and tutors have schedules they have to maintain at their University. We are both big supporters of the program and this information won’t change that fact, but I still wanted to share this complaint with you.
Also, our city sports programs have a wonderful community-building effect in Colle di Teroli (e.g., parents socializing at little league football games or at the fundraising lottery, etc.). Our Neighborhood Association came together to refurbish the dugouts and fields, and to refinish the courts in the parks, so we wouldn’t have to use the middle school playground. The recreation center is truly the “center” of our community. I say we use it through the Sports & School Experience!
Thanks again for your consideration. Keep up the good work you are doing for the City.
Oliviero Sansoni Owner, Centrosport di Teroli
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Document 4 – Letter from Pietro Rossi
To Ms. Diliberti Teroli City Manager Dear Ms. Diliberti, This letter is in response to your request for information on insurance premiums and the annual budget for youth programs paid for by Teroli’s Department of Parks and Recreation, including the Colle di Teroli Recreation Center. Table 1 breaks out the components of the overall premium for various program activities insured at the center, which are currently covered under general Policy #253-15685. Per your request, I have included your estimate of program enrollment that was used to calculate the annual premium. In general, insurance rates are determined more by the seriousness of injuries than their frequency.
Table 1. Annual Premiums for Programs funded by Teroli’s Department of Parks and Recreation
Policy n. 253-15685 Program Policy n. Enrollment Premium Sports & School Experience 253-15685-01 200 10,000 Forest Adventures 253-15685-02 35 1,400 Mommy and Me 253-15685-03 50 500 Gymnastics 253-15685-04 50 1,400
Holiday Music 253-15685-05 50 250
Science Camp 253-15685-06 50 750
Tennis Tournament 253-15685-07 50 2,000
League Football 253-15685-08 50 3,000
League Handball 253-15685-09 50 2,500 Conversational English 253-15685-10 50 250
Open Gym 253-15685-11 500 25,000
Total Premium €47,050
Table 2 breaks out the costs included in the annual budget for the youth programs that are funded by Teroli’s Department of Parks and Recreation Your estimated program enrollment numbers are included and were used to calculate the net cost per student. This information reflects savings for the use of Teroli Middle School classrooms, as well as an insurance discount from Region-funded insurance credit for programs that share resources between public agencies. Included in the material costs are supplies and on-site meals, and included in other costs are transportation, off-site meals, insurance, and external program costs (e.g., Forest Adventures). The net cost is calculated by subtracting the revenues from student fees that were not waived from the total program costs. Finally, financial assistance is provided as available and according to need as determined by a departmental review.
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Table 2. Annual Budget for Youth Programs funded by Teroli’s Department of Parks and Recreation
Program Enroll
ment Student
fees Staff
salaries Facilities
Costs Material
Costs Other Costs
Net Costs
Net Cost/Stu
dent
Gymnastics 50 1,250 7,500 3,500 1,000 3,125 13,875 278
Mommy and Me
50 1,250 6,000 500 500 2,625 8,375 167
Open Gym
500 ----- 10,000 10,000 500 15,000 35,500
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Forest Adventures
35 8,750 9,750 ----- 2,500 17,500 21,000 600
Science Camp
50 1,250 7,500 500 2,000 4,125 12,875 258
Sports & School Experience
200 5,000 7,500 2,500 1,000 10,000 16,000 80
Tennis Tournament
50 1,250 4,000 2,000 4,500 3,125 12,375 248
League Football
50 2,500 7,500 2,500 1,000 3,750 12,250 245
League Handball
50 2,500 7,500 2,500 1,000 3,750 12,250 245
Conversational English
50 1,250 6,000 500 500 2,625 8,375 167
Financial Assistance
--- ----- ----- ----- ----- ----- 25,000 -----
Total
€177.875
Please note that the information pertains to annual rates, regardless of the period of the program activities (e.g., two weeks for Forest Adventure, the summer months for the sports leagues, year-round for the Sports & School program). If you have any questions, please do not hesitate to call.
Best regards, Pietro Rossi
Vice-president AssiMar Group
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Document 5 – Newspaper Article
Teroli Gazette Wising Up in the Wilderness: Teroli Youth Praise Forest Adventures By our reporter Carlo Forte By his own admission, Enrico Marcovaldo was heading down the wrong path. “I was into a lot of things, ditching school, and hanging out with the wrong people,” the 13-year-old said. He knew that what he was doing was wrong. He even knew that he didn’t want to continue his adolescent spiral to nowhere, but he needed a push in the right direction, not another lecture from his parents or his guidance counselor. Instead, school personnel recommended he take a 14-day Forest Adventures course. Initially, Enrico wanted nothing to do with it. “I thought it was just going to be another camp,” he continued. “But it wasn’t what I was expecting. It was more of a learning experience.” Since participating, Enrico has made significant changes. “There were a lot of challenges; mostly mental,” Enrico said. “You’re out there for like two weeks, which is a long time when you’re in the woods. You start to think about how you are living your life. When I came back, I was a totally different person. Everyone was surprised.” Here’s how the camp works. Seven to ten students, none of whom previously know each other, gather for the adventure. Instructors put the students through outdoor activities, such as rafting and rock climbing. As each person learns new skills and is challenged, traits such as self-reliance, responsibility, and compassion begin to blossom. Though certainly not 100% effective, Forest Adventures’s recipe of nature and skill cultivation has reshaped even the most rigid of souls. “People sometimes want a change, but they don’t know what it is,” said Giovanna Petrini, the lead instructor on Enrico’s course. “What we help them do is find out what it is. Enrico’s experience was much like thousands of other Forest Adventures experiences in the last four decades.” Someone unfamiliar with the process might be a bit sceptical. You go into the wilderness for two weeks, and all of a sudden you’re a great human being? Mrs. Petrini explained: “It’s experiential education in a very powerful classroom,” she said “There’s sort of an alchemy and magic that happens in the wilderness. When people come into the new environment and have new challenges, it gives them new experiences.”
Yet, one misconception about Forest Adventures is that it is solely for youth with serious issues. In reality, it is for anyone who wants to give it a try. Raffaella Bignami, Senior Class President and a brilliant student, went only because her mom had taken a Forest Adventures course when she was young and asked her daughter to go. Though Raffaella’s personal and academic record were by no means troubled, she still found Forest Adventures to be an uplifting experience. “At first, it was weird because I’m the type of person that spends all her free time texting with friends and going to the mall, and here I am going on a trip where you don’t get to shower for two weeks. I thought I’d be lonely and that everybody would be really different from me. But as you get to know others, you find out you all have something in common, and need each other, and want to help each other. I got to see people who did have things going wrong in their lives, girls who didn’t want to try, and I saw them change. That helped me to better understand myself and other people. I changed, too. There’s no other experience
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like this.”
Prof. Davide Aroldi, principal of ITIS Galilei, has seen dramatic and positive effects in the students that the camp has served. “I see big changes in the inner-city youth who participate in the Forest Adventures programs. They have almost twice the schooling and graduation rates as their classmates who do not participate. Participants also have greater self-confidence, greater potential to do well in school, and over the past several years, I have seen several of them become model citizens in our community.” Prof. Aroldi believes that supporting these at-risk students is one of the best investments of public money. “The financial assistance the city provides makes participation in Forest Adventures possible for many families that couldn’t otherwise afford it. It’s an investment that pays returns to all of us.”
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Document 6 - Email from School Principal Aroldi
To: Ms. Cristina Diliberti From: Prof. Davide Aroldi Subject: Annual Academic Performance Report for ITIS Galilei, Teroli
Dear Ms. Diliberti, As requested, I looked at the annual change in test scores on the middle school examinations in English and mathematics. In addition to the overall results per subject, I also looked at the results according to enrollment in selected programs: the Sports & School Experience program, the Forest Adventures Wilderness Expedition, and the After-School Program. The results are provided in the table below. As you know, the Sports & School Program and the Forest Adventures program are offered by the Teroli Department of Parks and Recreation while the Teroli School District runs the After-School Program. The After-School Program is staffed by certified teachers from the district, and we currently have as many hired as the district can afford.
Annual percent change in scores on the examinations in Italian and mathematics according to enrollment in selected programs
Italian score Change since last year All students in the school (n = 533) +1% Students enrolled in: Sports & School Experience (n = 102) +2% Forest Adventures (n = 27) +3% After-School (n = 59) +11% Mathematics score Change since last year All students in the school (n = 533) +3% Students enrolled in: Sports & School Experience (n = 102) +9% Forest Adventures (n = 27) No change After-School (n = 59) +17%
Note: Error in estimates is +/- 1% on all measures.
Let me know if you need any additional information in preparation for the City Council meeting.
Best regards, Prof. Davide Aroldi Principal, ITIS Galilei Teroli School District
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5.2 Excerpt from the SRQ (closed-response items) module
Below we provide an excerpt from the closed-response items module. All other parts are covered by a confidentiality agreement within the framework of the CAE-ANVUR contract (see ALL. 10). Instructions for the multiple choice questions You have 30 minutes to complete this part of the test. The task consists in selecting the answers to 20
questions referring to the documents provided.
Read each question carefully and answer as best as you can. Each question is followed by four answer
options labelled with a letter. Answer by circling the letter corresponding to the answer you think is correct.
If you are not sure about the answer to a question, select the answer you think is most likely to be correct,
and move on to the next question. If you decide to change an answer to a question, draw an “X” through
your first answer and then circle the letter corresponding to your new answer.
While your personal values and experiences are important, please answer all of the questions in this task
solely on the basis of the information provided in the documentation and in the questions themselves.
The results on this test may be used by your Institute to improve the level of teaching provided to students.
We thus urge you to do the best you can.
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Update
September 18
In response to this post, several readers sent me links to a study conducted by Fischer and his colleagues in 2007. Similar to the Continental Tyres survey, this research revealed that playing racing video games was associated with “competitive road traffic behavior” and the “number of reported accidents. Some results are shown in Table 1 below.
Table 1 – Correlation between playing racing video games and driving behavior
(adapted from Fischer et al., 2007) Correlation between playing racing video
games and:
competitive road traffic behavior
cautious road traffic behavior
number of reported accidents
Men (n=106) 0.50** - 0.22** 0.29**
Women (n=92) 0.33** - 0.03* - 0.13*
Total (N=198) 0.49** - 0.21** 0.22**
** Indicates a significant correlation * Indicates a correlation not significantly different from 0
In a follow-up experiment, Fischer (2007) randomly assigned people to play a racing game or a “neutral” soccer game. Then the participants took a computer-simulation-based test of risky driving where they watched a video of a person driving and making a risky maneuver such as passing a car or approaching an active railroad crossing. Participants indicated at which point they would abandon the risky maneuver and researchers recorded the abandon time of the participants. Results revealed that men who played racing video games before the test demonstrated significantly riskier behavior than men who played neutral games. In contrast, playing a racing video game had no significant impact on women’s risk taking. The authors state, “On a practical level, our results pose the question whether playing racing video games leads to accidents in real-life road traffic”.
[Omitted] …
Video games - Questions
14. Which of the following research designs could be used to test the hypothesis that playing driving video games causes risky driving?
A. Give a real-life driving test to gamers and non-gamers and measure their driving skills.
B. Have gamers take a defensive driving course and see if their driving records improve in six months.
C. Compare the number of car accidents gamers and non-gamers have over the next 12 months.
D. Ask non-gamers to start playing driving video games for three months and observe changes in their driving records.
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[Omitted] …
1. Which of the following statements most accurately reflects the results of Fischer’s (2007) experiment?
a. Both men and women who played racing video games demonstrated riskier behavior than those who played neutral games.
b. Of the men and women who played neutral video games, women exhibited riskier behavior than men.
c. Men who played racing video games were riskier drivers than those who played neutral games, but the same was not true for women.
d. Both men and women who played neutral video games were better drivers than those who played racing video games.
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6. Index of Tables The following is a list of all tables produced for the analysis of the results of the pilot test. For those that have been shown already, reference is given to the section where they can be found; all other tables are shown in section 7. Other tables Introduction to the pilot test
- List of acronyms used in tables - TABLE 1: Additional costs and funds for ANVUR (000 €) for the 18 months of the TECO pilot
test phase (September 2012 – March 2014) (see section 2.3) - TABLE 2: Timeline and phases of the TECO pilot test over the scheduled 18 months (15
September 2012 to 15 March 2014) (see section 3) - TABLE 3: Contextual variables in the 12 participating universities, for eligible and ineligible
students (see section 3.7) - TABLE 3b: Variables related to the 12 participating universities - TABLE 4: Students enrolled in the 3rd or 4th year of a three-year first-cycle course or single-
cycle master course, excluding health care professions, and number within these of “graduating students” – All Italian universities – Academic Year 2010/2011, June-July 1011 (see section 3.11)
- TABLE 5: Students enrolled in the 3rd or 4th year of a three-year first-cycle course or single-cycle master course, including health care professions, and number within these of “graduating students” – All Italian universities – Academic Year 2010/2011, June-July 1011
- TABLE 6: Students enrolled in the 3rd or 4th year of a three-year first-cycle course or single-cycle master course, excluding health care professions, and number within these of “graduating students” – 12 Italian universities of the pilot test – Academic Year 2012/2013, 1 April 2013 (see section 3.11)
- TABLE 7: Students enrolled in the 3rd or 4th year of a three-year first-cycle course or single-cycle master course, excluding health care professions, in the 12 participating universities: eligibility for the test, pre-registration and attendance (see section 3.11)
- TABLE 8: Eligible and pre-registered students who did/did not come to sit the test, by condition of living off-site and travel time between university and residence, in relation to total eligible and pre-registered students who did/did not come to sit the test (see section 3.11)
- TABLE 9: Indicators and results on PT, SRQ, TECO per Disciplinary Group, broken down by University
- TABLE 10: Indicators and results on PT, SRQ, TECO per Disciplinary Group, broken down by Geographic Area
Part 1: Pilot test numbers - TABLE 1.1: Transition matrix for students, graduating students and graduated students in the
12 universities participating in the pilot test, from 1 April to 1 November 2013 (see section 4.1)
- TABLE 1.2: Enrolled students, eligible students and tested students, per Disciplinary Group - TABLE 1.3: Enrolled students, eligible students and tested students, per Macro-group - TABLE 1.4: Enrolled students, eligible students and tested students, per University - TABLE 1.5: Enrolled students, eligible students and tested students, per Geographic Area - TABLE 1.6: Percentage of eligible students per Disciplinary group, Macro-group, University
and Geographic Area, broken down by participation in TECO - TABLE 1.7: Percentage enrolled students, eligible students and tested students, per
Disciplinary Group within each Geographic Area - TABLE 1.8: Percentage enrolled, eligible and tested students per Disciplinary Group within the
Geographic Area NORTH - TABLE 1.9: Percentage enrolled, eligible and tested students per Disciplinary Group within the
Geographic Area CENTRE
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- TABLE 1.10: Percentage enrolled, eligible and tested students per Disciplinary Group within the Geographic Area SOUTH
- TABLE 1.11: Enrolled, eligible and tested students per University and Geographic Area, broken down by Gender
- TABLE 1.12: Mean age of eligible students broken down by Gender and percentage eligible students of female gender, per Disciplinary Group, Macro-group, University and Geographic Area
- TABLE 1.13: Eligible students’ mean diploma grade, mean age when diploma awarded and mean years since diploma awarded, per Disciplinary Group, Macro-group, University and Geographic Area, broken down by Gender
- TABLE 1.14: Percentage of eligible students per Disciplinary Group, Macro-group, University and Geographic Area, broken down by type of diploma
- TABLE 1.15: Percentage of eligible students who do not speak Italian at home per Disciplinary Group, Macro-group, University and Geographic Area, broken down by Gender
- TABLE 1.16: Distribution of eligible students according to distance from place of residence and University attended, per Disciplinary Group, Macro-group, University and Geographic Area
Part 2: Participation and Regularity
- TABLE 2.1: Regularity and participation indices per Disciplinary Group, highlighted according to the proportion of courses with an admission test and the level of grades (see section 4.1)
- TABLE 2.2: Regularity indices per Disciplinary group, broken down by Gender - TABLE 2.3: Regularity and participation indices per Macro-group, broken down by Gender
(see section 4.2) - TABLE 2.4: Regularity and participation indices per University, broken down by Gender (see
section 4.1) - TABLE 2.5: Regularity and participation indices per Geographic Area, broken down by Gender - TABLE 2.6: Number of pre-registered students, pre-registration index (PR) and participation
index for pre-registered students (TP), per Disciplinary group, Macro-group, University and Geographic Area
Appendix to Part 2: Participation and Regularity - TABLE A-2.1: Enrolled, graduating and tested students, regularity (R) and participation (P)
indices per Disciplinary group, Macro-group, University and Geographic Area, broken down by Gender
Part 3: TECO overall results - TABLE 3.1: Density distribution of TECO results by Gender and frequency of PT (Performance
Task) results and SRQ (Selected Response Questions) results in the 12 universities participating in the pilot test (ITA12) (see section 4.3)
- TABLE 3.2: Density distribution of TECO results per Disciplinary Group (see section 4.3) - TABLE 3.3: Density distribution of TECO results and descriptive statistics for PT, SRQ and
TECO, per Macro-group (see section 4.3) - TABLE 3.4: Density distribution of TECO results per University (see section 4.3) - TABLE 3.5: Density distribution of TECO results and frequency of PT (Performance Task)
results and SRQ (Selected Response Questions) results, per Geographic Area (see section 4.3) - TABLE 3.6: Results by Gender for the different PT components (Analysis and Problem Solving -
APS; Writing Effectiveness - WE; Writing Mechanics - WM) and SRQ components ( Critical Reading - CRE; Critique an Argument - CA; Scientific and Quantitative Reasoning - SQR) (see section 4.3)
- TABLE 3.7: Frequency distribution of TECO scores, broken down by Gender (see section 4.3) - TABLE 3.8: Descriptive statistics and individual correlation coefficients on TECO and CLA+
results and scores, for Italian and American students in 2014 (see section 4.4) - TABLE 3.9: International comparisons on the same test (CLA on PT) in 9 countries in 2012
(see section 4.4) - TABLE 3.10: Scores obtained in PT, SRQ and their components within the ITA12 quartiles - TABLE 3.11: TECO scores in the quartiles, per Disciplinary Group
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- TABLE 3.12: TECO scores in the quartiles, per Macro-group - TABLE 3.13: TECO scores in quartiles per University - TABLE 3.14: TECO scores in the quartiles, per Geographic Area - TABLE 3.15: Maximum TECO results in each percentile - TABLE 3.16: Maximum TECO results in each percentile, per Geographic Area - TABLE 3.17: Maximum TECO results in each percentile per University – NORTH - TABLE 3.18: Maximum TECO results in each percentile per University – CENTRE - TABLE 3.19: Maximum TECO results in each percentile per University – SOUTH
Appendix to Part 3: TECO overall results - TABLE A-3.1: Variance (VAR), standard deviation (STDEV) and variation coefficient (CV) for
results in the different test components
Part 4: TECO results and individual and mean correlations between PT and SRQ, per Disciplinary Group, Macro-group, University, Geographic Area and Gender
- TABLE 4.1: TECO results and variation coefficients per Disciplinary Group, broken down by Gender and highlighted according to the proportion of admission tests, the level of grades, and the significance of the distance versus the ITA12 mean (see section 4.5)
- TABLE 4.2: TECO results per Disciplinary group, broken down by Gender - TABLE 4.3: TECO results and variation coefficients per Disciplinary Group in the NORTH
Geographic Area, highlighted according to the proportion of admission tests, the level of grades, and the significance of the distance versus the ITA12 mean
- TABLE 4.4: TECO results and variation coefficients per Disciplinary Group in the CENTRE Geographic Area, highlighted according to the proportion of admission tests, the level of grades, and the significance of the distance versus the ITA12 mean
- TABLE 4.5: TECO results and variation coefficients per Disciplinary Group in the SOUTH Geographic Area, highlighted according to the proportion of admission tests, the level of grades, and the significance of the distance versus the ITA12 mean
- TABLE 4.6: PT and SRQ results, mean correlation between them and significance of difference between them, per Disciplinary Group (see section 4.5)
- TABLE 4.7: Individual and mean correlations between PT and SRQ results, per Disciplinary Group (see section 4.5)
- TABLE 4.8: TECO results and variation coefficients per Macro-group and Gender, highlighted according to the significance of the distance versus the ITA12 mean (see section 4.5)
- TABLE 4.9: PT and SRQ results, variation coefficients, mean correlation between them and significance of the difference between them, per Macro-group
- TABLE 4.10: Individual and mean correlations between PT and SRQ results, per Macro-group - TABLE 4.11: TECO results and variation coefficients per University and Gender, highlighted
according to the significance of the distance versus the ITA12 mean (see section 4.5) - TABLE 4.12: PT and SRQ results, variation coefficients, mean correlation between them and
significance of the difference between them, per University (see section 4.5) - TABLE 4.13: Individual and mean correlations between PT and SRQ results, per University
(see section 4.5) - TABLE 4.14: TECO results and variation coefficients per Geographic Area and Gender,
highlighted according to the significance of the distance versus the ITA12 mean (see section 4.5)
- TABLE 4.15: PT and SRQ results, variation coefficients, mean correlation between them and significance of the difference between them, per Geographic Area
- TABLE 4.16: Individual and mean correlations between PT and SRQ results, per Geographic Area
Appendix to Part 4: TECO results and individual and mean correlations between PT and SRQ, per Disciplinary Group, Macro-group, University, Geographic Area and Gender
- TABLE A-4.1: TECO, PT and SRQ results per Disciplinary group, broken down by Gender - TABLE A-4.2: Means and variation coefficients on TECO and its components’ results within
Disciplinary Groups, and variances between Disciplinary Groups of mean TECO and its components’ results
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- TABLE A-4.3: Means and variation coefficients on TECO and its components’ results within Disciplinary Groups, and variances between Disciplinary Groups of mean TECO and its components’ results
- TABLE A-4.4: Means and variation coefficients on TECO and its components’ results within Disciplinary Groups, and variances between Disciplinary Groups of mean TECO and its components’ results
- TABLE A-4.5: Means and variation coefficients on TECO and its components’ results within Disciplinary Groups, and variances between Disciplinary Groups of mean TECO and its components’ results
- TABLE A-4.6: PT, SRQ and TECO variation coefficients, per Disciplinary Group - TABLE A-4.7: PT and SRQ results and variation coefficients, per Disciplinary Group and Macro-
group - TABLE A-4.8: PT and SRQ results and variation coefficients, per University and Geographic
Area
Part 5: Top, high and low performers and ‘super bonus’ per Disciplinary Group, Macro-group, University, Geographic Area and Gender
- TABLE 5.1: Top, high and low performers per Disciplinary Group, broken down by Gender (see section 4.6)
- TABLE 5.2: Top, high e low performers per Macro-group, broken down by Gender - TABLE 5.3: Top, high and low performers per University, broken down by Gender - TABLE 5.4: Top, high and low performers per Geographic Area, broken down by Gender - TABLE 5.5: Granting of the ‘super bonus’ to Disciplinary Groups, broken down by Gender (see
section 4.6) - TABLE 5.6: Percentage of students scoring higher than the relevant ITA12 median in each
Disciplinary Group, broken down by Gender - TABLE 5.7: Granting of the ‘super bonus’ to Macro-groups, broken down by Gender - TABLE 5.8: Granting of the ‘super bonus’ to Universities, broken down by Gender - TABLE 5.9: Granting of the ‘super bonus’ to Geographic Areas, broken down by Gender
Appendix to Part 5: Top, high and low performers and ‘super bonus’ per Disciplinary Group, Macro-group, University, Geographic Area and Gender
- TABLE A-5.1: Top, high and low performers per Disciplinary Group, Macro-group, University and Geographic Area, broken down by Gender
- TABLE A-5.2: Number of students scoring higher than the relevant ITA12 median per Disciplinary Group, Macro-group, University and Geographic Area, broken down by Gender
Part 6: Simple correlations between TECO and the main contextual variables - TABLE 6.1: Contextual variables and TECO results per Geographic Area and ITA12 (see
section 4.7) Part 6.1: TECO, family data variables, some social data variables and supports for studying
- TABLE 6.1.1: PT, SRQ and TECO results, mean diploma grade (VMD) and mean grade in university exams sat so far (VME), by number of household members (aggregated) (see section 4.9)
- TABLE 6.1.2: PT, SRQ and TECO results, mean diploma grade (VMD) and mean grade in university exams sat so far (VME), by number of siblings in university or other higher education studies (aggregated) (see section 4.9)
- TABLE 6.1.3: PT, SRQ and TECO results, mean diploma grade (VMD) and mean grade in university exams sat so far (VME), by language spoken at home
- TABLE 6.1.4: PT, SRQ and TECO results, mean diploma grade (VMD) and mean grade in university exams sat so far (VME), by citizenship
- TABLE 6.1.5: PT, SRQ and TECO results, by off-site condition (see section 4.9) - TABLE 6.1.6: PT, SRQ and TECO results, by residence in the same region as the university or
elsewhere - TABLE 6.1.7: PT, SRQ and TECO results, by citizenship, language spoken at home and residence
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- TABLE 6.1.8: PT, SRQ and TECO results, by mean travel time between habitual residence and university (see section 4.9)
- TABLE 6.1.9: PT, SRQ and TECO results, mean diploma grade (VMD) and mean grade in university exams sat so far (VME), by number of technological devices used by students (aggregated) (see section 4.9)
- TABLE 6.1.10: PT, SRQ and TECO results, mean diploma grade (VMD) and mean grade in university exams sat so far (VME), by number of trips outside the region per year (aggregated) (see section 4.9)
- TABLE 6.1.11: PT, SRQ and TECO results, mean diploma grade (VMD) and mean grade in university exams sat so far (VME), by number of trips outside the region per year (aggregated) (see section 4.9)
- TABLE 6.1.12: PT, SRQ and TECO results, by parents’ profession and study qualifications: a synthesis (see section 4.9)
- TABLE 6.1.12 bis: Note on Table 6.1.12 - TABLE 6.1.13: Mean diploma grade (VMD) and mean grade in university exams sat so far
(VME), by parent’s profession and study qualification - TABLE 6.1.14: PT, SRQ and TECO results, by parents’ type of employment contract
(aggregated) - TABLE 6.1.15: PT, SRQ and TECO results, by working student condition (see section 4.10) - TABLE 6.1.16: PT, SRQ and TECO results, by scholarship holder status (see section 4.10) - TABLE 6.1.17: PT, SRQ and TECO results, by use of student residence status (see section 4.10) - TABLE 6.1.18: PT, SRQ and TECO results, by entitlement to meal vouchers status (see section
4.10) - TABLE 6.1.19: PT, SRQ and TECO results, by student collaboration contract status (see section
4.10) - TABLE 6.1.20: PT, SRQ and TECO results, by working student condition and entitlement to
different types of supports for studying (aggregated) Appendix to Part 6.1: TECO, family data variables, some social data variables and supports for studying - TABLE A-6.1.1: PT, SRQ and TECO results, by number of household members - TABLE A-6.1.2: PT and SRQ results, by number of household members - TABLE A-6.1.3: PT, SRQ and TECO results, by number of siblings in university or other higher
education studies - TABLE A-6.1.4: PT and SRQ results, by number of siblings in university or other higher
education studies - TABLE A-6.1.5: PT, SRQ and TECO results, by number of technological devices used by students - TABLE A-6.1.6: PT and SRQ results, by number of technological devices used by students - TABLE A-6.1.7: Mean diploma grade (VMD) and mean grade in university exams sat so far
(VME), by number of technological devices used by students - TABLE A-6.1.8: PT, SRQ and TECO results, by number of trips outside the region per year - TABLE A-6.1.9: Mean diploma grade (VMD) and mean grade in university exams sat so far
(VME), by number of trips outside the region per year - TABLE A-6.1.10: PT, SRQ and TECO results, by number of trips abroad per year - TABLE A-6.1.11: Mean diploma grade (VMD) and mean grade in university exams sat so far
(VME), by number of trips abroad per year - TABLE A-6.1.12: PT, SRQ and TECO results, by father’s type of employment contract - TABLE A-6.1.13: PT, SRQ and TECO results, by mother’s type of employment contract - TABLE A-6.1.14: PT, SRQ and TECO results, by working student status - TABLE A-6.1.15: PT, SRQ and TECO results, by scholarship holder status - TABLE A-6.1.16: PT, SRQ and TECO results, by use of student residence status - TABLE A-6.1.17: PT, SRQ and TECO results, by entitlement to meal vouchers - TABLE A-6.1.18: TECO, PT and SRQ results, by student collaboration contract status - TABLE A-6.1.19: Percentage of working students among those who pre-registered, per
Disciplinary group, Macro-group, University and Geographic Area, broken down by Gender
120
Part 6.2: TECO and individual merit - TABLE 6.2.1: TECO result per Disciplinary Group and type of course followed (with admission
test or not) (see section 4.10) - TABLE 6.2.2: PT, SRQ and TECO results per Macro-group and type of course followed (with
admission test or not) - TABLE 6.2.3: TECO result per University and type of course followed (with admission test or
not) - TABLE 6.2.4: PT, SRQ and TECO results per Geographic Area and type of course followed (with
admission test or not) - TABLE 6.2.5: PT, SRQ and TECO results, mean diploma grade (VMD) and mean grade in
university exams sat so far (VME), by type of high school - TABLE 6.2.6: PT, SRQ and TECO results and mean diploma grade (VMD), per Disciplinary
Group - TABLE 6.2.7: PT, SRQ and TECO results and mean grade in university exams sat so far (VME),
per Disciplinary Group - TABLE 6.2.8: Tested students’ mean diploma grade (VMD) and mean grade in university exams
sat so far (VME), per Disciplinary Group - TABLE 6.2.9: PT, SRQ and TECO results and mean diploma grade (VMD), per Macro-group - TABLE 6.2.10: PT, SRQ and TECO results and mean grade in university exams sat so far (VME),
per Macro-group - TABLE 6.2.11: PT, SRQ and TECO results and mean diploma grade (VMD), per University - TABLE 6.2.12: PT, SRQ and TECO results and mean grade in university exams sat so far (VME),
per University - TABLE 6.2.13: Mean diploma grade (VMD) and mean grade in university exams sat so far
(VME), per University - TABLE 6.2.14: TECO, PT and SRQ results and mean diploma grade (VMD), per Geographic Area - TABLE 6.2.15: TECO, PT and SRQ results and mean grade in university exams sat so far (VME),
per Geographic Area - TABLE 6.2.16: PT, SRQ and TECO results, mean diploma grade (VMD) and mean grade in
university exams sat so far (VME), by number of other languages known - TABLE 6.2.17: PT, SRQ and TECO results, mean diploma grade (VMD) and mean grade in
university exams sat so far (VME), by number of courses taught in foreign language followed in Italy
- TABLE 6.2.18: PT, SRQ and TECO results, mean diploma grade (VMD) and mean grade in university exams sat so far (VME), by number of courses followed abroad
- TABLE 6.2.19: PT, SRQ and TECO results, mean diploma grade (VMD) and mean grade in university exams sat so far (VME), by number of months in an Erasmus program abroad
- TABLE 6.2.20: Note on preceding Table 6.2.19 Appendix to Part 6.2: TECO and individual merit - TABLE A-6.2.1: PT, SRQ and TECO results per Disciplinary Group and type of course followed
(with admission test or not) - TABLE A-6.2.2: Percentage of eligible students enrolled in courses with a national or local
admission test per Disciplinary Group, broken down by University - TABLE A-6.2.3: PT, SRQ and TECO results per Macro-group and type of course followed (with
admission test or not) - TABLE A-6.2.4: Percentage of eligible students enrolled in courses with a national or local
admission test per Macro-group, broken down by University - TABLE A-6.2.5: PT, SRQ and TECO results per University and type of course followed (with
admission test or not) - TABLE A-6.2.6: PT, SRQ and TECO results per Geographic Area and type of course followed
(with admission test or not) - TABLE A-6.2.7: PT result per Disciplinary Group and type of course followed (with admission
test or not) - TABLE A-6.2.8: SRQ result per Disciplinary Group and type of course followed (with admission
test or not)
121
- TABLE A-6.2.9: PT result per University and type of course followed (with admission test or not)
- TABLE A-6.2.10: SRQ result per University and type of course followed (with admission test or not)
- TABLE A-6.2.11: PT, SRQ and TECO results, per type of high school - TABLE A-6.2.12: Mean diploma grade (VMD) and mean grade in university exams sat so far
(VME), per type of high school - TABLE A-6.2.13: PT, SRQ and TECO results, average diploma grade (VMD) and average grade
in university exams sat so far (VME), per Disciplinary Group, Macro-group, University and Geographic Area
- TABLE A-6.2.14: PT, SRQ and TECO results, mean diploma grade (VMD) and mean grade in university exams sat so far (VME), by number of other languages known
- TABLE A-6.2.15: PT and SRQ results, mean diploma grade (VMD) and mean grade in university exams sat so far (VME), by number of other languages known
- TABLE A-6.2.16: PT, SRQ and TECO results, mean diploma grade (VMD) and mean grade in university exams sat so far (VME), by number of courses taught in foreign language followed in Italy
- TABLE A-6.2.17: PT, SRQ and TECO results, mean diploma grade (VMD) and mean grade in university exams sat so far (VME), by number of courses taught in foreign language followed in Italy
- TABLE A-6.2.18: PT, SRQ and TECO results, mean diploma grade (VMD) and mean grade in university exams sat so far (VME), by number of courses followed abroad
- TABLE A-6.2.19: PT and SRQ results, mean diploma grade (VMD) and mean grade in university exams sat so far (VME), by number of courses followed abroad
- TABLE A-6.2.20: PT, SRQ and TECO results, mean diploma grade (VMD) and mean grade in university exams sat so far (VME), by number of months in an Erasmus program abroad
- TABLE A-6.2.21: PT and SRQ results, mean diploma grade (VMD) and mean grade in university exams sat so far (VME), by number of months in an Erasmus program abroad
Part 6.3: TECO, externalities of merit with respect to academic and student environment - TABLE 6.3.1: TECO, PT and SRQ results and average VQR 2004-2010 grade (measured by R12)
of teachers actively involved in teaching courses, per Disciplinary Group (see section 4.14) - TABLE 6.3.2: TECO, PT and SRQ results and mean VQR 2004-2010 grade (measured by R12) of
teachers actively involved in teaching courses, per Macro-group - TABLE 6.3.3: TECO, PT and SRQ results and mean VQR 2004-2010 grade (measured by R12) of
teachers actively involved in teaching courses, per University (see section 4.14) - TABLE 6.3.4: TECO, PT and SRQ results and mean VQR 2004-2010 grade (measured by R12) of
teachers actively involved in teaching courses, per Geographic Area (see section 4.14) - TABLE 6.3.5: Student environment quality, as shown by the mean diploma grade (VMD) and
mean grade in university exams sat so far (VME) for all eligible and ineligible students, per Disciplinary Group
- TABLE 6.3.6: TECO, PT, SRQ results and Merit Index (M) calculated for all eligible and ineligible students, per Disciplinary Group (see section 4.14)
- TABLE 6.3.7: Student environment quality, as shown by the average diploma grade (VMD) and average grade in university exams sat so far (VME) for all eligible and ineligible students, per Macro-group
- TABLE 6.3.8: TECO, PT, SRQ results and Merit Index (M) calculated for all eligible and ineligible students, per Macro-group
- TABLE 6.3.9: Student environment quality, as shown by the average diploma grade (VMD) and average grade in university exams sat so far (VME) for all eligible and ineligible students, per University
- TABLE 6.3.10: TECO, PT, SRQ results and Merit Index (M) calculated for all eligible and ineligible students, per University (see section 4.14)
- TABLE 6.3.11: Student environment quality, as shown by the average diploma grade (VMD) and average grade in university exams sat so far (VME) for all eligible and ineligible students, per Geographic Area
122
- TABLE 6.3.12: TECO, PT, SRQ results and Merit Index (M) calculated for all eligible and ineligible students, per Geographic Area
- TABLE 6.3.13: Mean diploma grade (VMD) and mean grade in university exams sat so far (VME) for all eligible and ineligible students, per Disciplinary Group, Macro-group, University and Geographic Area
Appendix to Part 6.3: TECO, externalities of merit with respect to academic and student environment - TABLE A-6.3.1: Mean VQR 2004-2010 grade (VM) and mean teaching grade (R12) obtained by
active teachers, per Disciplinary Group, Macro-group, University and Geographic Area - TABLE A-6.3.2: TECO, PT and SRQ results and mean VQR 2004-2010 grades of active teachers
(VM), per Disciplinary Group - TABLE A-6.3.3: Mean VQR 2004-2010 grade (VM) and mean teaching grade (R12) obtained by
active teachers, per Disciplinary Group - TABLE A-6.3.4: TECO, PT and SRQ results and mean VQR 2004-2010 grades of active teachers
(VM), per Macro-group - TABLE A-6.3.5: Mean VQR 2004-2010 grade (VM) and mean teaching grade (R12) obtained by
active teachers, per Macro-group - TABLE A-6.3.6: TECO, PT and SRQ results and mean VQR 2004-2010 grades of active teachers
(VM), per University - TABLE A-6.3.7: Mean VQR 2004-2010 grade (VM) and mean teaching grade (R12) obtained by
active teachers, per University - TABLE A-6.3.8: TECO, PT and SRQ results and mean VQR 2004-2010 grades of active teachers
(VM), per Geographic Area - TABLE A-6.3.9: Mean VQR 2004-2010 grade (VM) and mean teaching grade (R12) obtained by
active teachers, per Geographic Area
Part 7: Multiple correlations between TECO and some contextual variables - TABLE 7.1: TECO results in some linear regression models (see section 4.7) - TABLE 7.2: PT results in some linear regression models - TABLE 7.3: SRQ results in some linear regression models
Part 8: Self-selection Analysis - TABLE 8.1: Mean diploma grade (VMD) and mean grade in university exams sat so far (VME),
for tested students, eligible students who did not come to sit the test, and ineligible students (see section 4.13)
- TABLE 8.2: Mean diploma grade (VMD) and mean grade in university exams sat so far (VME), for eligible students who did or did not come to sit the test, per Disciplinary Group (see section 4.13)
- TABLE 8.3: Mean diploma grade (VMD) and mean grade in university exams sat so far (VME), for eligible students who did or did not come to sit the test, per Macro-group
- TABLE 8.4: Mean diploma grade (VMD) for eligible students who did or did not come to sit the test, per University and Geographic Area, broken down by Gender (see section 4.13)
- TABLE 8.5: Mean grade in university exams sat so far (VME) for eligible students who did or did not come to sit the test, per University and Geographic Area, broken down by Gender (see section 4.13)
- TABLE 8.6: Mean diploma grade (VMD) and mean grade in university exams sat so far (VME), for eligible students who did or did not come to sit the test, per University and Geographic Area (see section 4.13)
- TABLE 8.7: Mean diploma grade (VMD) and mean grade in university exams sat so far (VME), for eligible students who did or did not come to sit the test, broken down by quartiles (see section 4.13)
- TABLE 8.8: Mean diploma grade (VMD) and mean grade in university exams sat so far (VME), for eligible students who did or did not come to sit the test, broken down by quartiles within each Macro-group
123
- TABLE 8.9: Mean diploma grade (VMD) and mean grade in university exams sat so far (VME), for eligible students who did or did not come to sit the test, broken down by quartiles within each University
- TABLE 8.10: Mean age, percentage non-Italian citizenship (%citt no ita), percentage living outside the Region (% dist3) and percentage females, among tested students and eligible students who did not sit the test, per Disciplinary Group (see section 4.13)
- TABLE 8.11: Mean age, percentage non-Italian citizenship (%citt no ita), percentage living outside the Region (% dist3) and percentage females, among tested students and eligible students who did not sit the test, per Macro-group
- TABLE 8.12: Mean age, percentage non-Italian citizenship (%citt no ita), percentage living outside the Region (% dist3) and percentage females, among tested students and eligible students who did not sit the test, per University
- TABLE 8.13: Mean age, percentage non-Italian citizenship (%citt no ita), percentage living outside the Region (% dist3) and percentage females, among tested students and eligible students who did not sit the test, per Geographic Area
- TABLE 8.14: Percentage of eligible students who did/did not sit the test, per Geographic Area of the University, per Disciplinary Group and per Macro-group
- TABLE 8.15: Percentage of eligible students who did/did not sit the test broken down by type of secondary school, per Disciplinary group, Macro-group, University and Geographic Area
- TABLE 8.16: Percentage of married students, working students, students who do not speak Italian at home (%non ita) and students who do not know any language other than Italian (% no other lang) among tested students and pre-registered students who did not sit the test, per Disciplinary Group (see section 4.13)
- TABLE 8.17: Percentage of married students, working students, students who do not speak Italian at home (%non ita) and students who do not know any language other than Italian (% no other language) among tested students and pre-registered students who did not sit the test, per Macro-group
- TABLE 8.18: Percentage of married students, working students, students who do not speak Italian at home (%non ita) and students who do not know any language other than Italian (% no other language) among tested students and pre-registered students who did not sit the test, per University
- TABLE 8.19: Percentage of married students, working students, students who do not speak Italian at home (%non ita) and students who do not know any language other than Italian (% no other language) among tested students and pre-registered students who did not sit the test, per Geographic Area
Appendix to Part 8: Self-selection Analysis - TABLE A-8.1: Mean diploma grade (VMD) for eligible students who did/did not come to sit the
test, broken down by quartiles within each Disciplinary Group - TABLE A-8.2: Mean diploma grade (VMD) for eligible students who did/did not come to sit the
test, broken down by quartiles (Q1 and Q2) within each Disciplinary Group - TABLE A-8.3: Mean diploma grade (VMD) for eligible students who did/did not come to sit the
test, broken down by quartiles (Q3 and Q4) within each Disciplinary Group - TABLE A-8.4: Mean grade in university exams sat so far (VME), for eligible students who
did/did not come to sit the test, broken down by quartiles within each Disciplinary Group - TABLE A-8.5: Mean grade in university exams sat so far (VME), for eligible students who
did/did not come to sit the test, broken down by quartiles (Q1 and Q2) within each Disciplinary Group
- TABLE A-8.6: Mean grade in university exams sat so far (VME), for eligible students who did/did not come to sit the test, broken down by quartiles (Q3 and Q4) within each Disciplinary Group
- TABLE A-8.7: Mean diploma grade (VMD) for eligible students who did/did not come to sit the test, broken down by quartiles within each Macro-group
- TABLE A-8.8: Mean grade in university exams sat so far (VME), for eligible students who did/did not come to sit the test, broken down by quartiles within each Macro-group
124
- TABLE A-8.9: Mean diploma grade (VMD) for eligible students who did/did not come to sit the test, broken down by quartiles within each University
- TABLE A-8.10: Mean grade in university exams sat so far (VME) for eligible students who did/did not come to sit the test, broken down by quartiles within each University
- TABLE A-8.11: Participation Index and quality of active teachers (VQR 2004-2010 grade, measured by R12), per Disciplinary Group, Macro-group, University and Geographic Area
- TABLE A-8.12: Participation Index and quality of active teachers (VQR 2004-2010 grade, measured by the mean grade VM), per Disciplinary Group, Macro-group, University and Geographic Area
- TABLE A-8.13: Participation Index and Merit Index (M) calculated for all eligible students, per Disciplinary Group, Macro-group, University and Geographic Area
- TABLE A-8.14: Mean grade in university exams sat so far (VME) for pre-registered students, per Disciplinary Group, Macro-group, University and Geographic Area, broken down by Gender
Part 9: Universities’ and students’ self-assessments - TABLE 9.1: Synthesis of data from Universities’ Self-Assessment Forms concerning low
regularity index (R) for their courses (see section 4.1) - TABLE 9.2: Analysis of the correlation between TECO results (effective learning outcomes) and
the formulation of expectations in the ‘SUA’ forms for academic year 2012-2013 (expected learning outcomes), per class in the 12 Universities participating in the TECO pilot test (see section 4.14)
- TABLE 9.3: Overview of the correlation between TECO results and formulation of expectations in the ‘SUA’ forms for academic year 2012-2013 (see section 4.14)
- TABLE 9.4: Frequency distribution of TECO scores broken down by students’ self-assessment of adequacy for TECO of the competences acquired at University
- TABLE 9.5: Students’ self-assessment of adequacy of the competences acquired at University and their TECO results, per Disciplinary Group (see section 4.11)
- TABLE 9.6: Students’ self-assessment of adequacy of the competences acquired at University and their TECO results, per Macro-group
- TABLE 9.7: Students’ self-assessment of adequacy of the competences acquired at University and their TECO results, per University (see section 4.11)
- TABLE 9.8: Students’ self-assessment of adequacy of the competences acquired at University and their TECO results, per Geographic Area (see section 4.11)
- TABLE 9.9: Distributions by TECO quartiles based on students’ self-assessment of adequacy of the competences acquired at University (see section 4.11)
- TABLE 9.10: Proportion of negative and positive answers to the question on adequacy of competences in the quartiles of distribution of TECO results, per Macro-group
- TABLE 9.11: Distributions by TECO quartiles based on students’ self-assessment of adequacy of the competences acquired at University, per Macro-group
- TABLE 9.12: Proportion of negative and positive answers to the question on adequacy of competences in the quartiles of distribution of TECO results, per Geographic Area
- TABLE 9.13: Distributions by TECO quartiles based on students’ self-assessment of adequacy of the competences acquired at University, per Geographic Area
- TABLE 9.14: Students’ self-assessment of adequacy of the competences acquired at University and their TECO results, attendance in courses with an admission test, and these students’ TECO results, per Disciplinary Group and per Geographic Area
- TABLE 9.15: Attendance reported as regular by tested students and these students’ TECO results, per Disciplinary Group
- TABLE 9.16: Attendance reported as regular by tested students and these students’ TECO results, per Macro-group
- TABLE 9.17: Attendance reported as regular by tested students and these students’ TECO results, per University
- TABLE 9.18: Attendance reported as regular by tested students and these students’ TECO results, per Geographic Area
125
7. Other Tables
Macro-groups Acronym
Health and Welfare SAN
(Exact) Sciences SC
Social Sciences SOC
Humanities and Arts H
The definition of the Macro-groups is by MIUR (website: http://offf.miur.it/pubblico.php/ricerca/aree_e_classi/p/miur#A1 )
University Acronym
University of Eastern Piedmont PO
University of Milan MI
University of Padua PD
University of Udine UD
University of Udine BO
University of Florence FI
University of Rome "La Sapienza" RM1
University of Rome “Tor Vergata" RM2
University of Naples "Federico II" NA
University of Salento LE
University of Messina ME
University of Cagliari CA
All 12 universities participating in the pilot test ITA12
Geographic Areas Universities
NORTH PO + MI + PD + UD
CENTRE BO + FI + RM1 + RM2
SOUTH NA + LE + ME + CA
CENTRE+NORTH PO + MI + PD + UD + BO + FI + RM1 + RM2
CENTRE-NORTH PO + MI + PD + UD + BO + FI
CENTRE-SOUTH RM1 + RM2 + NA + LE + ME + CA
Students (I) (enrolled in the 3rd or 4th year of a three-year course or single-cycle master course, excluding healthcare professions)
Type Acronym
Eligible students (L) (I who have acquired all basic and characteristic study credits – if enrolled in a three-year course, or at least 120 basic and
characteristic study credits – if enrolled in a single-cycle master course)
Pre-registered and came to sit the test PRV
Pre-registered and came to sit the test, but the test was cancelled
PRVA
Pre-registered but did not come to sit the test PRN
Not pre-registered, did not come to sit the test N
Ineligible Ineligible NN
INDICES
Definition Symbol
Students enrolled in the 3rd or 4th year of a three-year first-cycle course or single-cycle master course, excluding courses for the healthcare professions.
I
Graduating students (also called eligible students) L
Students who sat the test T
Regularity index = (L/I)*100 R
Eligible students participation index = (T/L)*100 P
Enrolled students participation index = (T/I)*100 Q
Pre-registration index = (Pre-registered students/L)*100 PR
Pre-registered students participation index = (T/Pre-registered students)*100 TP
List of acronyms used in tables
Grade Acronym
Mean diploma grade VMD
Mean grade in university exams sat so far VME
List of acronyms used in tables : aggregation of classes in Disciplinary Groups (Part 1)
Disciplinary Group AcronymClass (DM 270/04 + DM
509/99 and further aggregations)
Class NameMacro-group
T (Class)T
(Disciplinary Group)
Cultural Heritage Group (**)
cultL-1 + 13 Cultural Heritage H 136
142L-43 Diagnostics for the conservation of cultural heritage SC 6
Arts Group lett L-10 + LM-14(*) Arts H 190 190
Languages Group lingL-11 Modern languages and cultures H 146
231L-12 Linguistic mediation H 85
Biology Group bioL-13 Biology SC 135
256L-2 Biotechnology SC 83L-22 Physical education and sports SC 38
Law Group giuL-14 + 2 Science of legal services SOC 45
874LMG/01 + 31 Master’s degree in law SOC 829
Economics Group econL-18 + 17 Economics and business administration SOC 340
465L-33 Economics SOC 125
Territory Group terr
L-17 Architecture SC 67
391
L-21 Land-use, urban, landscape and environmental planning SC 44L-23 Construction science and technology SC 31
L-32 + 27 Science and technology for the environment and nature SC 55L-34 Geology SC 52L-4 Industrial design SC 63L-7 Civil and environmental engineering SC 79
Education Group form L-19 + LM-85(*) Education H 128 128Communication Group comun L-20 Communication science SOC 131 131Psychology Group psic L-24 Psychology and psychological technique SOC 191 191
Food and Agriculture Group
agr.alL-25 + 20(*) Agricultural and forestry science and technology SC 82
139L-26 Food science and technology SC 38L-38 + LM-86(*) Livestock rearing science and technology SC 19
Chemistry Group chim L-27 + 21 Chemistry and chemical technology SC 106 106
Pharmacy Group farmL-29 Pharmaceutical science and technology SAN 24
394LM-13 + 14/S Pharmacy and industrial pharmacy SAN 370
Fine Arts Group art L-3 Visual arts, music, performing arts and fashion H 64 64
Mathematics, Physics and Statistics Group
mat.fis.stat
L-30 Physics science and technology SC 102
388L-31 Computing science and technology SC 85
L-35 + 32 Mathematics SC 129L-41 Statistics SC 72
Political Science Group politL-16 Administration and organization science SOC 47
201L-36 + 15 Political science and international relations SOC 141L-37 Social sciences for cooperation, development and peace SOC 13
126
List of acronyms used in tables and appendices: aggregation of classes in Disciplinary Groups (Part 2)
Disciplinary Group AcronymClass (DM 270/04 + DM
509/99 and further aggregations)
Class NameMacro-group
T (Class)T (Disciplinary
Group)
Sociology Group socL-39 Social services SOC 34
79L-40 Sociology SOC 45
History Group sto L-42 History H 57 57
Geography Group (**) geoL-15 Tourism SOC 38
53L-6 + 30 Geography H 15
Philosophy Group filo L-5 Philosophy H 108 108
Engineering Group ingL-8 IT Engineering SC 192
463L-9 Industrial Engineering SC 271
Architecture Group arch LM-4 C.U. Architecture and construction engineering/architecture (5-year course) SC 272 272Medicine Group med LM-41 + 46/S Medicine and surgery SAN 393 393Veterinary Science Group
vet LM-42 Veterinary medicine SAN 93 93
Dentistry Group odon LM-46 Dentistry and Dental Prosthetics SAN 44 44Defence Group dif DS/1 Defence and security SOC 0 0As of the next slide, and then in the Appendix, we always refer to the Disciplinary Groups of the 12 universities participating in the pilot test, unless explicitly stated otherwise.The aggregation of classes in Disciplinary Groups is conditioned by the presence of ‘interclasses’: students have the possibility of following a course and then deciding in which class to earn their qualification. This happens for these aggregations: L-15 with L-6; L-26 with L-38; L-8 with L-9; L-12 with L-11; L-18 with L-33; L-25 with L-26; L-39 with L-40. Consequently, we have determined all other aggregations so as to put together classes that are sufficiently similar as regards curriculum and mean class grades, and that do not have sufficient numbers for reporting results in a significant way without aggregation. In the 4 cases marked with (*), the aggregations are due to the following reasons:- Class 20 - Agricultural, agro-food and forestry science was divided into L-25 and L-26 following DM 270/04, and was hence arbitrarily aggregated with Class L-25 -Agricultural and forestry science and technology, in order to have a univocal conversion.- Class LM-14 - Modern philology, attended by a single student, was aggregated to Class L-10 - Arts, to make the analysis easier.- Class LM-85 - Pedagogy, attended by a single student, was aggregated to Class L-19 - Education, to make the analysis easier.- Class LM-86 - Livestock science and technology, attended by a single student, was aggregated to Class L-38 - Livestock rearing science and technology, to make the analysis easier.CLASS (DM 270/04 + DM 509/99 and further aggregations): class code under the new regulation (DM 270/2004) + class code under the old regulation (DM509/99) if applicable.The dark grey highlighting indicates classes and Disciplinary Groups for which there is a national admission test.The light grey highlighting indicates classes and Disciplinary groups for which there is a local admission test for more than 50% of the eligible students in the 12 universities participating in the pilot test (ITA12). The Territory Group is considered to be in the same situation: 49.94% of students in the ITA12 are enrolled in courses with a local admission test and 21.39% of the same students are enrolled in courses with a national admission test. CLASS NAME: full name of the class as listed in DM270/2004MACRO-GROUP: Code indicating the MIUR Macro-group to which the class belongs. The two Groups indicated with (**) comprise within them classes that belong to different MIUR Macro-groups. Given that the Geography Group is conditioned by an existing interclass, and thus vetted by MIUR, we have taken the liberty of doing the same for the Cultural Heritage Group. To avoid any confusion when analyzing the data, whenever a reference is made to MIUR Macro-Groups these are to be understood as aggregations of classes (not of groups). For a complete list, see: http://offf.cineca.it/pubblico.php/ricerca/aree_e_classi/p/cercauniv#A1 in the MIUR website.
TABLE 3b: Variables related to the 12 participating universities (Part 1)
Source Acronym
ANS: National Student Register - MIUR
TEACHERS: Teachers Database - MIUR
Unless otherwise stated, ANS refers to academic year 2011/2012 and calendar year
2012.
Variable Name Source DescriptionUNIVERSITY NAME (CITY) ANS Name of the university. Identifies the city.COURSE ANS Name of the course of studyCLASS (270+509) ANS Description of the class (DM 509/99 and 279/04)
TYPE OF DEGREE (*)DM 509/99DM 270/04
Identification of the type of degree
EXPECTED NUMBER OF YEARS ANS Expected number of years of study to earn the qualificationEXPECTED CREDITS ANS Expected number of study credits to earn the qualificationAVERAGE YEARS TAKEN TO EARN DEGREE ANS Average number of years taken to earn the qualification
POPULATION VARIANCE ANSPopulation variance with respect to the average number of years taken to earn the qualification
SAMPLE VARIANCE ANS Sample variance with respect to the average number of years taken to earn the qualificationENROLLED STUDENTS 2011/2012 ANS Number of students enrolled in the course for academic year 2011/2012
TEACHER/STUDENT RATIO FOR HOMOGENOUS FIELDS OF STUDY (**)
TEACHERS (2010/2011),
ANS ("2010/2011)Teacher/student ratio for homogenous fields of study (Table 2, Attachment C, DM 17/2010)
ITALIANS ANSNumber of students of Italian citizenship registered in the course for academic year 2011/2012
FOREIGNERS ANSNumber of students of foreign citizenship registered in the course for academic year 2011/2012
ACQUIRED CREDITS ANSNumber of credits acquired by students enrolled in the course for academic year 2011/2012 in calendar year 2012
AVERAGE CREDITS PER STUDENT ANS Ratio between ACQUIRED CREDITS and ENROLLED STUDENTS 2011/2012ENROLLED STUDENTS 2010/2011 ANS Students enrolled in the course for academic year 2010/2011STUDENTS ENROLLED IN 2ND YEAR 2011/2012 ANS Number of students enrolled in the 2nd year of the course for academic year 2011/2012STUDENTS ENROLLED IN 2ND YEAR 2011/2012 WITH ALL 1ST YEAR CREDITS ACQUIRED
ANS (2010/2011, 2011-2012)
Number of students enrolled in the 2nd year of the course for academic year 2011/2012 who have acquired all 1st year credits
% STUDENTS ENROLLED IN 2ND YEAR 2011/2012 WITH ALL 1ST YEAR CREDITS ACQUIRED
ANS (2010/2011, 2011-2012)
Percentage of students enrolled in the 2nd year of the course for academic year 2011/2012 who have acquired all 1st year credits, versus total students enrolled in the 2nd year for academic year 2011/2012 (who were enrolled in 2010)
‘REGULAR’ GRADUATES ANSNumber of students enrolled in the course for academic year 2011/2012 who earned their qualification in calendar year 2012 after studying for a duration less than or equal to the expected duration
TOTAL GRADUATES ANS Total number of graduates in calendar year 2012REGULARITY RATE ANS Ratio of ‘REGULAR’ GRADUATES to TOTAL GRADUATES
127
TABLE 3b: Variables related to the 12 participating universities (Part 2)
Source Acronym
ANS: National Student Register - MIUR
TEACHERS: Teachers Database - MIUR
Unless otherwise stated, ANS refers to academic year 2011/2012 and calendar year
2012.
Variable Name Source Description
ENROLLED STUDENTS 2010/2011ANS (2010/2011, 2011-
2012)Number of students enrolled in the course for academic year 2010/2011
GRADUATES 2010/2011ANS (2010/2011, 2011-
2012)Number of students who graduated from the course in academic year 2010/2011
COURSE DROP-OUT RATE (***)ANS (2010/2011, 2011-
2012)Number of students who dropped out of the course in academic year 2011/2012
WORKING STUDENTS (****) ANS Number of working students enrolled in the course for academic year 2011/2012PROPORTION OF WORKING STUDENTS ANS Ratio between WORKING STUDENTS and ENROLLED STUDENTS 2011/2012
IRREGULAR STUDENTS ANSNumber of students enrolled in the course for academic year 2011/2012 since a number of years higher than the expected duration
PROPORTION OF IRREGULAR STUDENTS ANS Ratio between IRREGULAR STUDENTS and ENROLLED STUDENTS 2011/2012
INACTIVE STUDENTS ANSNumber of students considered inactive (have not acquired at least 5 credits in calendar year 2012)
CREDITS ACQUIRED ABROAD (*****) ANS Number of credits acquired abroad in calendar year 2012ENROLLED STUDENTS WITH AT LEAST 15 CREDITS ACQUIRED ABROAD
ANSNumber of students who have acquired more than 15 credits abroad in the course of their career
PART-TIME STUDENTS (******) ANS Number of part-time students (less than 50 credits per year)PROPORTION OF PART-TIME STUDENTS ANS Ratio between PART-TIME STUDENTS and ENROLLED STUDENTS 2011/2012
DISCLAIMER: the reliability of indicators derived from the ANS system is highly dependent on the correctness of data transmitted directly from the universities to the system. Any consideration, study, evaluation and analysis on these data is subject to this disclaimer. All variables are grouped by course of study and by class, within the University.
(*): Law 509: 3-year degree (LT); Single Section (TU); Specialized degree (LS);Law post 509 not reformed: Degree under the old system (LV); Law 270: 3-year degree (MT); single-cycle master degree (LM); specialized master degree (MS).
(**) The value provided is the one used for the distribution of FFO 2012 funds in relation to indicator A1, index KA: “Ratio between the number of tenured teachers belonging to basic and characteristic scientific-disciplinary sectors and the the theoretical number of courses activated during academic year 2010/11 (degree courses and single-cycle master courses)”.
(***) Students enrolled in academic year 2010/2011 who appear to have dropped out from the course during academic year 2011/2012. Drop-out refers to a change of course or exit from the university without obtaining a degree.Drop-outs 2011/2012 = [Enrolled students 2010/2011] - [Enrolled students 2011/2012 (of which enrolled in 2010/2011)] – [Graduates 2010/2011 (of which enrolled in 2010/2011)].
(****) The definition of working student may not be homogenous across all universities. Moreover, the data are not individual; as for all variables in the matrix, they are provided by course of study and class within the University and obtained from ANS.(*****) Credits acquired abroad during calendar year 2012, in relation to the total number of students enrolled in academic year 2011/12, by course of study and class. By “acquired abroad” we mean credits earned in foreign universities or in internships abroad.(******) The concept of full-time student/part-time student may be understood differently across all universities. For the purposes of TECO, a student is considered to be part-time if he/she declares to be targeting < 50 study credits for a given academic year. All variables are grouped by course of study and by class.
TABLE 5: Students enrolled in the 3rd or 4th year of a three-year first-cycle course or single-cycle master course, including health care professions, and number within these of graduating students (*)
– All Italian universities – Academic Year 2010/2011, June-July 1011
Academic year 2010/2011
All Italian universities - Three-year first-cycle course
Enrolment year 3rd 4th 3rd + 4th All years (from 1st to 10th)
Total students 188676 123598 312274 1090615
Graduating students 40853 24073 64926 101200
% Graduating students 21.65 19.48 20.79 9.28
All Italian universities - Single-cycle master course
Enrolment year 3rd 4th 3rd + 4th All years (from 1st to 10th)
Total students 41393 40417 81810 309892
Graduating students 8978 14729 23707 78043
% Graduating students 21.69 36.44 28.98 25.18
All Italian universities - Total
Enrolment year 3rd 4th 3rd + 4th All years (from 1st to 10th)
Total students 230069 164015 394084 1400507
Graduating students 49831 38802 88633 179243
% Graduating students 21.66 23.66 22.49 12.80
(*) See TABLE 4
Source: National Student Register - MIUR (ANS)
128
TABLE 9: Indicators and results on PT, SRQ, TECO per Disciplinary Group, broken down by UniversityDisciplinary
GroupIndicat
orPO (*) MI PD UD BO FI RM1 RM2 NA LE ME CA ITA12
agr.al
I 981 514 332 427 338 1 611 35 3239L 100 97 11 9 107 0 34 3 361T 42 38 7 0 36 0 14 2 139R 10.19 18.87 3.31 2.11 31.66 0.00 5.56 8.57 11.15P 42.00 39.18 63.64 0.00 33.64 41.18 66.67 38.50Q 4.28 7.39 2.11 0.00 10.65 0.00 2.29 5.71 4.29PT 991.29 980.95 1007.43 1010.64 886.29 818.50 981.22
SRQ 990.93 983.26 977.71 1028.31 912.43 767.00 986.72TECO 991.19 982.11 992.57 1019.56 899.29 792.50 984.01
arch
I 166 453 522 1130 118 667 3056L 93 248 99 415 37 244 1136T 38 50 14 139 9 22 272R 56.02 54.75 18.97 36.73 31.36 36.58 37.17P 40.86 20.16 14.14 33.49 24.32 9.02 23.94Q 22.89 11.04 2.68 12.30 7.63 3.30 8.90PT 1041.61 1019.22 963.86 964.55 1025.67 997.18 989.99
SRQ 1095.76 1066.02 1027.79 1001.88 1024.33 913.73 1021.73
TECO1068.76
(**)1042.70(**
)995.79 983.29 1025.22 955.55 1005.94
art
I 180 43 784 274 477 194 19 1971L 21 9 105 34 207 36 0 412T 9 7 5 5 31 7 0 64R 11.67 20.93 13.39 12.41 43.40 18.56 0.00 20.90P 42.86 77.78 4.76 14.71 14.98 19.44 15.53Q 5.00 16.28 0.64 1.82 6.50 3.61 0.00 3.25PT 973.00 910.43 1069.40 1137.40 937.65 1026.86 975.30
SRQ 938.67 977.71 1033.60 1061.80 923.26 957.57 954.58
TECO 955.89 944.14 1051.80 1099.60930.58(**
)992.14 965.03
PT, SRQ, TECO: mean scores obtained for the PT module, the SRQ module, and the TECO test as a whole.Source: The 12 universities participating in the pilot test. For L, the source is the National Student Register (CINECA ANS)(*): For the University of Eastern Piedmont (PO), there is an anomalous presence of eligible students who sat the test despite the absence of registered students according to the CINECA database.(**): TECO result significantly different versus ITA12 (95% confidence interval)The yellow highlighting indicates groups that do not reach the threshold level of 30 tested students. For these groups, the significance of the difference in the TECO score versus ITA12 was therefore not tested.The dark grey highlighting indicates the Disciplinary Groups for which there is a national admission test. The light grey highlighting indicates the Disciplinary Groups for which there is a local admission test for more than 50% of the eligible students within the University.The very light grey highlighting indicates the Disciplinary Groups for which there is a local admission test for less than 50% of the eligible students within the University
TABLE 9: Indicators and results on PT, SRQ, TECO per Disciplinary Group, broken down by University
Disciplinary Group
Indicator
PO (*) MI PD UD BO FI RM1 RM2 NA LE ME CA ITA12
bio
I 124 762 349 96 789 561 407 841 1501 273 375 211 6289L 20 212 37 23 35 146 153 95 61 0 10 13 805T 15 88 22 17 0 30 45 5 22 0 7 5 256R 16.13 27.82 10.60 23.96 4.44 26.02 37.59 11.30 4.06 0.00 2.67 6.16 12.80P 75.00 41.51 59.46 73.91 0.00 20.55 29.41 5.26 36.07 70.00 38.46 31.80Q 12.10 11.55 6.30 17.71 0.00 5.35 11.06 0.59 1.47 0.00 1.87 2.37 4.07PT 1006.13 1001.16 1052.91 1011.88 981.30 1003.13 757.20 1006.64 891.00 1015.00 997.59
SRQ 982.27 1036.67 1118.09 1056.24 1075.97 943.29 837.20 990.41 897.29 879.40 1015.21TECO 994.33 1018.97 1085.45 1034.12 1028.60 973.20 797.40 998.50 894.29 947.20 1006.43
chim
I 38 235 106 207 84 282 109 175 63 66 1365L 5 41 16 15 17 91 12 80 0 1 278T 5 36 9 1 1 44 3 7 0 0 106R 13.16 17.45 15.09 7.25 20.24 32.27 11.01 45.71 0.00 1.52 20.37P 100.00 87.80 56.25 6.67 5.88 48.35 25.00 8.75 0.00 38.13Q 13.16 15.32 8.49 0.48 1.19 15.60 2.75 4.00 0.00 0.00 7.77PT 906.60 1021.97 957.78 1463.00 920.00 940.16 852.33 900.71 967.51
SRQ 963.60 1067.28 1086.78 978.00 1048.00 974.41 930.67 1108.00 1023.29TECO 935.20 1044.72 1022.33 1220.00 984.00 957.32 891.67 1004.29 995.45
comun
I 498 184 272 433 156 563 207 168 290 294 3065L 38 11 63 123 25 151 25 18 14 42 510T 20 3 36 12 4 41 2 1 2 10 131R 7.63 5.98 23.16 28.41 16.03 26.82 12.08 10.71 4.83 14.29 16.64P 52.63 27.27 57.14 9.76 16.00 27.15 8.00 5.56 14.29 23.81 25.69Q 4.02 1.63 13.24 2.77 2.56 7.28 0.97 0.60 0.69 3.40 4.27PT 1059.20 1078.67 961.72 908.92 1022.00 993.00 852.00 988.00 920.50 1028.80 989.09
SRQ 1012.65 954.00 952.31 995.25 907.50 955.32 1153.50 556.00 837.00 991.70 966.36TECO 1036.10 1016.67 957.08 952.08 964.75 974.29 1002.50 772.00 878.50 1010.20 977.81
PT, SRQ, TECO: mean scores obtained for the PT module, the SRQ module, and the TECO test as a whole.Source: The 12 universities participating in the pilot test. For L, the source is the National Student Register (CINECA ANS)(*): For the University of Eastern Piedmont (PO), there is an anomalous presence of eligible students who sat the test despite the absence of registered students according to the CINECA database.(**): TECO result significantly different versus ITA12 (95% confidence interval)The yellow highlighting indicates groups that do not reach the threshold level of 30 tested students. For these groups, the significance of the difference in the TECO score versus ITA12 was therefore not tested.The dark grey highlighting indicates the Disciplinary Groups for which there is a national admission test. The light grey highlighting indicates the Disciplinary Groups for which there is a local admission test for more than 50% of the eligible students within the University.The very light grey highlighting indicates the Disciplinary Groups for which there is a local admission test for less than 50% of the eligible students within the University
129
TABLE 9: Indicators and results on PT, SRQ, TECO per Disciplinary Group, broken down by UniversityDisciplinary
GroupIndicat
orPO (*) MI PD UD BO FI RM1 RM2 NA LE ME CA ITA12
cult
I 3 709 247 89 189 311 709 249 340 159 52 181 3238L 3 126 27 3 37 19 205 39 43 9 6 6 523T 2 37 5 3 2 10 50 3 21 3 1 5 142R 100 17.77 10.93 3.37 19.58 6.11 28.91 15.66 12.65 5.66 11.54 3.31 16.15P 66.67 29.37 18.52 100.00 5.41 52.63 24.39 7.69 48.84 33.33 16.67 83.33 27.15Q 66.67 5.22 2.02 3.37 1.06 3.22 7.05 1.20 6.18 1.89 1.92 2.76 4.39PT 1022.00 1006.43 1055.80 920.00 1293.50 1022.00 963.58 988.00 949.19 852.33 1056.00 1042.40 986.12
SRQ 942.50 1000.43 1019.60 1000.67 1047.50 1160.10 941.10 603.33 924.24 907.67 978.00 1005.80 969.73
TECO 982.50 1003.51 1037.80 960.67 1171.00 1091.00952.42(**
)795.67 936.71 880.00 1017.00 1024.20 977.99
econ
I 366 233 482 539 1807 1164 1944 1682 1732 639 687 772 12047L 97 6 50 79 288 140 317 147 155 20 37 14 1350T 71 3 23 51 58 56 117 25 25 3 23 10 465R 26.50 2.58 10.37 14.66 15.94 12.03 16.31 8.74 8.95 3.13 5.39 1.81 11.21P 73.20 50.00 46.00 64.56 20.14 40.00 36.91 17.01 16.13 15.00 62.16 71.43 34.44Q 19.40 1.29 4.77 9.46 3.21 4.81 6.02 1.49 1.44 0.47 3.35 1.30 3.86PT 989.99 807.00 946.65 1018.69 1032.55 1003.79 941.62 1007.04 996.20 1010.33 949.61 913.20 982.34
SRQ 992.45 954.33 959.26 1079.45 1073.24 1027.73 951.84 1011.28 935.36 1048.00 974.57 907.30 999.80
TECO 991.28 880.67 953.091049.12
(**)1052.97
(**)1015.82
946.78(**)
1009.28 965.92 1029.33 962.22 910.50 991.15
farm
I 333 1201 462 904 430 821 72 1279 250 244 5996L 76 199 193 88 31 119 7 314 35 46 1108T 39 107 54 15 21 58 0 68 27 5 394R 22.82 16.57 41.77 9.73 7.21 14.49 9.72 24.55 14.00 18.85 18.48P 51.32 53.77 27.98 17.05 67.74 48.74 0.00 21.66 77.14 10.87 35.56Q 11.71 8.91 11.69 1.66 4.88 7.06 0.00 5.32 10.80 2.05 6.57PT 1031.56 1050.89 998.11 1028.67 1039.62 948.22 886.19 824.52 920.20 979.59
SRQ 948.85 1038.62 1030.89 977.60 954.14 932.86 887.76 922.93 949.80 971.19
TECO 990.281044.74(**
)1014.59 1003.27 997.00
940.59(**)
887.12(**)
873.89 934.80 975.45
PT, SRQ, TECO: mean scores obtained for the PT module, the SRQ module, and the TECO test as a whole.Source: The 12 universities participating in the pilot test. For L, the source is the National Student Register (CINECA ANS)(*): For the University of Eastern Piedmont (PO), there is an anomalous presence of eligible students who sat the test despite the absence of registered students according to the CINECA database.(**): TECO result significantly different versus ITA12 (95% confidence interval)The yellow highlighting indicates groups that do not reach the threshold level of 30 tested students. For these groups, the significance of the difference in the TECO score versus ITA12 was therefore not tested.The dark grey highlighting indicates the Disciplinary Groups for which there is a national admission test. The light grey highlighting indicates the Disciplinary Groups for which there is a local admission test for more than 50% of the eligible students within the University.The very light grey highlighting indicates the Disciplinary Groups for which there is a local admission test for less than 50% of the eligible students within the University
TABLE 9: Indicators and results on PT, SRQ, TECO per Disciplinary Group, broken down by UniversityDisciplinary
GroupIndicat
orPO (*) MI PD UD BO FI RM1 RM2 NA LE ME CA ITA12
filo
I 57 428 156 217 110 277 90 209 83 45 58 1730L 17 85 21 41 31 91 21 92 6 11 6 422T 14 19 9 8 18 19 10 6 2 0 3 108R 29.82 19.86 13.46 18.89 28.18 32.85 23.33 44.02 7.23 24.44 10.34 24.39P 82.35 22.35 42.86 19.51 58.06 20.88 47.62 6.52 33.33 0.00 50.00 25.59Q 24.56 4.44 5.77 3.69 16.36 6.86 11.11 2.87 2.41 0.00 5.17 6.24PT 1046.21 1059.53 1108.67 1098.38 976.72 1088.16 974.60 840.67 886.00 1033.33 1032.05
SRQ 942.43 1018.21 1032.22 1091.63 985.50 1047.74 935.50 977.67 907.00 1071.00 1004.23TECO 994.43 1038.95 1070.44 1095.13 981.17 1068.05 955.00 909.17 897.00 1052.00 1018.20
form
I 618 851 684 119 162 309 343 220 3306L 107 16 132 49 0 69 40 18 431T 48 2 30 21 0 8 17 2 128R 17.31 1.88 19.30 41.18 0.00 22.33 11.66 8.18 13.04P 44.86 12.50 22.73 42.86 11.59 42.50 11.11 29.70Q 7.77 0.24 4.39 17.65 0.00 2.59 4.96 0.91 3.87PT 971.04 1090.00 917.77 900.76 911.63 880.12 954.00 932.83
SRQ 894.27 1083.00 874.63 853.86 837.13 833.00 837.00 873.38TECO 932.79(**) 1086.50 896.40 877.48 874.63 856.76 896.00 903.28
geo
I 101 108 162 107 172 286 211 288 145 26 1606L 21 15 19 7 24 105 36 21 2 2 252T 16 6 5 3 3 15 3 1 0 1 53R 20.79 13.89 11.73 6.54 13.95 36.71 17.06 7.29 1.38 7.69 15.69P 76.19 40.00 26.32 42.86 12.50 14.29 8.33 4.76 0.00 50.00 21.03Q 15.84 5.56 3.09 2.80 1.74 5.24 1.42 0.35 0.00 3.85 3.30PT 971.06 920.17 1069.40 988.00 1056.00 992.73 988.00 988.00 852.00 985.51
SRQ 894.25 802.00 1019.80 860.33 954.33 879.33 837.00 697.00 697.00 882.23TECO 932.69 861.17 1044.80 924.67 1005.33 936.07 912.67 842.00 775.00 933.96
PT, SRQ, TECO: mean scores obtained for the PT module, the SRQ module, and the TECO test as a whole.Source: The 12 universities participating in the pilot test. For L, the source is the National Student Register (CINECA ANS)(*): For the University of Eastern Piedmont (PO), there is an anomalous presence of eligible students who sat the test despite the absence of registered students according to the CINECA database.(**): TECO result significantly different versus ITA12 (95% confidence interval)The yellow highlighting indicates groups that do not reach the threshold level of 30 tested students. For these groups, the significance of the difference in the TECO score versus ITA12 was therefore not tested.The dark grey highlighting indicates the Disciplinary Groups for which there is a national admission test. The light grey highlighting indicates the Disciplinary Groups for which there is a local admission test for more than 50% of the eligible students within the University.The very light grey highlighting indicates the Disciplinary Groups for which there is a local admission test for less than 50% of the eligible students within the University
130
TABLE 9: Indicators and results on PT, SRQ, TECO per Disciplinary Group, broken down by UniversityDisciplinary
GroupIndicat
orPO (*) MI PD UD BO FI RM1 RM2 NA LE ME CA ITA12
giu
I 275 2116 1607 361 2060 1170 1924 856 3332 818 1017 811 16347L 85 937 277 72 526 475 438 113 958 228 101 109 4319T 64 191 74 47 55 119 99 24 59 86 35 21 874R 30.91 44.28 17.24 19.94 25.53 40.60 22.77 13.20 28.75 27.87 9.93 13.44 26.42P 75.29 20.38 26.71 65.28 10.46 25.05 22.60 21.24 6.16 37.72 34.65 19.27 20.24Q 23.27 9.03 4.60 13.02 2.67 10.17 5.15 2.80 1.77 10.51 3.44 2.59 5.35PT 1005.08 1049.21 1046.76 1103.57 1063.38 1060.47 993.47 948.46 961.53 954.87 955.11 994.52 1021.76
SRQ 968.81 1025.34 1063.00 1041.83 1017.20 1029.48 925.83 945.50 997.81 953.08 925.46 970.95 997.59
TECO 986.971037.32(**
)1054.93(**
)1072.77(**
)1040.31(**
)1045.06(**
)959.77(**
)946.96 979.73
954.13(**)
940.26(**)
982.71 1009.73
ing
I 1722 360 1468 586 1680 962 2546 489 203 535 10551L 141 10 62 183 472 138 154 45 0 14 1219T 44 7 6 68 208 35 67 20 0 8 463R 8.19 2.78 4.22 31.23 28.10 14.35 6.05 9.20 0.00 2.62 11.55P 31.21 70.00 9.68 37.16 44.07 25.36 43.51 44.44 57.14 37.98Q 2.56 1.94 0.41 11.60 12.38 3.64 2.63 4.09 0.00 1.50 4.39PT 977.20 910.43 1248.17 998.03 969.74 1013.31 968.75 988.05 903.13 980.10
SRQ 1011.07 1048.00 1188.17 1082.87 1004.23 1094.00 994.36 932.00 1030.38 1022.16
TECO 994.25 979.14 1218.331040.51(**
)987.07 1053.63 981.58 960.10 966.75 1001.20
lett
I 79 621 276 94 518 185 754 192 809 179 123 172 4002L 18 78 46 13 75 61 292 52 32 10 1 3 681T 15 38 12 13 12 13 52 14 15 3 1 2 190R 22.78 12.56 16.67 13.83 14.48 32.97 38.73 27.08 3.96 5.59 0.81 1.74 17.02P 83.33 48.72 26.09 100.00 16.00 21.31 17.81 26.92 46.88 30.00 100.00 66.67 27.90Q 18.99 6.12 4.35 13.83 2.32 7.03 6.90 7.29 1.85 1.68 0.81 1.16 4.75PT 1105.73 1075.61 1090.00 1019.38 1106.83 1040.31 990.71 988.00 1074.13 807.00 1056.00 954.00 1039.17
SRQ 1001.00 973.89 1065.42 928.85 1071.17 1020.85 977.63 957.57 958.80 884.00 1188.00 907.50 985.73TECO 1053.40 1024.87 1077.67 974.23 1089.08 1030.54 984.19 972.93 1016.53 845.67 1122.00 931.00 1012.51
PT, SRQ, TECO: mean scores obtained for the PT module, the SRQ module, and the TECO test as a whole.Source: The 12 universities participating in the pilot test. For L, the source is the National Student Register (CINECA ANS)(*): For the University of Eastern Piedmont (PO), there is an anomalous presence of eligible students who sat the test despite the absence of registered students according to the CINECA database.(**): TECO result significantly different versus ITA12 (95% confidence interval)The yellow highlighting indicates groups that do not reach the threshold level of 30 tested students. For these groups, the significance of the difference in the TECO score versus ITA12 was therefore not tested.The dark grey highlighting indicates the Disciplinary Groups for which there is a national admission test. The light grey highlighting indicates the Disciplinary Groups for which there is a local admission test for more than 50% of the eligible students within the University.The very light grey highlighting indicates the Disciplinary Groups for which there is a local admission test for less than 50% of the eligible students within the University
TABLE 9: Indicators and results on PT, SRQ, TECO per Disciplinary Group, broken down by University
Disciplinary Group
Indicator
PO (*) MI PD UD BO FI RM1 RM2 NA LE ME CA ITA12
ling
I 101 1491 605 332 1104 430 1153 598 353 316 283 338 7104L 22 109 69 66 27 52 413 87 228 15 1 84 1173T 13 20 14 44 3 7 70 17 25 3 0 15 231R 21.78 7.31 11.40 19.88 2.45 12.09 35.82 14.55 64.59 4.75 0.35 24.85 16.51P 59.09 18.35 20.29 66.67 11.11 13.46 16.95 19.54 10.96 20.00 0.00 17.86 19.69Q 12.87 1.34 2.31 13.25 0.27 1.63 6.07 2.84 7.08 0.95 0.00 4.44 3.25PT 1029.77 1018.60 1094.79 1015.77 920.00 959.00 989.01 936.06 1026.12 829.67 969.93 1000.38
SRQ 918.31 956.55 1012.79 988.77 1047.67 957.71 980.63 903.24 972.08 1048.00 940.13 970.34TECO 974.23 987.65 1053.79 1002.32 984.00 958.29 984.77 919.76 999.12 938.67 955.07 985.38
mat.fis.stat
I 85 789 626 200 709 331 986 506 517 152 165 229 5295L 12 66 45 13 76 119 344 62 61 2 3 26 829T 10 30 22 11 11 63 201 19 12 2 3 4 388R 14.12 8.37 7.19 6.50 10.72 35.95 34.89 12.25 11.80 1.32 1.82 11.35 15.66P 83.33 45.45 48.89 84.62 14.47 52.94 58.43 30.65 19.67 100.00 100.00 15.38 46.80Q 11.76 3.80 3.51 5.50 1.55 19.03 20.39 3.75 2.32 1.32 1.82 1.75 7.33PT 1062.70 1085.37 1086.77 951.00 1173.09 1023.62 1000.16 1063.11 1039.00 886.00 1191.67 1174.75 1027.57
SRQ 914.40 1120.30 1063.68 1009.55 1200.91 1103.48 1045.34 992.37 925.00 1082.50 1071.33 995.00 1055.19
TECO 988.50 1102.93 1075.23 980.27 1187.091063.56(**
)1022.81 1027.79 981.92 984.50 1131.67 1085.00 1041.43
med
I 184 766 706 181 759 729 2339 506 1148 451 380 8149L 65 199 189 69 420 89 558 141 221 42 107 2100T 27 30 14 32 49 32 84 1 95 6 23 393R 35.33 25.98 26.77 38.12 55.34 12.21 23.86 27.87 19.25 9.31 28.16 25.77P 41.54 15.08 7.41 46.38 11.67 35.96 15.05 0.71 42.99 14.29 21.50 18.71Q 14.67 3.92 1.98 17.68 6.46 4.39 3.59 0.20 8.28 1.33 6.05 4.82PT 1053.44 1094.37 1172.21 1081.44 1006.06 1136.56 1026.77 1124.00 1054.49 1135.33 1011.65 1057.48
SRQ 1102.41 1120.27 1133.00 1131.19 1095.06 1131.09 1076.23 1188.00 1047.07 1012.83 1075.22 1086.91
TECO 1077.85 1107.43 1152.711106.31(**
)1050.63(**
)1133.91(**
)1051.56
(**)1156.00
1050.84 (**)
1074.00 1043.57 1072.25
PT, SRQ, TECO: mean scores obtained for the PT module, the SRQ module, and the TECO test as a whole.Source: The 12 universities participating in the pilot test. For L, the source is the National Student Register (CINECA ANS)(*): For the University of Eastern Piedmont (PO), there is an anomalous presence of eligible students who sat the test despite the absence of registered students according to the CINECA database.(**): TECO result significantly different versus ITA12 (95% confidence interval)The yellow highlighting indicates groups that do not reach the threshold level of 30 tested students. For these groups, the significance of the difference in the TECO score versus ITA12 was therefore not tested.The dark grey highlighting indicates the Disciplinary Groups for which there is a national admission test. The light grey highlighting indicates the Disciplinary Groups for which there is a local admission test for more than 50% of the eligible students within the University.The very light grey highlighting indicates the Disciplinary Groups for which there is a local admission test for less than 50% of the eligible students within the University
131
TABLE 9: Indicators and results on PT, SRQ, TECO per Disciplinary Group, broken down by UniversityDisciplinary
GroupIndicat
orPO (*) MI PD UD BO FI RM1 RM2 NA LE ME CA ITA12
odon
I 91 38 54 71 109 61 47 43 32 546L 41 30 45 22 42 8 29 17 10 244T 12 1 0 1 3 6 20 0 1 44R 45.05 78.95 83.33 30.99 38.53 13.11 61.70 39.53 31.25 44.69P 29.27 3.33 0.00 4.55 7.14 75.00 68.97 0.00 10.00 18.03Q 13.19 2.63 0.00 1.41 2.75 9.84 42.55 0.00 3.13 8.06PT 1016.17 1192.00 920.00 1124.00 920.17 994.90 1192.00 1006.57
SRQ 1164.67 907.00 1188.00 1094.33 837.17 988.05 907.00 1023.75TECO 1090.50 1050.00 1054.00 1109.67 878.83 991.55 1050.00 1015.30
polit
I 124 1277 497 670 643 933 542 273 122 525 5606L 34 127 59 76 61 236 37 23 3 22 678T 16 51 9 11 22 50 20 10 2 10 201R 27.42 9.95 11.87 11.34 9.49 25.29 6.83 8.42 2.46 4.19 12.09P 47.06 40.16 15.25 14.47 36.07 21.19 54.05 43.48 66.67 45.45 29.65Q 12.90 3.99 1.81 1.64 3.42 5.36 3.69 3.66 1.64 1.90 3.59PT 1043.06 1040.00 1116.22 1012.73 1037.36 1023.32 920.20 811.50 1157.50 1028.80 1015.05
SRQ 916.06 1025.71 1071.22 996.82 1038.18 1014.14 953.00 823.00 1013.00 998.60 997.13TECO 979.63 1032.94 1093.78 1004.82 1037.95 1018.78 936.70 817.40 1085.50 1013.80 1006.18
psic
I 1042 303 498 1168 310 142 342 268 4073L 221 69 194 504 39 56 7 18 1108T 54 19 31 61 12 11 1 2 191R 21.21 22.77 38.96 43.15 12.58 39.44 2.05 6.72 27.20P 24.43 27.54 15.98 12.10 30.77 19.64 14.29 11.11 17.24Q 5.18 6.27 6.22 5.22 3.87 7.75 0.29 0.75 4.69PT 1037.04 1041.63 1034.00 1005.87 999.33 1006.64 988.00 852.50 1020.74
SRQ 1054.30 1055.16 1004.74 1046.67 1094.67 990.45 627.00 872.50 1038.62
TECO1045.78(**
)1048.47 1019.52 1026.31 1047.00 998.55 807.00 862.50 1029.75
PT, SRQ, TECO: mean scores obtained for the PT module, the SRQ module, and the TECO test as a whole.Source: The 12 universities participating in the pilot test. For L, the source is the National Student Register (CINECA ANS)(*): For the University of Eastern Piedmont (PO), there is an anomalous presence of eligible students who sat the test despite the absence of registered students according to the CINECA database.(**): TECO result significantly different versus ITA12 (95% confidence interval)The yellow highlighting indicates groups that do not reach the threshold level of 30 tested students. For these groups, the significance of the difference in the TECO score versus ITA12 was therefore not tested.The dark grey highlighting indicates the Disciplinary Groups for which there is a national admission test. The light grey highlighting indicates the Disciplinary Groups for which there is a local admission test for more than 50% of the eligible students within the University.The very light grey highlighting indicates the Disciplinary Groups for which there is a local admission test for less than 50% of the eligible students within the University
TABLE 9: Indicators and results on PT, SRQ, TECO per Disciplinary Group, broken down by University
Disciplinary Group
Indicator
PO (*) MI PD UD BO FI RM1 RM2 NA LE ME CA ITA12
soc
I 26 216 436 132 433 1 755 251 246 24 2520L 26 28 127 21 74 0 52 31 6 3 368T 8 10 10 10 17 0 22 1 1 0 79R 100.00 12.96 29.13 15.91 17.09 0.00 6.89 12.35 2.44 12.50 14.60P 30.77 35.71 7.87 47.62 22.97 42.31 3.23 16.67 0.00 21.47Q 30.77 4.63 2.29 7.58 3.93 0.00 2.91 0.40 0.41 0.00 3.13PT 1022.00 1022.00 1042.30 1117.00 976.00 901.55 649.00 852.00 986.28
SRQ 942.63 914.30 1047.80 1026.70 882.65 894.64 556.00 767.00 929.61TECO 982.25 968.10 1045.00 1072.00 929.41 898.32 603.00 810.00 958.04
sto
I 349 114 382 99 269 96 35 1344L 35 21 61 11 75 30 2 235T 16 5 10 2 17 6 1 57R 10.03 18.42 15.97 11.11 27.88 31.25 5.71 17.49P 45.71 23.81 16.39 18.18 22.67 20.00 50.00 24.26Q 4.58 4.39 2.62 2.02 6.32 6.25 2.86 4.24PT 1098.31 1042.20 960.80 1022.00 1024.00 1090.00 717.00 1036.86
SRQ 1061.00 1076.20 858.20 1083.00 969.29 919.00 1048.00 985.00TECO 1079.81 1059.20 909.70 1052.50 996.65 1004.67 882.00 1011.02
terr
I 51 236 718 434 674 1247 1874 283 1411 282 224 657 8091L 5 24 53 10 13 364 457 24 68 23 1 3 1045T 4 15 16 9 1 95 215 0 29 4 1 2 391R 9.80 10.17 7.38 2.30 1.93 29.19 24.39 8.48 4.82 8.16 0.45 0.46 12.92P 80.00 62.50 30.19 90.00 7.69 26.10 47.05 0.00 42.65 17.39 100.00 66.67 37.42Q 7.84 6.36 2.23 2.07 0.15 7.62 11.47 0.00 2.06 1.42 0.45 0.30 4.83PT 971.00 1006.13 962.56 965.33 1056.00 961.56 920.43 927.14 920.00 1192.00 954.00 938.69
SRQ 942.50 1061.87 933.69 1032.22 1188.00 998.32 904.79 791.21 1048.00 1048.00 977.50 932.54
TECO 956.75 1034.00 948.25 998.89 1122.00 979.99912.72(**
)859.21 984.00 1120.00 966.00 935.70
PT, SRQ, TECO: mean scores obtained for the PT module, the SRQ module, and the TECO test as a whole.Source: The 12 universities participating in the pilot test. For L, the source is the National Student Register (CINECA ANS)(*): For the University of Eastern Piedmont (PO), there is an anomalous presence of eligible students who sat the test despite the absence of registered students according to the CINECA database.(**): TECO result significantly different versus ITA12 (95% confidence interval)The yellow highlighting indicates groups that do not reach the threshold level of 30 tested students. For these groups, the significance of the difference in the TECO score versus ITA12 was therefore not tested.The dark grey highlighting indicates the Disciplinary Groups for which there is a national admission test. The light grey highlighting indicates the Disciplinary Groups for which there is a local admission test for more than 50% of the eligible students in ITA12 as a whole.The Territory Group is considered to be in the same situation: 49.94% of students in the ITA12 are enrolled in courses with a local admission test and 21.39% of the same students are enrolled in courses with a national admission test. The very light grey highlighting indicates the Disciplinary Groups for which there is a local admission test for less than 50% of the eligible students within the University.
132
TABLE 9: Indicators and results on PT, SRQ, TECO per Disciplinary Group, broken down by UniversityDisciplinary
GroupIndicator PO (*) MI PD UD BO FI RM1 RM2 NA LE ME CA ITA12
vet
I 282 123 278 264 72 1019L 136 47 63 23 16 285T 37 11 28 16 1 93R 48.23 38.21 22.66 8.71 22.22 27.97P 27.21 23.40 44.44 69.57 6.25 32.63Q 13.12 8.94 10.07 6.06 1.39 9.13PT 993.54 1043.55 1029.25 852.13 717.00 982.90
SRQ 1047.78 1137.18 1070.29 837.25 697.00 1025.14TECO 1020.70 1090.36 1049.93 844.81 707.00 1004.11
dif
I 2 58 60L 0 0 0T 0 0 0R 0.0 0.0 0.0PQ 0.0 0.0 0.0PT
SRQTECO
Column Total
I 1947 13173 11916 3440 16476 10927 20637 7903 18990 4533 5630 6043 121615L 506 2574 1918 448 2645 2457 5808 1080 2976 555 358 547 21872T 319 798 549 287 368 691 1657 183 584 157 131 129 5853R 25.99 19.54 16.1 13.02 16.05 22.49 28.14 13.67 15.67 12.24 6.36 9.05 17.98P 63.04 31 28.62 64.06 13.91 28.12 28.53 16.94 19.62 28.29 36.59 23.58 26.76Q 16.38 6.06 4.61 8.34 2.23 6.32 8.03 2.32 3.08 3.46 2.33 2.13 4.81
PT1017.21
(**)1036.55 1024.14 1020.45 1039.69 1016.02 974.09 984.34 971.17 940.89 927.4 990.66 999.46
SRQ 972.55 1032.39 1026.71 1028.01 1055.61 1033.26 977.86 978 959.57 938.68 924.01 981.41 999.48
TECO 994.921034.53
(**)1025.49
(**)1024.28
(**)1047.73
(**)1024.71
(**)976.04
(**)981.22
965.43 (**)
939.92 (**)
925.78 (**)
986.1 999.53
PT, SRQ, TECO: mean scores obtained for the PT module, the SRQ module, and the TECO test as a whole.Source: The 12 universities participating in the pilot test. For L, the source is the National Student Register (CINECA ANS)(*): For the University of Eastern Piedmont (PO), there is an anomalous presence of eligible students who sat the test despite the absence of registered students according to the CINECA database.(**): TECO result significantly different versus ITA12 (95% confidence interval)The yellow highlighting indicates groups that do not reach the threshold level of 30 tested students. For these groups, the significance of the difference in the TECO score versus ITA12 was therefore not tested.The dark grey highlighting indicates the Disciplinary Groups for which there is a national admission test. The light grey highlighting indicates the Disciplinary Groups for which there is a local admission test for more than 50% of the eligible students within the University.The very light grey highlighting indicates the Disciplinary Groups for which there is a local admission test for less than 50% of the eligible students within the University
TABLE 9: Indicators and results on PT, SRQ, TECO per Macro-group, broken down by University
Macro-groupIndicat
orPO (*) MI PD UD BO FI RM1 RM2 NA LE ME CA ITA12
SAN
I 517 2,340 1,329 181 1,995 1,230 3,269 639 2,738 816 656 15,710L 141 575 459 69 616 142 719 156 587 110 163 3,737T 66 186 80 32 92 54 145 7 199 34 29 924R 27.27 24.57 34.54 38.12 30.88 11.54 21.99 24.41 21.44 13.48 24.85 23.79P 46.81 32.35 17.43 46.38 14.94 38.03 20.17 4.49 33.90 30.91 17.79 24.73Q 12.77 7.95 6.02 17.68 4.61 4.39 4.44 1.10 7.27 4.17 4.42 5.88PT 1040.52 1044.25 1037.25 1081.44 1016.80 1094.85 997.37 949.29 974.72 876.21 1002.10 1014.33
SRQ 1011.67 1061.74 1061.83 1131.19 1068.37 1063.33 1019.26 887.29 969.83 932.15 1047.79 1028.34TECO 1026.11 1053.02 1049.63 1106.31 1042.70 1079.19 1008.37 918.43 972.37 904.29 1025.03 1021.40
SC
I 298 3,026 4,216 1,422 4,757 3,705 6,393 2,842 7,428 1,225 1,065 1,698 38,075L 42 448 487 67 458 1,035 1,932 373 702 70 17 57 5,688T 34 215 190 51 69 307 852 72 173 26 13 19 2,021R 14.09 14.81 11.55 4.71 9.63 27.94 30.22 13.12 9.45 5.71 1.60 3.36 14.94P 80.95 47.99 39.01 76.12 15.07 29.66 44.10 19.30 24.64 37.14 76.47 33.33 35.53Q 11.41 7.11 4.51 3.59 1.45 8.29 13.33 2.53 2.33 2.12 1.22 1.12 5.31PT 1004.00 1013.30 1009.12 976.00 1070.62 990.03 963.86 1004.10 965.65 969.73 972.38 995.11 983.99
SRQ 954.88 1042.90 1039.30 1030.02 1098.64 1051.23 983.69 1025.39 942.71 961.42 929.00 977.63 1007.37TECO 979.50 1028.17 1024.26 1003.04 1084.71 1020.67 973.85 1014.79 954.19 965.65 950.77 986.42 995.74
PT, SRQ, TECO: mean scores obtained for the PT module, the SRQ module, and the TECO test as a whole.Source: The 12 universities participating in the pilot test. For L, the source is the National Student Register (CINECA ANS)(*): For the University of Eastern Piedmont (PO), there is an anomalous presence of eligible students who sat the test despite the absence of registered students according to the CINECA database.
133
TABLE 9: Indicators and results on PT, SRQ, TECO per Macro-group, broken down by University
Macro-groupIndicat
orPO (*) MI PD UD BO FI RM1 RM2 NA LE ME CA ITA12
SOC
I 887 4,124 4,190 1,279 5,709 3,932 7,219 2,959 7,017 2,291 2,849 2,720 45,176L 258 1,108 665 221 1,209 940 1,809 321 1,262 376 170 210 8,549T 171 265 178 137 165 245 395 54 139 112 64 54 1,979R 29.09 26.87 15.87 17.28 21.18 23.91 25.06 10.85 17.98 16.41 5.97 7.72 18.92P 66.28 23.92 26.77 61.99 13.65 26.06 21.84 16.82 11.01 29.79 37.65 25.71 23.15Q 19.28 6.43 4.25 10.71 2.89 6.23 5.47 1.82 1.98 4.89 2.25 1.99 4.38PT 1002.76 1045.45 1034.17 1032.17 1034.15 1043.71 981.84 974.20 955.78 945.97 957.28 984.26 1007.55
SRQ 968.16 1023.65 1035.97 1028.34 1040.17 1023.71 963.40 977.63 970.00 939.67 935.94 959.41 995.25TECO 985.50 1034.62 1035.15 1030.32 1037.20 1033.81 972.70 975.96 962.99 942.96 946.64 971.89 1001.47
H
I 245 3,683 2,181 558 4,015 2,060 3,756 1,463 1,807 1,017 900 969 22,654L 65 443 307 91 362 340 1,348 230 425 109 61 117 3,898T 48 132 101 67 42 85 265 50 73 19 20 27 929R 26.53 12.03 14.08 16.31 9.02 16.50 35.89 15.72 23.52 10.72 6.78 12.07 17.21P 73.85 29.80 32.90 73.63 11.60 25.00 19.66 21.74 17.18 17.43 32.79 23.08 23.83Q 19.59 3.58 4.63 12.01 1.05 4.13 7.06 3.42 4.04 1.87 2.22 2.79 4.10PT 1046.00 1045.67 1024.34 1001.18 1060.74 980.02 982.68 971.74 1003.86 867.33 889.55 988.04 1001.06
SRQ 946.88 991.44 958.89 976.52 1017.64 976.81 958.00 922.86 951.68 899.61 868.75 956.78 962.56TECO 996.56 1018.65 991.68 988.93 1039.36 978.48 970.38 947.36 977.81 883.61 879.30 972.48 981.88
PT, SRQ, TECO: mean scores obtained for the PT module, the SRQ module, and the TECO test as a whole.Source: The 12 universities participating in the pilot test. For L, the source is the National Student Register (CINECA ANS)(*): For the University of Eastern Piedmont (PO), there is an anomalous presence of eligible students who sat the test despite the absence of registered students according to the CINECA database.
TABLE 10: Indicators and results on PT, SRQ, TECO per Disciplinary Group, broken down by Geographic Area
Disciplinary Group
Indicator
NORTH CENTRE SOUTHCENTRE-NORTH
CENTRE-SOUTH
ITA12Disciplinary
GroupIndicat
orNORTH CENTRE SOUTH
CENTRE-NORTH
CENTRE-SOUTH
ITA12
agr.al
I 1827 766 646 2592 647 3239
chim
I 379 682 304 670 695 1365L 208 116 37 324 37 361 L 62 135 81 94 184 278T 87 36 16 123 16 139 T 50 49 7 52 54 106R 11.38 15.14 5.73 12.50 5.72 11.15 R 16.36 19.79 26.64 14.03 26.47 20.37P 41.83 31.03 43.24 37.96 43.24 38.50 P 80.65 36.30 8.64 55.32 29.35 38.13Q 4.76 4.70 2.48 4.75 2.47 4.29 Q 13.19 7.18 2.30 7.76 7.77 7.77PT 988.07 1010.64 877.81 994.67 877.81 981.22 PT 998.88 945.04 900.71 1006.29 930.17 967.51
SRQ 986.52 1028.31 894.25 998.75 894.25 986.72 SRQ 1060.42 973.31 1108.00 1058.60 989.30 1023.29TECO 987.33 1019.56 885.94 996.76 885.94 984.01 TECO 1029.74 959.20 1004.29 1032.52 959.76 995.45
arch
I 166 2223 667 1141 1915 3056
comun
I 954 1359 752 1543 1522 3065L 93 799 244 440 696 1136 L 112 324 74 260 250 510T 38 212 22 102 170 272 T 59 59 13 75 56 131R 56.02 35.94 36.58 38.56 36.34 37.17 R 11.74 23.84 9.84 16.85 16.43 16.64P 40.86 26.53 9.02 23.18 24.43 23.94 P 52.68 18.21 17.57 28.85 22.40 25.69Q 22.89 9.54 3.30 8.94 8.88 8.90 Q 6.18 4.34 1.73 4.86 3.68 4.27PT 1041.61 979.99 997.18 1019.96 972.01 989.99 PT 1000.71 973.08 1009.00 987.16 991.68 989.09
SRQ 1095.76 1019.67 913.73 1071.85 991.66 1021.73 SRQ 972.85 966.92 934.38 972.95 957.54 966.36
TECO1068.76
(**)999.91 955.55 1045.97 981.92 1005.94 TECO 986.90 970.08 971.62 980.15 974.68 977.81
art
I 223 1729 19 1281 690 1971
cult
I 1048 1458 732 1548 1690 3238L 30 382 0 169 243 412 L 159 300 64 215 308 523T 16 48 0 26 38 64 T 47 65 30 59 83 142R 13.45 22.09 0.00 13.19 35.22 20.90 R 15.17 20.58 8.74 13.89 18.22 16.15P 53.33 12.57 15.38 15.64 15.53 P 29.56 21.67 46.88 27.44 26.95 27.15Q 7.17 2.78 0.00 2.03 5.51 3.25 Q 4.48 4.46 4.10 3.81 4.91 4.39PT 945.63 985.19 1006.31 954.08 975.30 PT 1006.83 983.8462 958.6 1019.119 962.6627 986.12
SRQ 955.75 954.19 991.12 929.58 954.58 SRQ 1000.021 962.4769 937.9667 1028.763 927.759 969.73TECO 950.75 969.79 998.81 941.92 965.03 TECO 1003.532 973.2308 948.3 (**) 1024.034 945.2651 977.99
bio
I 1331 2598 2360 2681 3608 6289
econ
I 1620 6597 3830 4591 7456 12047L 292 429 84 473 332 805 L 232 892 226 660 690 1350T 142 80 34 172 84 256 T 148 256 61 262 203 465R 21.94 16.51 3.56 17.64 9.20 12.80 R 14.32 13.52 5.90 14.38 9.25 11.21P 48.63 18.65 40.48 36.36 25.30 31.80 P 63.79 28.70 26.99 39.70 29.42 34.44Q 10.67 3.08 1.44 6.42 2.33 4.07 Q 9.14 3.88 1.59 5.71 2.72 3.86PT 1010.99 979.58 984.06 1005.81 980.77 997.59 PT 989.43 982.21 965.72 1002.05 956.92 982.34
SRQ 1045.88 986.41 954.91 1051.13 941.68 1015.21 SRQ 1016.50 1001.75 951.08 1031.46 958.93 999.80
TECO1028.48
(**)982.99 969.50 1028.50 961.24 1006.43 TECO 1003.03 992.04 958.56 (**) 1016.82 958.01 991.15
PT, SRQ, TECO: mean scores obtained for the PT module, the SRQ module, and the TECO test as a whole.
(**): TECO result significantly different versus ITA12 (95% confidence interval) calculated for the Geographic Areas NORTH, CENTRE and SOUTHand for ITA12
Source: The 12 universities participating in the pilot test. For L, the source is the National Student Register (CINECA ANS)The yellow highlighting indicates groups that do not reach the threshold level of 30 tested students. For these groups, the significance of the difference in the TECO score versus ITA12 was therefore not tested.
The dark grey highlighting indicates the Disciplinary Groups for which there is a national admission test.
The light grey highlighting indicates the Disciplinary Groups for which there is a local admission test for more than 50% of the eligible students within the Geographic Area.
The very light grey highlighting indicates the Disciplinary Groups for which there is a local admission test for less than 50% of the eligible students within the Geographic Area
134
TABLE 10: Indicators and results on PT, SRQ, TECO per Disciplinary Group, broken down by Geographic AreaDisciplinary
GroupIndicat
orNORTH CENTRE SOUTH
CENTRE-NORTH
CENTRE-SOUTH
ITA12Disciplinary
GroupIndicat
orNORTH CENTRE SOUTH
CENTRE-NORTH
CENTRE-SOUTH
ITA12
farm
I 1996 2227 1773 3330 2666 5996
giu
I 4359 6010 5978 7589 8758 16347
L 468 245 395 587 521 1108 L 1371 1552 1396 2372 1947 4319
T 200 94 100 236 158 394 T 376 297 201 550 324 874
R 23.45 11.00 22.28 17.63 19.54 18.48 R 31.45 25.82 23.35 31.26 22.23 26.42
P 42.74 38.37 25.32 40.20 30.33 35.56 P 27.43 19.14 14.40 23.19 16.64 20.24
Q 10.02 4.22 5.64 7.09 5.93 6.57 Q 8.63 4.94 3.36 7.25 3.70 5.35
PT 1032.87 981.48 871.24 1033.20 899.50 979.59 PT 1048.01 1029.63 961.01 1052.24 970.00 1021.76
SRQ 1019.03 944.76 900.36 1010.62 912.29 971.19 SRQ 1025.19 985.87 963.27 1025.32 950.51 997.59
TECO 1025.98(**) 963.19(**) 885.93(**) 1021.96 905.99 975.45(**) TECO 1036.65(**) 1007.82 962.21(**) 1038.83 960.34 1009.73
filo
I 641 694 395 968 762 1730
ing
I 2082 4696 3773 4136 6415 10551
L 123 184 115 195 227 422 L 151 855 213 396 823 1219
T 42 55 11 68 40 108 T 51 317 95 125 338 463
R 19.19 26.51 29.11 20.14 29.79 24.39 R 7.25 18.21 5.65 9.57 12.83 11.55
P 34.15 29.89 9.57 34.87 17.62 25.59 P 33.77 37.08 44.60 31.57 41.07 37.98
Q 6.55 7.93 2.78 7.02 5.25 6.24 Q 2.45 6.75 2.52 3.02 5.27 4.39
PT 1065.62 1032.53 901.45 1045.94 1008.43 1032.05 PT 968.04 985.89 967.28 997.80 973.56 980.10
SRQ 995.95 1013.35 990.27 1004.44 1003.88 1004.23 SRQ 1016.14 1034.49 984.26 1060.70 1007.91 1022.16
TECO 1030.86 1023.00 945.91 1025.26 1006.20 1018.20 TECO 992.18 1010.26 975.81 1029.33 990.80 1001.20
form
I 618 1816 872 2153 1153 3306
lett
I 1070 1649 1283 1773 2229 4002
L 107 197 127 255 176 431 L 155 480 46 291 390 681
T 48 53 27 80 48 128 T 78 91 21 103 87 190
R 17.31 10.85 14.56 11.84 15.26 13.04 R 14.49 29.11 3.59 16.41 17.50 17.02
P 44.86 26.90 21.26 31.37 27.27 29.70 P 50.32 18.96 45.65 35.40 22.31 27.90
Q 7.77 2.92 3.10 3.72 4.16 3.87 Q 7.29 5.52 1.64 5.81 3.90 4.75
PT 971.04 917.53 894.93 954.04 897.48 932.83 PT 1074.24 1012.69 1023.67 1073.76 998.23 1039.17
SRQ 894.27 874.26 834.52 891.63 842.98 873.38 SRQ 985.68 993.05 954.14 1000.08 968.74 985.73
TECO 932.79(**) 896.08(**) 864.96 922.99 870.44 903.28(**) TECO 1030.04 1002.91 989.00 1036.98 983.54 1012.51
geo
I 478 669 459 650 956 1606
ling
I 2529 3285 1290 4063 3041 7104
L 62 165 25 86 166 252 L 266 579 328 345 828 1173
T 30 21 2 33 20 53 T 91 97 43 101 130 231
R 12.97 24.66 5.45 13.23 17.36 15.69 R 10.52 17.63 25.43 8.49 27.23 16.51
P 48.39 12.73 8.00 38.37 12.05 21.03 P 34.21 16.75 13.11 29.28 15.70 19.69
Q 6.28 3.14 0.44 5.08 2.09 3.30 Q 3.60 2.95 3.33 2.49 4.27 3.25
PT 978.97 1001.10 920.00 985.97 984.75 985.51 PT 1030.55 975.43 992.81 1022.31 983.35 1000.38
SRQ 893.33 884.00 697.00 898.88 854.75 882.23 SRQ 975.32 967.48 966.23 976.25 965.75 970.34
TECO 936.27(**) 942.62 808.50 942.55 919.80 933.96(**) TECO 1003.00 971.44(**) 979.53 999.34 974.54 985.38
PT, SRQ, TECO: mean scores obtained for the PT module, the SRQ module, and the TECO test as a whole.
(**): TECO result significantly different versus ITA12 (95% confidence interval)calculated for the Geographic Areas NORTH, CENTRE and SOUTH and for ITA12
Source: The 12 universities participating in the pilot test. For L, the source is the National Student Register (CINECA ANS)
The yellow highlighting indicates groups that do not reach the threshold level of 30 tested students. For these groups, the significance of the difference in the TECO score versus ITA12 was therefore not tested.
The dark grey highlighting indicates the Disciplinary Groups for which there is a national admission test.
The light grey highlighting indicates the Disciplinary Groups for which there is a local admission test for more than 50% of the eligible students within the Geographic Area.
The very light grey highlighting indicates the Disciplinary Groups for which there is a local admission test for less than 50% of the eligible students within the Geographic Area
TABLE 10: Indicators and results on PT, SRQ, TECO per Disciplinary Group, broken down by Geographic Area
Disciplinary Group
Indicator
NORTH CENTRE SOUTHCENTRE-NORTH
CENTRE-SOUTH
ITA12Disciplinary
GroupIndicat
orNORTH CENTRE SOUTH
CENTRE-NORTH
CENTRE-SOUTH
ITA12
mat.fis.stat
I 1700 2532 1063 2740 2555 5295
psic
I 1042 1969 1062 1843 2230 4073
L 136 601 92 331 498 829 L 221 767 120 484 624 1108
T 73 294 21 147 241 388 T 54 111 26 104 87 191
R 8.00 23.74 8.65 12.08 19.49 15.66 R 21.21 38.95 11.30 26.26 27.98 27.20
P 53.68 48.92 22.83 44.41 48.39 46.80 P 24.43 14.47 21.67 21.49 13.94 17.24
Q 4.29 11.61 1.98 5.36 9.43 7.33 Q 5.18 5.64 2.45 5.64 3.90 4.69
PT 1062.44 1015.73 1072.10 1054.08 1011.39 1027.57 PT 1037.04 1019.85 990.69 1036.97 1001.33 1020.74
SRQ 1058.34 1060.19 974.24 1088.35 1034.97 1055.19 SRQ 1054.30 1036.41 1015.50 1039.68 1037.36 1038.62
TECO 1060.42(**) 1038.01(**) 1023.19 1071.24 1023.24 1041.43(**) TECO 1045.78(**) 1028.21(**) 1003.08 1038.44 1019.37 1029.75(**)
med
I 1837 4333 1979 3325 4824 8149
soc
I 242 1002 1276 810 1710 2520
L 522 1208 370 1031 1069 2100 L 54 222 92 202 166 368
T 103 166 124 184 209 393 T 18 37 24 38 41 79
R 28.42 27.88 18.70 31.01 22.16 25.77 R 22.31 22.16 7.21 24.94 9.71 14.60
P 19.73 13.74 33.51 17.85 19.55 18.71 P 33.33 16.67 26.09 18.81 24.70 21.47
Q 5.61 3.83 6.27 5.53 4.33 4.82 Q 7.44 3.69 1.88 4.69 2.40 3.13
PT 1090.20 1042.41 1050.46 1075.86 1041.29 1057.48 PT 1022.00 1032.03 888.96 1052.34 925.05 986.28
SRQ 1120.71 1093.04 1050.64 1115.68 1061.58 1086.91 SRQ 926.89 966.22 875.21 984.97 878.29 929.61
TECO 1105.49(**) 1067.79(**) 1050.61(**) 1095.82 1051.50 1072.25(**) TECO 974.39 999.19 882.33 1018.66 901.85 958.04(**)
odon
I 129 295 122 254 292 546
sto
I 463 750 131 944 400 1344
L 71 117 56 138 106 244 L 56 147 32 128 107 235
T 13 10 21 14 30 44 T 21 29 7 33 24 57
R 55.04 39.66 45.90 54.33 36.30 44.69 R 12.10 19.60 24.43 13.56 26.75 17.49
P 18.31 8.55 37.50 10.14 28.30 18.03 P 37.50 19.73 21.88 25.78 22.43 24.26
Q 10.08 3.39 17.21 5.51 10.27 8.06 Q 4.54 3.87 5.34 3.50 6.00 4.24
PT 1029.69 981.30 1004.29 1021.86 999.43 1006.57 PT 1084.95 1002.07 1036.71 1043.52 1027.71 1036.86
SRQ 1144.85 949.40 984.19 1147.93 965.80 1023.75 SRQ 1064.62 938.83 937.43 1003.18 960.00 985.00
TECO 1087.38 965.60 994.33 1085.00 982.77 1015.30 TECO 1074.90 970.52 987.14 1023.48 993.88 1011.02
polit
I 1898 2246 1462 3211 2395 5606
terr
I 1439 4078 2574 3360 4731 8091
L 220 373 85 357 321 678 L 92 858 95 469 576 1045
T 76 83 42 109 92 201 T 44 311 36 140 251 391
R 11.59 16.61 5.81 11.12 13.40 12.09 R 6.39 21.04 3.69 13.96 12.18 12.92
P 34.55 22.25 49.41 30.53 28.66 29.65 P 47.83 36.25 37.89 29.85 43.58 37.42
Q 4.00 3.70 2.87 3.39 3.84 3.59 Q 3.06 7.63 1.40 4.17 5.31 4.83
PT 1049.67 1025.64 931.48 1043.46 981.39 1015.05 PT 978.75 933.43 935.19 967.64 922.55 938.69
SRQ 1008.01 1018.22 935.76 1012.97 978.36 997.13 SRQ 998.34 934.27 837.22 999.68 895.10 932.54
TECO 1028.92 1022.01 933.74(**) 1028.31 979.96 1006.18 TECO 988.61 933.94(**) 886.25(**) 983.71 908.92 935.70(**)
PT, SRQ, TECO: mean scores obtained for the PT module, the SRQ module, and the TECO test as a whole.
(**): TECO result significantly different versus ITA12 (95% confidence interval)calculated for the Geographic Areas NORTH, CENTRE and SOUTH and for ITA12
Source: The 12 universities participating in the pilot test. For L, the source is the National Student Register (CINECA ANS)
The yellow highlighting indicates groups that do not reach the threshold level of 30 tested students. For these groups, the significance of the difference in the TECO score versus ITA12 was therefore not tested.
The dark grey highlighting indicates the Disciplinary Groups for which there is a national admission test.
The light grey highlighting indicates the Disciplinary Groups for which there is a local admission test for more than 50% of the eligible students within the Geographic Area. The Territory Group is considered to be in the same situation: 49.94% of students in the ITA12 are enrolled in courses with a local admission test and 21.39% of the same students are enrolled in courses with a national admission test.
The very light grey highlighting indicates the Disciplinary Groups for which there is a local admission test for less than 50% of the eligible students within the Geographic Area.
135
TABLE 10: Indicators and results on PT, SRQ, TECO per Disciplinary Group, broken down by Geographic AreaDisciplinary
GroupIndicator NORTH CENTRE SOUTH
CENTRE-NORTH
CENTRE-SOUTH
ITA12Disciplinary
GroupIndicat
orNORTH CENTRE SOUTH
CENTRE-NORTH
CENTRE-SOUTH
ITA12
vet
I 405 278 336 683 336 1019
dif
I 0 2 58 0 60 60
L 183 63 39 246 39 285 L 0 0 0 0 0 0
T 48 28 17 76 17 93 T 0 0 0 0 0 0
R 45.19 22.66 11.61 36.02 11.61 27.97 R 0.00 0.00 0.00 0.0P 26.23 44.44 43.59 30.89 43.59 32.63 P
Q 11.85 10.07 5.06 11.13 5.06 9.13 Q 0.00 0.00 0.00 0.0
PT 1005.00 1029.25 844.18 1013.93 844.18 982.90 PT
SRQ 1068.27 1070.29 829.00 1069.01 829.00 1025.14 SRQ
TECO 1036.67 1049.93 836.71 1041.55 836.71 1004.11 TECO
Indicator NORTH CENTRE SOUTHCENTRE-NORTH
CENTRE-SOUTH
ITA12
Column Totals
I 30476 55943 336 57879 63736 121615
L 5446 11990 35196 10548 11324 21872
T 1953 2899 4436 3012 2841 5853
R 17.87 21.43 1001 18.22 17.77 17.98
P 35.86 24.18 12.6 28.56 25.09 26.76
Q 6.41 5.18 22.57 5.2 4.46 4.81
PT 1027.53 993.06 963.2 1026.38 970.91 999.46
SRQ 1020.37 1000.94 954.45 1027.64 969.62 999.48
TECO1024.01
(**)997.07
958.90 (**)
1027.07 (**)
970.34 (**)
999.53
PT, SRQ, TECO: mean scores obtained for the PT module, the SRQ module, and the TECO test as a whole.
(**): TECO result significantly different versus ITA12 (95% confidence interval)calculated for the Geographic Areas NORTH, CENTRE and SOUTH and for ITA12
Source: The 12 universities participating in the pilot test. For L, the source is the National Student Register (CINECA ANS)
The yellow highlighting indicates groups that do not reach the threshold level of 30 tested students. For these groups, the significance of the difference in the TECO score versus ITA12 was therefore not tested.
The dark grey highlighting indicates the Disciplinary Groups for which there is a national admission test.
TABLE 1.2: Enrolled students, eligible students and tested students, per Disciplinary Group
Disciplinary Group
Number of students Column percentage
I L T I L T
agr.al 3239 361 139 2.66 1.65 2.37
arch 3056 1136 272 2.51 5.19 4.65
art 1971 412 64 1.62 1.88 1.09
bio 6289 805 256 5.17 3.68 4.37
chim 1365 278 106 1.12 1.27 1.81
comun 3065 510 131 2.52 2.33 2.24
cult 3238 523 142 2.66 2.39 2.43
econ 12047 1350 465 9.91 6.17 7.94
farm 5996 1108 394 4.93 5.07 6.73
filo 1730 422 108 1.42 1.93 1.85
form 3306 431 128 2.72 1.97 2.19
geo 1606 252 53 1.32 1.15 0.91
giu 16347 4319 874 13.44 19.75 14.93
ing 10551 1219 463 8.68 5.57 7.91
lett 4002 681 190 3.29 3.11 3.25
ling 7104 1173 231 5.84 5.36 3.95
mat.fis.stat 5295 829 388 4.35 3.79 6.63
med 8149 2100 393 6.70 9.60 6.71
odon 546 244 44 0.45 1.12 0.75
polit 5606 678 201 4.61 3.10 3.43
psic 4073 1108 191 3.35 5.07 3.26
sociol 2520 368 79 2.07 1.68 1.35
sto 1344 235 57 1.11 1.07 0.97
terr 8091 1045 391 6.65 4.78 6.68
vet 1019 285 93 0.84 1.30 1.59
dif 60 0 0 0.05 0.00 0.00
ITA12 121615 21872 5853 100 100 100
Source: See TABLE 3
0
5
10
15
20
25
agr.
al
arch ar
t
bio
chim
com
un
cult
eco
n
farm filo
form ge
o
giu
ing
lett
ling
mat
.fis
.sta
t
med
od
on
po
lit
psi
c
soci
ol
sto
terr
vet
dif
% o
n IT
A1
2
I L T
136
TABLE 1.3: Enrolled students, eligible students and tested students, per Macro-group
Macro-groupNumber of students Column percentage
I L T I L T
SAN 15710 3737 924 12.92 17.09 15.79
SC 38075 5688 2021 31.31 26.01 34.53
SOC 45176 8549 1979 37.15 39.09 33.81
H 22654 3898 929 18.63 17.82 15.87
ITA12 121615 21872 5853 100 100 100
Source: See TABLE 3
0
5
10
15
20
25
30
35
40
45
SAN SC SOC H
% o
n IT
A1
2
I L T
TABLE 1.4: Enrolled students, eligible students and tested students, per University
UniversityNumber of students Column percentage
I L T I L T
PO 1947 506 319 1.60 2.31 5.45
MI 13173 2574 798 10.83 11.77 13.63
PD 11916 1918 549 9.80 8.77 9.38
UD 3440 448 287 2.83 2.05 4.90
BO 16476 2645 368 13.55 12.09 6.29
FI 10927 2457 691 8.98 11.23 11.81
RM1 20637 5808 1657 16.97 26.55 28.31
RM2 7903 1080 183 6.50 4.94 3.13
NA 18990 2976 584 15.61 13.61 9.98
LE 4533 555 157 3.73 2.54 2.68
ME 5630 358 131 4.63 1.64 2.24
CA 6043 547 129 4.97 2.50 2.20
ITA12 121615 21872 5853 100 100 100
Source: See TABLE 3
0
5
10
15
20
25
30
PO MI PD UD BO FI RM1 RM2 NA LE ME CA
I L T
% o
n IT
A1
2
137
TABLE 1.5: Enrolled students, eligible students and tested students, per Geographic Area
Geographic AreaNumber of students Column percentage
I L T I L T
NORTH 30476 4892 1953 25.06 26.87 33.37
CENTRE 55943 9492 2899 46.00 52.14 49.53
SOUTH 35196 3821 1001 28.94 20.99 17.10
ITA12 121615 18205 5853 100 100 100
CENTRE-NORTH 57879 8877 3012 47.59 48.76 51.46
CENTRE-SOUTH 63736 9328 2841 52.41 51.24 48.54
ITA12 121615 18205 5853 100 100 100
Source: See TABLE 3
0
10
20
30
40
50
60
70
80
90
100
NORD CENTRO SUD
I L T
0
10
20
30
40
50
60
70
80
90
100
CENTRO-NORD CENTRO-SUD
I L T
% o
n IT
A1
2
% o
n IT
A1
2
TABLE 1.6: Percentage of eligible students per Disciplinary group, Macro-group, University and Geographic Area, broken down by participation in TECO
Disciplinary Group
% N % PRN % PRV % PRVA Macro-group % N % PRN % PRV % PRVA
agr.al 48.48 13.02 38.50 0.00 SAN 61.01 14.08 24.73 0.19arch 57.75 18.22 23.94 0.09 SC 47.12 17.05 35.53 0.30art 58.01 26.46 15.53 0.00 SOC 63.40 13.29 23.15 0.16bio 53.17 14.91 31.80 0.12 H 59.57 16.42 23.83 0.18
chim 43.53 17.99 38.13 0.36 ITA12 58.07 14.96 26.76 0.21comun 59.22 14.90 25.69 0.20
cult 58.13 14.34 27.15 0.38 University % N % PRN % PRV % PRVAecon 53.48 11.78 34.44 0.30 PO 24.70 11.86 63.04 0.40
farm 54.51 9.75 35.56 0.18 MI 58.08 10.68 31.00 0.23filo 57.11 17.06 25.59 0.24 PD 65.75 5.47 28.62 0.16
form 60.79 9.51 29.70 0.00 UD 29.02 6.70 64.06 0.22geo 59.13 19.84 21.03 0.00 BO 80.64 5.37 13.91 0.08giu 67.93 11.76 20.24 0.07 FI 59.34 12.29 28.12 0.24ing 43.40 18.46 37.98 0.16 RM1 41.53 29.72 28.53 0.22lett 51.98 19.97 27.90 0.15 RM2 71.11 11.94 16.94 0.00ling 65.56 14.49 19.69 0.26 NA 68.35 11.79 19.62 0.24
mat.fis.stat 36.91 15.68 46.80 0.60 LE 64.50 7.03 28.29 0.18med 64.52 16.57 18.71 0.19 ME 49.44 13.13 36.59 0.84
odon 63.52 18.44 18.03 0.00 CA 64.17 12.07 23.58 0.18polit 54.57 15.49 29.65 0.29 ITA12 58.07 14.96 26.76 0.21psic 64.71 17.87 17.24 0.18soc 64.95 13.04 21.47 0.54 Geographic Area % N % PRN % PRV % PRVAsto 62.13 13.19 24.26 0.43 NORTH 55.29 8.63 35.86 0.22terr 43.92 18.09 37.42 0.57 CENTRE 56.47 19.17 24.18 0.18vet 58.25 8.77 32.63 0.35 SOUTH 65.83 11.34 22.57 0.27
ITA12 58.07 14.96 26.76 0.21 CENTRE-NORTH 62.59 8.67 28.56 0.19CENTRE-SOUTH 53.87 20.82 25.09 0.22
Source: See TABLE 3
ITA12 58.07 14.96 26.76 0.21
138
TABLE 1.7: Percentage enrolled students, eligible students and tested students, per Disciplinary Group within each Geographic AreaColumn percentage of total Geographic Area Percentage of ITA12 (*)
Disciplinary GroupNORTH CENTRE SOUTH NORTH CENTRE SOUTH
I L T I L T I L T I L T I L T I L T
agr.al 5.99 3.82 4.45 1.37 0.97 1.24 1.84 0.83 1.60 1.50 0.95 1.49 0.63 0.53 0.62 0.53 0.17 0.27
arch 0.54 1.71 1.95 3.97 6.66 7.31 1.90 5.50 2.20 0.14 0.43 0.65 1.83 3.65 3.62 0.55 1.12 0.38
art 0.73 0.55 0.82 3.09 3.19 1.66 0.05 0.00 0.00 0.18 0.14 0.27 1.42 1.75 0.82 0.02 0.00 0.00
bio 4.37 5.36 7.27 4.64 3.58 2.76 6.71 1.89 3.40 1.09 1.34 2.43 2.14 1.96 1.37 1.94 0.38 0.58
chim 1.24 1.14 2.56 1.22 1.13 1.69 0.86 1.83 0.70 0.31 0.28 0.85 0.56 0.62 0.84 0.25 0.37 0.12
comun 3.13 2.06 3.02 2.43 2.70 2.04 2.14 1.67 1.30 0.78 0.51 1.01 1.12 1.48 1.01 0.62 0.34 0.22
cult 3.44 2.92 2.41 2.61 2.50 2.24 2.08 1.44 3.00 0.86 0.73 0.80 1.20 1.37 1.11 0.60 0.29 0.51
econ 5.32 4.26 7.58 11.79 7.44 8.83 10.88 5.09 6.09 1.33 1.06 2.53 5.42 4.08 4.37 3.15 1.03 1.04
farm 6.55 8.59 10.24 3.98 2.04 3.24 5.04 8.90 9.99 1.64 2.14 3.42 1.83 1.12 1.61 1.46 1.81 1.71
filo 2.10 2.26 2.15 1.24 1.53 1.90 1.12 2.59 1.10 0.53 0.56 0.72 0.57 0.84 0.94 0.32 0.53 0.19
form 2.03 1.96 2.46 3.25 1.64 1.83 2.48 2.86 2.70 0.51 0.49 0.82 1.49 0.90 0.91 0.72 0.58 0.46
geo 1.57 1.14 1.54 1.20 1.38 0.72 1.30 0.56 0.20 0.39 0.28 0.51 0.55 0.75 0.36 0.38 0.11 0.03
giu 14.30 25.17 19.25 10.74 12.94 10.24 16.98 31.47 20.08 3.58 6.27 6.42 4.94 7.10 5.07 4.92 6.38 3.43
ing 6.83 2.77 2.61 8.39 7.13 10.93 10.72 4.80 9.49 1.71 0.69 0.87 3.86 3.91 5.42 3.10 0.97 1.62
lett 3.51 2.85 3.99 2.95 4.00 3.14 3.65 1.04 2.10 0.88 0.71 1.33 1.36 2.19 1.55 1.05 0.21 0.36
ling 8.30 4.88 4.66 5.87 4.83 3.35 3.67 7.39 4.30 2.08 1.22 1.55 2.70 2.65 1.66 1.06 1.50 0.73
mat.fis.stat 5.58 2.50 3.74 4.53 5.01 10.14 3.02 2.07 2.10 1.40 0.62 1.25 2.08 2.75 5.02 0.87 0.42 0.36
med 6.03 9.59 5.27 7.75 10.08 5.73 5.62 8.34 12.39 1.51 2.39 1.76 3.56 5.52 2.84 1.63 1.69 2.12
odon 0.42 1.30 0.67 0.53 0.98 0.34 0.35 1.26 2.10 0.11 0.32 0.22 0.24 0.53 0.17 0.10 0.26 0.36
polit 6.23 4.04 3.89 4.01 3.11 2.86 4.15 1.92 4.20 1.56 1.01 1.30 1.85 1.71 1.42 1.20 0.39 0.72
psic 3.42 4.06 2.76 3.52 6.40 3.83 3.02 2.71 2.60 0.86 1.01 0.92 1.62 3.51 1.90 0.87 0.55 0.44
sociol 0.79 0.99 0.92 1.79 1.85 1.28 3.63 2.07 2.40 0.20 0.25 0.31 0.82 1.01 0.63 1.05 0.42 0.41
sto 1.52 1.03 1.08 1.34 1.23 1.00 0.37 0.72 0.70 0.38 0.26 0.36 0.62 0.67 0.50 0.11 0.15 0.12
terr 4.72 1.69 2.25 7.29 7.16 10.73 7.31 2.14 3.60 1.18 0.42 0.75 3.35 3.92 5.31 2.12 0.43 0.62
vet 1.33 3.36 2.46 0.50 0.53 0.97 0.95 0.88 1.70 0.33 0.84 0.82 0.23 0.29 0.48 0.28 0.18 0.29
dif 0.00 0.00 0.00 0.00 0.00 0.00 0.16 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.05 0.00 0.00
Column Total 100 100 100 100 100 100 100 100 100 25.05 24.92 33.36 45.99 54.81 49.55 28.96 20.27 17.09
Number of students 30476 5446 1953 55943 11990 2899 35196 4436 1001 30476 5446 1953 55943 11990 2899 35196 4436 1001
(*): The total of percentages ofITA12 corresponds to the percentage of students (I, L or T) in that Geographic Area in the sum total of ITA12. The sum of I in NORTH, CENTRE and SOUTH is equal to 100. The same goes for L and T.
The dark grey highlighting indicates the Disciplinary Groups for which there is a national admission test.
The light grey highlighting indicates the Disciplinary Groups for which there is a local admission test for more than 50% of the eligible students in ITA12 as a whole. The Territory Group is considered to be in the same situation: 49.94% of students in the ITA12 are enrolled in courses with a local admission test and 21.39% of the same students are enrolled in courses with a national admission test.
02468
101214161820222426
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n
farm filo
form ge
o
giu
ing
lett
ling
mat
.fis
.s…
med
od
on
po
lit
psi
c
soci
ol
sto
terr
vet
% d
i co
lon
na
su IT
A1
2
I L T
TABLE 1.8: Percentage enrolled, eligible and tested students per Disciplinary Group within the Geographic Area NORTH
Source: See TABLE 1.7
139
TABLE 1.9: Percentage enrolled, eligible and tested students per Disciplinary Group within the Geographic Area CENTRE
0
2
4
6
8
10
12
14
agr.
al
arch ar
t
bio
chim
com
un
cult
eco
n
farm filo
form ge
o
giu
ing
lett
ling
mat
.fis
.s…
me
d
od
on
po
lit
psi
c
soci
ol
sto
terr
vet
% d
i co
lon
na
su t
ota
le C
ENTR
O I L T
0
1
2
3
4
5
6
7
8
agr.
al
arch ar
t
bio
chim
com
un
cult
eco
n
farm filo
form ge
o
giu
ing
lett
ling
mat
.fis
.s…
med
od
on
po
lit
psi
c
soci
ol
sto
terr
vet
% d
i co
lon
na
su IT
A1
2
I L T
Source: See TABLE 1.7
TABLE 1.10: Percentage enrolled, eligible and tested students per Disciplinary Group within the Geographic Area SOUTH
0
5
10
15
20
25
30
35
agr.
al
arch ar
t
bio
chim
com
un
cult
eco
n
farm filo
form ge
o
giu
ing
lett
ling
mat
.fis
.s…
med
od
on
po
lit
psi
c
soci
ol
sto
terr
vet
% d
i co
lon
na
su t
ota
le S
UD I L T
0
1
2
3
4
5
6
7
agr.
al
arch ar
t
bio
chim
com
un
cult
eco
n
farm filo
form ge
o
giu
ing
lett
ling
mat
.fis
.s…
med
od
on
po
lit
psi
c
soci
ol
sto
terr
vet
% d
i co
lon
na
su IT
A1
2
I L T
Source: See TABLE 1.7
140
TABLE 1.11: Enrolled, eligible and tested students per University and Geographic Area, broken down by Gender
UniversityEnrolled students (I) Eligible students (L) Tested students (T)
# F+M % F % M # F+M % F % M # F+M % F % MPO 1947 61.79 38.21 506 69.96 30.04 319 69.59 30.41MI 13173 58.85 41.15 2574 61.03 38.97 798 61.9 38.1PD 11916 54.41 45.59 1918 63.35 36.65 549 64.12 35.88UD 3440 50.58 49.42 448 63.39 36.61 287 63.07 36.93BO 16476 56.90 43.10 2645 60.6 39.4 368 61.14 38.86FI 10927 57.66 42.34 2457 61.17 38.83 691 57.31 42.69
RM1 20637 59.06 40.94 5808 61.73 38.27 1657 55.16 44.84RM2 7903 51.49 48.51 1080 52.69 47.31 183 50.82 49.18NA 18990 57.94 42.06 2976 60.99 39.01 584 57.71 42.29LE 4533 64.75 35.25 555 69.37 30.63 157 60.51 39.49ME 5630 63.46 36.54 358 65.36 34.64 131 67.94 32.06CA 6043 58.91 41.09 547 63.99 36.01 129 58.14 41.86
ITA12 121615 58.44 41.56 21872 61.58 38.42 5853 59.34 40.66
Geographic AreaEnrolled students (I) Eligible students (L) Tested students (T)
# F+M % F % M # F+M % F % M # F+M % F % MNORTH 30476 39.09 28.13 5446 62.87 37.13 1953 63.95 36.05CENTRE 55943 43.22 32.1 11990 60.55 39.45 2899 56.16 43.84SOUTH 35196 59.87 40.13 4436 62.76 37.24 1001 59.54 40.46
CENTRE-NORTH 57879 34.27 24.61 10548 61.91 38.09 3012 62.08 37.92CENTRE-SOUTH 63736 58.57 41.43 11324 61.27 38.73 2841 56.42 43.58
ITA12 121615 58.44 41.56 21872 61.58 38.42 5853 59.34 40.66The percentages are per rowSource: See TABLE 3
TABLE 1.12: Mean age of eligible students broken down by Gender and percentage eligible students of female gender, per Disciplinary Group, Macro-group, University and Geographic Area Disciplinary
GroupMean age
% F Macro-groupMean age
% FF + M F M F + M F M
agr.al 23.30 23.19 23.37 37.95 SAN 23.51 23.41 23.67 62arch 23.31 23.16 23.52 59.77 SC 23.34 23.15 23.50 45.9art 23.83 23.51 24.76 74.76 SOC 23.70 23.52 24.03 64.95bio 23.26 23.04 23.55 58.14 H 23.87 23.62 24.71 76.63
chim 23.22 23.21 23.22 50.72 ITA12 23.60 23.45 23.85 61.58comun 24.85 24.27 25.89 63.73
cult 24.16 23.88 25.33 80.11University
Mean age % F
econ 23.15 23.10 23.21 50.59 F + M F Mfarm 23.35 23.16 23.89 74.37 PO 25.29 24.82 26.38 69.96filo 23.70 23.05 24.66 59.48 MI 23.35 23.25 23.52 61.03
form 24.81 24.53 31.65 96.06 PD 23.60 23.52 23.73 63.35geo 24.45 24.34 24.77 74.6 UD 23.41 23.08 23.97 63.39giu 23.29 23.18 23.47 64.14 BO 23.38 23.27 23.55 60.6ing 22.74 22.55 22.82 28.38 FI 23.32 23.20 23.50 61.17lett 23.00 22.92 23.23 75.92 RM1 23.86 23.61 24.26 61.73ling 23.68 23.49 24.72 84.06 RM2 23.59 23.63 23.54 52.69
mat.fis.stat 23.67 23.19 23.88 30.28 NA 23.07 22.95 23.26 60.99med 23.54 23.51 23.58 55.81 LE 24.41 24.08 25.14 69.37odon 23.69 23.61 23.76 47.13 ME 24.26 23.99 24.76 65.36polit 25.39 25.05 25.80 55.01 CA 24.79 24.53 25.23 63.99psic 23.73 23.50 24.89 83.21 ITA12 23.60 23.45 23.85 61.58soc 25.46 24.61 29.65 83.15sto 24.99 24.80 25.11 39.15
Geographic AreaMean age
% Fterr 23.92 23.52 24.41 55.02 F + M F Mvet 23.79 23.77 23.82 72.28 NORTH 23.62 23.49 23.85 62.87
ITA12 23.60 23.45 23.85 61.58 CENTRE 23.62 23.45 23.87 60.55SOUTH 23.55 23.39 23.80 62.76
CENTRE-NORTH 23.49 23.37 23.69 61.91CENTRE-SOUTH 23.71 23.53 24.00 61.27
Source: See TABLE 3 ITA12 23.60 23.45 23.85 61.58
141
TABLE 1.13: Eligible students’ mean diploma grade (*), mean age when diploma awarded and mean years since diploma awarded, per Disciplinary group, Macro-group, University and Geographic Area, broken down by Gender
Disciplinary Group
VMD Mean age when diploma awarded (**)
Mean years since diploma awarded (**)
Macro-groupVMD Mean age
when diploma awarded (**)
Mean years since diploma awarded (**)F+M F M F+M F M
agr.al 81.04 82.4 80.21 19.03 4.21 SAN 86.76 88.04 84.64 18.74 4.76
arch 81.53 82.4 80.27 18.73 4.57 SC 82.09 82.71 81.58 18.89 4.42
art 75.29 76.03 73.19 19.16 4.67 SOC 81.35 82.33 79.53 18.95 4.74
bio 78.75 81.45 75.05 18.97 4.28 H 79.27 79.82 77.5 18.99 4.87
chim 84.26 86.07 82.35 18.63 4.59 ITA12 82.1 82.84 80.91 18.9 4.68
comun 76.75 77.59 75.27 19.2 5.6
cult 77.65 78.35 74.8 18.95 5.21University
VMD Mean age when diploma awarded (**)
Mean years since diploma awarded (**)econ 82.1 84.39 79.75 18.93 4.21 F+M F M
farm 84.26 85.79 79.89 18.66 4.69 PO 82.56 83.48 80.42 19.36 5.93
filo 82.86 84.37 80.65 18.88 4.8 MI 81.41 82.58 79.57 19.07 4.29
form 77.21 77.27 75.71 19.37 5.45 PD 84.9 85.28 84.25 19.13 4.43
geo 77.43 78.41 74.44 19.45 5 UD 83.53 84.68 81.51 19.18 4.23
giu 82.94 84.02 81.01 18.76 4.52 BO 83.99 85.17 82.16 19.06 4.28
ing 85.45 86.1 85.19 18.8 3.94 FI 82.9 83.29 82.29 19.09 4.23
lett 82.19 82.89 79.98 18.98 4.02 RM1 75.63 75.91 75.19 19.02 4.81
ling 79.29 79.8 76.61 18.82 4.84 RM2 84 85.62 82.22 19.04 4.54
mat.fis.stat 83.36 85.28 82.53 18.94 4.63 NA 86.56 87.66 84.84 17.81 5.26
med 88.74 90.27 86.8 18.77 4.76 LE 86.44 86.51 86.27 19.44 4.97
odon 85.01 87.68 82.73 18.75 4.96 ME 87.22 88.04 85.66 18.86 5.4
polit 78.26 79.53 76.72 19.3 6.03 CA 86.23 86.63 85.54 19.1 5.64
psic 79.77 80.22 77.53 19.06 4.67 ITA12 82.1 82.84 80.91 18.9 4.68
soc 78.19 78.83 75 19.55 5.84
sto 78.93 80.68 77.85 18.97 5.91 Geographic Area
VMD Mean age when diploma awarded (**)
Mean years since diploma awarded (**)terr 80.29 80.36 80.2 19.09 4.79 F+M F M
vet 83.89 85.03 80.78 18.89 4.88 NORTH 82.92 83.8 81.42 19.04 4.55
ITA12 82.1 82.84 80.91 18.9 4.68 CENTRE 79.91 80.44 79.09 19.13 4.49
Source: See TABLE 3 SOUTH 86.56 87.4 85.13 18.26 5.28
(*): The mean high school diploma grade (VMD) is calculated in all slides and in the Appendix based on all grades out of 100, assigning 110 to “cum laude” grades.
CENTRE-NORTH
83.17 84.01 81.81 19.1 4.38
CENTRE-SOUTH 81.05 81.68 80.06 18.72 4.97
(**): data are in years; “dipl” refers to diploma ITA12 82.1 82.84 80.91 18.9 4.68
TABLE 1.14: Percentage of eligible students per Disciplinary group, Macro-group, University and Geographic Area, broken down by type of diploma
Disciplinary Group
% Tech/Prof
% Classical%
Scientific% Other Lyceum
% Other Institute
% No data
% TotalMacro-group
% Tech/Prof
% Classical%
Scientific% Other Lyceum
% Other Institute
% No data%
Totalagr.al 43.77 6.37 35.73 3.05 9.14 1.94 100 SAN 6.45 26.73 54.86 3.56 3.77 4.63 100arch 9.68 19.63 53.87 5.28 5.11 6.43 100 SC 17.49 13.41 52.87 4.36 5.59 6.28 100art 11.65 27.43 28.16 14.56 8.25 9.95 100 SOC 16.31 31.24 32.12 9.88 5.38 5.06 100bio 18.26 14.91 50.93 7.45 5.22 3.23 100 H 11.34 30.63 26.65 17.27 6.7 7.41 100
chim 19.78 16.55 48.2 2.88 5.04 7.55 100 ITA12 14.05 25.73 40.43 8.68 5.4 5.72 100comun 18.82 18.43 28.63 13.92 7.65 12.55 100
cult 8.8 39.2 22.37 16.25 7.84 5.54 100 University%
Tech/Prof% Classical
% Scientific
% Other Lyceum
% Other Institute
% No data%
Totalecon 31.26 12.74 40.89 4.37 5.7 5.04 100 PO 27.87 16.8 32.41 12.25 9.29 1.38 100farm 11.64 19.77 54.6 5.96 4.69 3.34 100 MI 16.12 27.62 38.5 10.92 5.09 1.75 100filo 5.92 41 33.18 9.95 5.92 4.03 100 PD 20.13 19.29 42.7 11.68 5.37 0.83 100
form 19.95 11.37 15.55 37.59 12.3 3.25 100 UD 27.46 12.95 37.95 9.15 10.27 2.23 100geo 46.03 9.92 14.29 13.1 3.97 12.7 100 BO 14.14 22.34 45.9 7.03 6.01 4.57 100giu 10.42 42.6 32.23 7.43 4.72 2.59 100 FI 15.79 18.15 40.05 10.95 12.13 2.93 100ing 13.7 10.17 66.28 1.31 3.28 5.25 100 RM1 8.76 29.8 38.65 5.94 1.5 15.34 100lett 5.29 48.75 27.02 9.99 3.38 5.58 100 RM2 14.07 24.44 47.04 5.56 6.67 2.22 100ling 13.81 20.46 28.47 19.78 5.88 11.59 100 NA 10.92 32.49 41.77 8.47 5.38 0.97 100
mat.fis.stat
21.95 9.17 56.21 1.57 3.26 7.84 100 LE 20.18 29.55 28.83 13.87 5.95 1.62 100
med 3.05 31.29 54.76 2.19 3 5.71 100 ME 11.73 31.56 36.59 10.61 5.31 4.19 100odon 5.74 25 58.61 2.87 3.69 4.1 100 CA 19.2 23.22 38.94 11.7 4.57 2.38 100polit 20.94 26.4 25.07 12.39 5.31 9.88 100 ITA12 14.05 25.73 40.43 8.68 5.4 5.72 100psic 8.48 26.26 34.66 18.68 5.6 6.32 100
soc 23.1 20.38 20.92 21.2 8.97 5.43 100Geographi
c Area%
Tech/Prof% Classical
% Scientific
% Other Lyceum
% Other Institute
% No data%
Totalsto 12.77 34.47 31.06 8.94 6.38 6.38 100 NORTH 19.56 22.48 39.37 11.16 6 1.43 100terr 16.65 14.07 42.58 7.18 9.95 9.57 100 CENTRE 11.87 25.29 41.29 7.17 5.14 9.24 100vet 11.93 21.75 53.33 4.91 5.96 2.11 100 SOUTH 13.17 30.91 39.38 9.72 5.34 1.49 100
ITA12 14.05 25.73 40.43 8.68 5.4 5.72 100CENTRE-NORTH
17.32 21.44 41.16 10.08 7.43 2.57 100
Number of eligible students, broken down by GenderCENTRE-SOUTH
10.99 29.72 39.74 7.38 3.5 8.66 100
Gender Tech/Prof Classical ScientificOther
LyceumOther
instituteNo
dataITA12 ITA12 14.05 25.73 40.43 8.68 5.4 5.72 100
F+M 3072 5627 8842 1899 1180 1252 21872
F 1472 3980 4868 1577 810 761 13468
M 1600 1647 3974 322 370 491 8404
Source: See TABLE 3
142
TABLE 1.15: Percentage of eligible students who do not speak Italian at home (*) per Disciplinary Group, Macro-group, University and Geographic Area, broken
down by GenderDisciplinary Group % F+M % F % M Macro-group % F+M % F % M
agr.al 1.94 0.83 1.11 SAN 2.22 1.34 0.88arch 2.46 1.32 1.14 SC 3.06 1.42 1.64art 4.61 3.40 1.21 SOC 3.40 2.12 1.29bio 2.61 1.86 0.75 H 3.18 2.10 1.08
chim 5.04 1.44 3.60 ITA12 3.07 1.80 1.27comun 4.12 2.55 1.57
cult 2.68 2.29 0.38 University % F+M % F % Mecon 5.41 2.67 2.74 PO 4.94 3.16 1.78farm 2.89 2.08 0.81 MI 2.10 1.01 1.09filo 3.32 0.95 2.37 PD 1.62 0.99 0.63
form 2.09 2.09 0.00 UD 6.70 4.69 2.01geo 3.97 1.59 2.38 BO 1.44 0.87 0.57giu 2.76 1.81 0.95 FI 2.77 1.67 1.10ing 3.28 1.07 2.21 RM1 5.11 3.01 2.10lett 3.38 2.20 1.17 RM2 2.50 1.02 1.48ling 2.90 2.22 0.68 NA 2.45 1.44 1.01
mat.fis.stat 2.29 0.72 1.57 LE 2.70 1.80 0.90med 1.95 1.05 0.90 ME 1.68 1.12 0.56odon 2.05 0.82 1.23 CA 1.46 0.91 0.55polit 5.31 3.24 2.06 ITA12 3.07 1.80 1.27psic 2.26 2.08 0.18soc 2.45 1.63 0.82 Geographic Area % F+M % F % Msto 3.83 0.43 3.40 NORTH 2.57 1.51 1.07terr 4.31 2.39 1.91 CENTRE 3.59 2.09 1.50vet 1.75 1.05 0.70 SOUTH 2.30 1.40 0.90
ITA12 3.07 1.80 1.27 CENTRE-NORTH 2.33 1.38 0.95CENTRE-SOUTH 3.76 2.19 1.57
Source: See TABLE 3 ITA12 3.07 1.80 1.27(*): No data was treated as “Language spoken at home = Italian”
TABLE 1.16: Distribution of eligible students according to distance from place of residence and University attended, per Disciplinary group, Macro-group, University and Geographic Area
Disciplinary Group % dist0 % dist1 % dist2 % dist3 Total Macro-group % dist0 % dist1 % dist2 % dist3 Totalagr.al 11.46 22.93 55.41 10.19 100 SAN 19.02 17.62 49.43 13.94 100
arch 22.51 16.52 45.30 15.67 100 SC 23.82 17.60 46.81 11.76 100
art 29.67 15.38 40.66 14.29 100 SOC 19.65 22.01 44.30 14.05 100
bio 16.34 21.24 49.02 13.40 100 H 20.80 20.80 45.78 12.62 100
chim 21.48 19.26 52.59 6.67 100 ITA12 21.22 19.57 46.22 12.99 100
comun 19.63 20.25 41.10 19.02 100
cult 27.55 18.88 46.94 6.63 100 University % dist0 % dist1 % dist2 % dist3 Totalecon 20.51 19.15 44.79 15.56 100 PO 5.34 3.86 67.66 23.15 100
farm 13.02 20.31 54.08 12.58 100 MI 16.53 17.99 55.54 9.94 100
filo 18.80 15.79 42.86 22.56 100 PD 6.47 24.02 57.07 12.44 100
form 12.93 27.89 54.42 4.76 100 UD 10.59 31.78 36.45 21.18 100
geo 13.11 21.31 40.98 24.59 100 BO 15.01 12.70 40.18 32.10 100
giu 20.33 24.70 44.12 10.85 100 FI 19.09 21.55 50.00 9.37 100
ing 25.92 17.60 46.24 10.24 100 RM1 30.84 11.52 42.95 14.69 100
lett 18.45 19.74 47.21 14.59 100 RM2 34.64 17.50 40.71 7.14 100
ling 19.79 23.61 43.40 13.19 100 NA 22.96 33.25 41.56 2.24 100
mat.fis.stat 29.07 14.99 45.16 10.79 100 LE 9.25 64.16 24.86 1.73 100
med 26.13 16.17 45.86 11.84 100 ME 26.40 18.54 33.15 21.91 100
odon 20.00 12.73 47.27 20.00 100 CA 17.83 42.04 39.49 0.64 100
polit 20.55 18.58 49.01 11.86 100 ITA12 21.22 19.57 46.22 12.99 100
psic 16.59 19.65 41.92 21.83 100
soc 15.05 25.81 45.16 13.98 100 Geographic Area % dist0 % dist1 % dist2 % dist3 Totalsto 24.00 21.33 41.33 13.33 100 NORTH 11.27 19.45 55.02 14.27 100
terr 25.38 16.79 44.85 12.98 100 CENTRE 26.85 14.23 44.01 14.91 100
vet 7.92 15.84 48.51 27.72 100 SOUTH 20.93 36.49 37.84 4.74 100
ITA12 21.22 19.57 46.22 12.99 100 CENTRE-NORTH 13.65 19.12 51.95 15.28 100
CENTRE-SOUTH 27.93 19.96 41.15 10.96 100
Source: See TABLE 3 ITA12 21.22 19.57 46.22 12.99 100
dist0: place of residence in the same municipality as University attended dist1: place of residence in a different municipality than University attended but in the same provincedist2: place of residence in a different province than University attended but in the same regiondist3: place of residence in a different region than University attended
143
TABLE 2.2: Regularity (R) and participation (P) indices per Disciplinary group, broken down by Gender
Source: See TABLE A-2.1
5
10
15
20
25
30
35
40
45
50
R
F M
5
15
25
35
45
55
65
P
TABLE 2.5: Regularity and participation indices per Geographic Area, broken down
by Gender
Geographic Area
I L T R P Q
NORTH 30476 5446 1953 17.87 35.86 6.41
CENTRE 55943 11990 2899 21.43 24.18 5.18
SOUTH 35196 4436 1001 12.60 22.57 2.84
CENTRE-NORTH 57879 10548 3012 18.22 28.56 5.20
CENTRE-SOUTH 63736 11324 2841 17.77 25.09 4.46
ITA1212161
521872 5853 17.98 26.76 4.81
Source: See TABLE 10. For the breakdown by Gender, see TABLE A-2.1
NORTH
CENTRE
SOUTH
CENTRE-NORTH
CENTRE-SOUTH
ITA12
-6
-4
-2
0
2
4
6
8
10
-6 -4 -2 0 2 4
R
P
10
12
14
16
18
20
22
24
R
F M
15
20
25
30
35
40
P
Breakdown by Gender
144
TABLE 2.6: Numberof pre-registered students, pre-registration index (PR) and participation index for pre-registered students (TP), per Disciplinary group, Macro-group, University and Geographic Area
Disciplinary Group
Pre-registered students (#)
PR TP Macro-groupPre-registered
students (#)PR TP
agr.al 186 51.52 74.73 SAN 1457 38.99 63.42
arch 480 42.25 56.67 SC 3008 52.88 67.19
art 173 41.99 36.99 SOC 3129 36.60 63.25
bio 377 46.83 67.90 H 1576 40.43 58.95
chim 157 56.47 67.52 ITA12 9170 41.93 63.83
comun 208 40.78 62.98
cult 219 41.8764.84
UniversityPre-registered
students (#)PR TP
econ 628 46.52 74.04 PO 381 75.30 83.73
farm 504 45.49 78.17 MI 1079 41.92 73.96
filo 181 42.89 59.67 PD 657 34.25 83.56
form 169 39.21 75.74 UD 318 70.98 90.25
geo 103 40.87 51.46 BO 512 19.36 71.88
giu 1385 32.07 63.10 FI 999 40.66 69.17
ing 690 56.60 67.10 RM1 3396 58.47 48.79
lett 327 48.02 58.10 RM2 312 28.89 58.65
ling 404 34.44 57.18 NA 942 31.65 62.00
mat.fis.stat 523 63.09 74.19 LE 197 35.50 79.70
med 745 35.48 52.75 ME 181 50.56 72.38
odon 89 36.48 49.44 CA 196 35.83 65.82
polit 308 45.43 65.26 ITA12 9170 41.93 63.83
psic 391 35.29 48.85
soc 129 35.0561.24
Geographic AreaPre-registered
students (#)PR TP
sto 89 37.87 64.04 NORTH 2435 44.71 80.21
terr 586 56.08 66.72 CENTRE 5219 43.53 55.55
vet 119 41.75 78.15 SOUTH 1516 34.17 66.03
ITA12 9170 41.93 63.83 CENTRE-NORTH 3946 37.41 76.33
CENTRE-SOUTH 5224 46.13 54.38
Source: See TABLE 3 ITA12 9170 41.93 63.83
TABLE A-2.1: Enrolled, graduating and tested students, regularity (R) and participation (P) indices per Disciplinary group, Macro-group, University and Geographic Area, broken down by Gender
Disciplinary Group
F MMacro-group
F M
I L T R P I L T R P I L T R P I L T R P
agr.al 1438 137 57 9.53 41.61 1801 224 82 12.44 36.61 SAN 9807 2317 575 23.63 24.82 5903 1420 349 24.06 24.58
arch 1731 679 170 39.23 25.04 1325 457 102 34.49 22.32 SC 15303 2611 918 17.06 35.16 22772 3077 1103 13.51 35.85
art 1371 308 48 22.47 15.58 600 104 16 17.33 15.38 SOC 28007 5553 1275 19.83 22.96 17169 2996 704 17.45 23.5
bio 3839 468 169 12.19 36.11 2450 337 87 13.76 25.82 H 17066 2987 705 17.5 23.6 5588 911 224 16.3 24.59
chim 614 141 45 22.96 31.91 751 137 61 18.24 44.53 ITA12 70183 13468 3473 19.19 25.79 51432 8404 2380 16.34 28.32
comun 1987 325 79 16.36 24.31 1078 185 52 17.16 28.11
cult 2546 419 110 16.46 26.25 692 104 32 15.03 30.77University
F M
econ 5962 683 245 11.46 35.87 6085 667 220 10.96 32.98 I L T R P I L T R P
farm 4352 824 288 18.93 34.95 1644 284 106 17.27 37.32 PO 1203 354 222 29.43 62.71 744 152 97 20.43 63.82
filo 933 251 71 26.9 28.29 797 171 37 21.46 21.64 MI 7752 1571 494 20.27 31.44 5421 1003 304 18.5 30.31
form 3044 414 124 13.6 29.95 262 17 4 6.49 23.53 PD 6484 1215 352 18.74 28.97 5432 703 197 12.94 28.02
geo 1214 188 39 15.49 20.74 392 64 14 16.33 21.88 UD 1740 284 181 16.32 63.73 1700 164 106 9.65 64.63
giu 10510 2770 576 26.36 20.79 5837 1549 298 26.54 19.24 BO 9375 1603 225 17.1 14.04 7101 1042 143 14.67 13.72
ing 2393 346 127 14.46 36.71 8158 873 336 10.7 38.49 FI 6300 1503 396 23.86 26.35 4627 954 295 20.62 30.92
lett 2971 517 141 17.4 27.27 1031 164 49 15.91 29.88 RM1 12189 3585 914 29.41 25.5 8448 2223 743 26.31 33.42
ling 5775 986 189 17.07 19.17 1329 187 42 14.07 22.46 RM2 4069 569 93 13.98 16.34 3834 511 90 13.33 17.61
mat.fis.stat 1656 251 141 15.16 56.18 3639 578 247 15.88 42.73 NA 11003 1815 337 16.5 18.57 7987 1161 247 14.54 21.27
med 4491 1172 205 26.1 17.49 3658 928 188 25.37 20.26 LE 2935 385 95 13.12 24.68 1598 170 62 10.64 36.47
odon 250 115 15 46 13.04 296 129 29 43.58 22.48 ME 3573 234 89 6.55 38.03 2057 124 42 6.03 33.87
polit 3106 373 113 12.01 30.29 2500 305 88 12.2 28.85 CA 3560 350 75 9.83 21.43 2483 197 54 7.93 27.41
psic 3216 922 159 28.67 17.25 857 186 32 21.7 17.2 ITA12 70183 13468 3473 19.19 25.79 51432 8404 2380 16.34 28.32
soc 2084 306 72 14.68 23.53 436 62 7 14.22 11.29
sto 507 92 20 18.15 21.74 837 143 37 17.08 25.87Geographic Area
F M
terr 3477 575 203 16.54 35.3 4614 470 188 10.19 40 I L T R P I L T R P
vet 714 206 67 28.85 32.52 305 79 26 25.9 32.91 NORTH 17179 3424 1249 19.93 36.48 13297 2022 704 15.21 34.82
ITA12 70183 13468 3473 19.19 25.79 51432 8404 2380 16.34 28.32 CENTRE 31933 7260 1628 22.74 22.42 24010 4730 1271 19.7 26.87
SOUTH 21071 2784 596 13.21 21.41 14125 1652 405 11.7 24.52
CENTRE-NORTH 32854 6530 1870 19.88 28.64 25025 4018 1142 16.06 28.42
CENTRE-SOUTH 37329 6938 1603 18.59 23.1 26407 4386 1238 16.61 28.23
Source: See TABLE 3 ITA12 70183 13468 3473 19.19 25.79 51432 8404 2380 16.34 28.32
145
TABLE 3.10: Scores obtained in PT, SRQ and their components within the ITA12 quartiles
Test componentPT APS WE WM SRQ CRE CA SQR
Minimum score 3 0 0 0 0 0 0 0
Lowest score of the 2nd quartile 7 2 2 3 11 5 3 2
Lowest score of the 3rd quartile (median) 9 3 3 3 13 6 3 3
Mean score 9.17 2.89 2.98 3.29 12.31 5.70 3.35 3.26
Lowest score of the 4th quartile 11 4 4 4 14 7 4 4
Maximum score 18 6 6 6 19 8 5 7
Variation coefficient (CV) 0.321 0.377 0.371 0.331 0.231 0.286 0.336 0.410
The quartiles are calculated within each test component
Source: See TABLE 3
TABLE 3.11: TECO scores in the quartiles, per Disciplinary Group
agr.al
arch art bio chimcomun
cult econ farm filo form geo giu ing lett ling m.f.s medodo
npolit psic soc sto terr vet
ITA12
Minimum score (*) 7 8 3 9 5 7 10 4 7 11 3 9 4 9 10 9 9 7 7 9 8 10 6 5 12 3
Lowest score of the 2nd quartile 18 19 18 19 19 18 19 18 18 20 16 17 19 19 19 18 19 21 19 19 20 17 19 16 19 16
Lowest score of the 3rd quartile (median)
22 22 21 22 22 22 21 22 21 22 18 21 22 22 22 21 23 24 22 22 22 21 22 20 22 18
Mean score 21.03 21.66 20.48 21.68 21.35 20.85 20.86 21.23 20.78 22.03 18.70 19.60 21.78 21.52 21.87 21.08 22.69 23.58 21.93 21.68 22.35 20.29 21.82 19.63 21.60 21.48
Lowest score of the 4th quartile 25 24 24 25 25 24 23 24 24 26 22 24 25 24 25 24 26 27 26 25 25 24 25 23 25 22
Maximum score 33 33 29 33 31 32 32 32 34 34 31 28 34 34 34 32 35 36 33 34 31 31 36 32 33 36
Variation coefficient (CV) 0.228 0.185 0.228 0.215 0.206 0.217 0.188 0.214 0.228 0.199 0.244 0.243 0.212 0.198 0.195 0.203 0.214 0.184 0.229 0.219 0.165 0.234 0.243 0.237 0.221 0.215
(*): The minimum score should not be confused with the minimum for a “pass grade”, which is 1/2 of the distance between the theoretical maximum and minimum scores (respectively 38 and 3) increased by ε. The pass grade is hence any score higher than 20.5
The quartiles are calculated within each Disciplinary Group m.f.s = mat.fis.stat
3
8
13
18
23
28
33
38
agr.al art chim cult farm form giu lett m.f.s odon psic sto vet
pu
nte
ggio
Punteggio più basso del 2° quartile Punteggio minimo Punteggio più basso del 3° quartile (mediana)
Punteggio medio Punteggio massimo Punteggio più basso del 4° quartile
146
TABLE 3.12: TECO scores in the quartiles, per Macro-group
SAN SC SOC H ITA12
Minimumscore(*) 7 5 4 3 3
Lowest score of the 2nd quartile 19 18 19 18 18
Lowest score of the 3rd quartile (median) 22 22 22 21 21
Mean score 22.11 21.36 21.54 20.97 21.48
Lowest score of the 4th quartile 26 24 25 24 24
Maximum score 36 35 34 36 36
Variation coefficient (CV) 0.215 0.216 0.211 0.215 0.215
(*): The minimum score should not be confused with the minimum for a “pass grade”, which is 1/2 of the distance between the theoretical maximum and minimum scores (respectively 38 and 3) increased by ε. The pass grade is hence any score higher than 20.5
The quartiles are calculated within each Macro-group.
3
8
13
18
23
28
33
38
SAN SC SOC H ITA12
pu
nte
ggio
Punteggio più bassodel 2° quartile
Punteggio minimo
Punteggio più bassodel 3° quartile(mediana)
Punteggio medio
Punteggio massimo
TABLE 3.13: TECO scores in quartiles per University
PO MI PD UD BO FI RM1 RM2 NA LE ME CA ITA12
Minimum score(*) 4 8 3 9 6 8 4 7 5 3 7 10 3
Lowest score of the 2nd quartile 18 20 19 20 20 19 18 17 18 17 16 18 16
Lowest score of the 3rd quartile (median) 21 23 22 23 23 22 21 21 21 20 19 22 19
Mean score 21.36 22.49 22.23 22.20 22.87 22.21 20.80 20.95 20.49 19.75 19.34 21.09 21.48
Lowest score of the 4th quartile 25 26 25 25 26 25 24 25 24 23 22 24 22
Maximum score 33 34 34 34 34 36 36 34 33 31 32 32 36
Variation coefficient (CV) 0.202 0.202 0.207 0.184 0.19 0.2 0.22 0.227 0.232 0.239 0.226 0.211 0.215
(*): The minimum score should not be confused with the minimum for a “pass grade”, which is 1/2 of the distance between the theoretical maximum and minimum scores (respectively 38 and 3) increased by ε. The pass grade is hence any score higher than 20.5
The quartiles are calculated within each University.
3
8
13
18
23
28
33
38
PO MI PD UD BO FI RM1 RM2 NA LE ME CA ITA12
pu
nte
ggio
Punteggio più basso del2° quartile
Punteggio minimo
Punteggio più basso del3° quartile (mediana)
Punteggio medio
Punteggio massimo
Punteggio più basso del4° quartile
147
TABLE 3.14: TECO scores in the quartiles, per Geographic Area
NORTH CENTRE SOUTHCENTRE-NORTH
CENTRE-SOUTH
ITA12
Minimumscore(*) 3 4 3 3 3 3
Lowest score of the 2nd quartile 19 18 17 19 18 18
Lowest score of the 3rd quartile (median) 22 22 20 22 21 21
Mean score 22.19 21.41 20.30 22.28 20.63 21.48
Lowest score of the 4th quartile 25 24 24 25 24 24
Maximum score 34 36 33 36 36 36
Variation coefficient (CV) 0.201 0.215 0.231 0.2 0.224 0.215
(*): The minimum score should not be confused with the minimum for a “pass grade”, which is 1/2 of the distance between the theoretical maximum and minimum scores (respectively 38 and 3) increased by ε. The pass grade is hence any score higher than 20.5
The quartiles are calculated within each Geographic Area
3
8
13
18
23
28
33
38
NORD CENTRO SUD C-N C-S ITA12
pu
nte
ggio
Punteggio più basso del 2°quartile
Punteggio minimo
Punteggio più basso del 3°quartile (mediana)
Punteggio medio
Punteggio massimo
Punteggio più basso del 4°quartile
TABLE 3.15: Maximum TECO results in each percentileTens/Hundreds
0 1 2 3 4 5 6 7 8 9 10
U
n
i
t
s
0 - 807 878 978 1016 1087 1123 1191 1500
1 599 810 879 916 981 1017 1052 1126 1193 -
2 639 811 880 944 982 1088 1154 1223 -
3 674 812 881 947 983 1018 1053 1089 1155 1224 -
4 707 841 948 1092 1226 -
5 739 844 883 949 984 1019 1054 1119 1156 1257 -
6 742 845 911 1020 1055 1120 1157 1260 -
7 773 846 913 950 985 1021 1085 1121 1186 1292 -
8 776 847 914 986 1048 1086 1189 1325 -
9 777 873 915 951 1014 1051 1122 1190 1360 -
How to read this table - Example Percentile 34: take the cell at the intersection of column 3 (tens) and row 4 (units). The result shown in this cell is the maximum score obtained within the 34th percentileIf a cell is empty, the maximum score obtained within that percentile is shown in the first non-empty previous cell
148
NORTH: MAXIMUM RESULT IN THE PERCENTILE
Tens
0 1 2 3 4 5 6 7 8 9 10
U
n
i
t
s
0 - 813 909 949 984 1020 1057 1120 1155 1223 1500
1 635 842 911 950 985 1021 1085 1120 1156 1224 -
2 674 845 913 950 986 1050 1085 1121 1157 1225 -
3 708 846 914 951 1016 1051 1086 1121 1158 1227 -
4 741 847 915 981 1016 1052 1087 1122 1188 1258 -
5 773 876 916 982 1017 1052 1087 1122 1189 1259 -
6 776 878 942 982 1018 1052 1088 1123 1190 1292 -
7 779 879 947 983 1018 1053 1088 1126 1191 1294 -
8 810 880 948 983 1018 1053 1089 1153 1191 1327 -
9 811 881 949 983 1019 1054 1119 1154 1219 1360 -
CENTRE: MAXIMUM RESULT IN THE PERCENTILE
Tens
0 1 2 3 4 5 6 7 8 9 10
U
n
i
t
s
0 - 779 878 915 1016 1051 1086 1122 1190 1500
1 599 809 916 981 1017 1087 1123 1192 -
2 666 811 879 917 982 1052 1153 1222 -
3 674 880 945 983 1018 1088 1155 1224 -
4 708 814 881 948 1053 1226 -
5 741 842 882 984 1090 1156 1257 -
6 742 845 910 949 1019 1054 1119 1157 1260 -
7 773 846 913 985 1020 1055 1120 1158 1292 -
8 776 950 986 1021 1083 1121 1188 1326 -
9 847 914 951 1012 1048 1085 1189 1361 -
SOUTH: MAXIMUM RESULT IN THE PERCENTILE
Tens
0 1 2 3 4 5 6 7 8 9 10
U
n
i
t
s
0 - 744 842 880 916 951 986 1052 1088 1156 1395
1 569 775 844 880 916 979 1013 1052 1090 1157 -
2 600 776 845 881 920 981 1016 1052 1119 1189 -
3 633 776 846 882 947 982 1017 1053 1120 1190 -
4 639 779 846 885 948 983 1017 1054 1121 1191 -
5 674 809 847 911 948 983 1018 1083 1122 1218 -
6 704 810 849 913 949 983 1019 1086 1122 1224 -
7 707 811 876 914 950 984 1020 1087 1126 1258 -
8 739 811 878 914 950 984 1050 1087 1155 1292 -
9 742 813 879 915 951 985 1051 1087 1155 1327 -
CENTRE-NORTH: MAXIMUM RESULT IN THE PERCENTILE
Tens0 1 2 3 4 5 6 7 8 9 10
U
n
i
t
s
0 - 840 910 950 985 1021 1057 1120 1224 15001 635 844 913 986 1050 1085 1156 -2 703 846 914 951 1014 1051 1086 1121 1157 1225 -3 739 847 915 977 1016 1122 1160 1257 -4 743 875 982 1017 1052 1087 1189 1258 -5 775 878 916 1018 1123 1260 -6 777 879 945 983 1053 1088 1190 1292 -7 807 880 947 1151 1191 1294 -8 811 881 948 984 1019 1090 1154 1192 1327 -9 812 882 949 1020 1054 1119 1155 1222 1361 -
CENTRE-SOUTH: MAXIMUM RESULT IN THE PERCENTILE
Tens0 1 2 3 4 5 6 7 8 9 10
U
n
i
t
s
0 - 775 845 947 983 1018 1053 1085 1157 14991 569 776 883 948 1086 1188 -2 603 777 846 911 949 1054 1087 1190 -3 667 779 913 984 1019 1082 1191 -4 674 809 847 914 950 1020 1085 1088 1193 -5 706 810 878 985 1021 1086 1223 -6 708 811 879 915 951 986 1050 1087 1090 1227 -7 739 812 916 952 1014 1051 1119 1259 -8 742 814 880 981 1016 1052 1088 1120 1292 -9 744 842 881 945 982 1017 1121 1328 -
TABLE 3.16: Maximum TECO results in each percentile, per Geographic Area
How to read this table - Example Percentile 34: take the cell at the intersection of column 3 (tens) and row 4 (units). The result shown in this cell is the maximum score obtained within the 34th percentileIf a cell is empty, the maximum score obtained within that percentile is shown in the first non-empty previous cell
TABLE 3.17: Maximum TECO results in each percentile per University – NORTH
PADUA: MAXIMUM RESULT IN THE PERCENTILE
Tens
0 1 2 3 4 5 6 7 8 9 10
U
n
i
t
s
0 - 811 907 950 984 1020 1055 1120 1156 1225 14301 638 841 913 985 1021 1085 1226 -
2 672 845 914 986 1050 1086 1121 1158 1257 -3 707 951 1014 1051 1189 1259 -
4 741 847 915 954 1016 1087 1122 1190 1260 -
5 742 876 916 982 1017 1052 1123 1191 1289 -6 776 879 945 1018 1088 1126 1293 -
7 777 880 948 983 1053 1153 1193 1325 -
8 779 881 949 1019 1089 1154 1222 1327 -9 810 1054 1117 1155 1224 1361 -
UDINE: MAXIMUM RESULT IN THE PERCENTILE
Tens
0 1 2 3 4 5 6 7 8 9 10
U
n
i
t
s
0 - 845 915 1021 1088 1192 1431
1 672 954 1016 1050 1123 1219 -2 704 846 945 982 1017 1051 1055 1116 1124 1224 -
3 741 848 947 1082 1119 1154 -
4 775 878 983 1085 1120 1155 1225 -5 776 880 948 1018 1086 1226 -
6 779 881 949 984 1052 1121 1157 1257 -
7 810 882 985 1019 1087 1186 1259 -8 811 911 950 1122 1190 1325 -
9 840 914 951 986 1020 1053 1191 1395 -
EASTERN PIEDMONT: MAXIMUM RESULT IN THE PERCENTILE
Tens
0 1 2 3 4 5 6 7 8 9 10
U
n
i
t
s
0 - 878 950 1020 1085 1122 1189 1395
1 670 811 915 984 1021 1086 1190 -2 706 813 879 916 1014 1051 1087 1123 1220 -
3 709 814 880 944 979 1016 1052 1223 -
4 739 842 947 981 1017 1089 1151 1224 -
5 742 882 982 1053 1117 1154 1225 -
6 776 845 885 1119 1155 1257 -
7 804 846 910 948 983 1018 1120 1156 1260 -8 809 847 913 949 1054 1157 1293 -
9 810 876 914 1083 1121 1186 1358 -
MILAN: MAXIMUM RESULT IN THE PERCENTILE
Tens0 1 2 3 4 5 6 7 8 9 10
U
n
i
t
s
0 - 841 911 951 1016 1016 1087 1122 1429
1 603 845 913 979 1017 1017 1188 1225 -
2 670 846 914 981 1018 1018 1088 1123 1226 -
3 707 847 915 982 1124 1189 1257 -
4 742 876 916 983 1089 1153 1190 1259 -5 773 878 917 1019 1019 1119 1154 1288 -
6 778 880 947 984 1020 1020 1120 1155 1293 -
7 807 948 985 1021 1021 1191 1294 -
8 811 881 949 1050 1050 1121 1156 1193 1327 -
9 812 910 950 1011 1051 1051 1157 1223 1361 -
How to read this table - Example Percentile 34: take the cell at the intersection of column 3 (tens) and row 4 (units). The result shown in this cell is the maximum score obtained within the 34th percentileIf a cell is empty, the maximum score obtained within that percentile is shown in the first non-empty previous cell
149
BOLOGNA: MAXIMUM RESULT IN THE PERCENTILE
Tens
0 1 2 3 4 5 6 7 8 9 10
U
n
i
t
s
0 - 917 984 1018 1121 1160 1225 14291 637 878 948 1053 1122 1189 -
2 741 879 985 1019 1087 1257 -3 771 880 949 1020 1088 1123 1190 1259 -
4 775 881 986 1021 1054 1154 1191 1263 -
5 810 882 950 1012 1050 1089 1155 1192 1294 -6 842 910 1016 1051 1055 1090 1222 1327 -
7 844 914 951 1057 1120 1156 1223 1329 -
8 846 982 1017 1052 1085 1224 1360 -9 847 915 983 1086 1157 1394 -
FLORENCE: MAXIMUM RESULT IN THE PERCENTILE
Tens
0 1 2 3 4 5 6 7 8 9 10
U
n
i
t
s
0 - 814 882 1054 1119 1224 1500
1 604 842 911 949 985 1020 1055 1120 1156 1225 -2 707 846 913 951 986 1050 1085 1121 1227 -
3 742 847 914 1016 1051 1086 1158 1257 -
4 775 876 954 1017 1087 1122 1160 1259 -5 776 878 915 981 1052 1189 1260 -
6 916 982 1018 1053 1088 1123 1293 -
7 807 879 917 983 1124 1190 1325 -8 811 880 947 1089 1153 1191 1327 -
9 881 948 984 1019 1092 1155 1220 1364 -
ROME "LA SAPIENZA": MAXIMUM RESULT IN THE PERCENTILE
Tens
0 1 2 3 4 5 6 7 8 9 10
U
n
i
t
s
0 - 776 911 949 1090 1158 1499
1 569 846 913 984 1019 1054 1119 1188 -
2 637 778 847 1020 1120 1190 -
3 670 807 848 914 950 985 1082 1121 1191 -
4 703 809 878 915 951 986 1021 1085 1122 1222 -
5 707 811 879 916 952 1016 1048 1086 1123 1225 -
6 737 880 917 981 1051 1087 1153 1257 -7 741 813 881 945 982 1017 1155 1260 -
8 742 842 947 983 1018 1052 1088 1156 1292 -
9 773 845 882 948 1053 1157 1328 -
ROME "TOR VERGATA": MAXIMUM RESULT IN THE PERCENTILE
Tens
0 1 2 3 4 5 6 7 8 9 10
U
n
i
t
s
0 - 776 947 983 1018 1088 1123 1189 1432
1 569 777 845 882 948 1153 1190 -
2 639 911 949 984 1019 1089 1155 1191 -
3 674 807 846 913 985 1048 1195 -4 704 809 950 1013 1051 1117 1222 -
5 738 810 847 915 1014 1052 1120 1224 -
6 739 812 878 951 1016 1057 1121 1156 1226 -
7 741 879 954 1085 1255 -
8 742 813 881 916 982 1017 1086 1158 1261 -
9 771 844 1087 1122 1188 1430 -
TABLE 3.18: Maximum TECO results in each percentile per University – CENTRE
How to read this table - Example Percentile 34: take the cell at the intersection of column 3 (tens) and row 4 (units). The result shown in this cell is the maximum score obtained within the 34th percentileIf a cell is empty, the maximum score obtained within that percentile is shown in the first non-empty previous cell
TABLE 3.19: Maximum TECO results in each percentile per University – SOUTH
NAPLES: MAXIMUM RESULT IN THE PERCENTILE
Tens
0 1 2 3 4 5 6 7 8 9 10
U
n
i
t
s
0 - 743 842 881 917 980 1017 1053 1119 1157 13951 564 775 845 882 944 982 1018 1054 1120 1188 -
2 598 776 846 885 948 983 1055 1121 1190 -
3 603 777 911 1019 1083 1122 -4 639 779 849 949 1020 1086 1191 -
5 673 809 876 914 950 984 1050 1153 1223 -
6 704 810 878 985 1051 1087 1155 1225 -7 706 811 879 915 1052 1088 1258 -
8 734 812 880 951 1156 1293 -9 741 813 916 976 1013 1089 1327 -
LECCE: MAXIMUM RESULT IN THE PERCENTILE
Tens
0 1 2 3 4 5 6 7 8 9 10
U
n
i
t
s
0 - 742 878 915 950 984 1155 1326
1 498 743 812 879 916 951 1017 1087 -
2 569 772 813 880 979 1018 1156 -3 603 775 844 881 947 981 1019 1189 -
4 633 845 882 985 -
5 635 776 883 948 983 1020 1089 1192 -6 638 778 909 1051 1119 1223 -
7 702 779 846 913 986 1120 1224 -8 704 811 949 988 1052 1123 1258 -
9 741 914 1016 1053 1124 1292 -
MESSINA: MAXIMUM RESULT IN THE PERCENTILE
Tens
0 1 2 3 4 5 6 7 8 9 10
U
n
i
t
s
0 - 744 845 1122 13621 603 772 913 951 1016 1052 1123 -
2 604 775 914 1055 1126 -
3 635 776 813 846 952 1017 1085 1155 -4 699 881 915 979 1089 1189 -
5 704 805 847 948 982 1018 1117 -
6 707 807 842 848 983 1019 1119 1190 -7 709 810 875 882 1021 1120 1192 -
8 741 876 984 1051 1121 1259 -9 742 811 880 911 950 1325 -
CAGLIARI: MAXIMUM RESULT IN THE PERCENTILE
Tens
0 1 2 3 4 5 6 7 8 9 10
U
n
i
t
s
0 - 848 914 986 1020 1083 1117 1156 1361
1 600 806 876 915 1021 1085 1157 -
2 604 810 878 916 1014 1050 1086 1119 1190 -3 670 811 879 917 951 1016 1120 1191 -
4 706 846 920 1052 1087 1223 -
5 742 881 947 981 1017 1121 1258 -6 743 949 982 1122 -
7 773 847 882 1053 1326 -8 913 1088 1123 1329 -
9 775 950 984 1019 1082 1155 1358 -
How to read this table - Example Percentile 34: take the cell at the intersection of column 3 (tens) and row 4 (units). The result shown in this cell is the maximum score obtained within the 34th percentileIf a cell is empty, the maximum score obtained within that percentile is shown in the first non-empty previous cell
150
VAR between APS, WE, WM 0.01598
STDEV between APS, WE, WM
0.12643
CV between APS, WE, WM 0.00013
VAR between CRE, CA, SQR 0.04492
STDEV between CRE, CA, SQR
0.21193
CV between CRE, CA, SQR 0.00021
TABLE A-3.1: Variance (VAR), standard deviation (STDEV) and variation coefficient (CV) for results in the different test components
PT = Performance Task• APS = Analysis and Problem Solving• WE = Writing Effectiveness• WM = Writing Mechanics
SRQ = Selected Response Questions• CRE = Critical Reading• CA = Critique an Argument• SQR = Scientific and Quantitative Reasoning
TECO PT APS WE WM SRQ CRE CA SQR
mean 999.53 999.46 999.49 999.66 999.53 999.48 999.38 999.83 999.42VARIANCE (between individuals) 25338.76 40033.18 39941.15 39993.99 39987.54 39989.16 39952.97 40015.67 40037.20
STDEV (between individuals) 35.67 29.84 27.40 27.26 29.55 49.04 44.25 24.94 39.18
CV (between individuals) 0.1593 0.2002 0.2000 0.2001 0.2001 0.2001 0.2000 0.2001 0.2002
VARIANCE (between Disciplinary Groups) 1168.16 936.12 727.33 851.48 821.15 1874.84 1413.92 482.32 1270.63
STDEV (between Disciplinary Groups) 34.18 30.60 26.97 29.18 28.66 43.30 37.60 21.96 35.65
CV (between Disciplinary Groups) 0.0342 0.0306 0.0270 0.0292 0.0287 0.0433 0.0376 0.0220 0.0357
VARIANCE (between Macro-groups) 131.17 140.13 120.36 147.27 95.48 375.20 253.11 85.08 228.38
STDEV (between Macro-groups) 11.45 11.84 10.97 12.14 9.77 19.37 15.91 9.22 15.11
CV (between Macro-groups) 0.0115 0.0118 0.0110 0.0121 0.0098 0.0194 0.0159 0.0092 0.0151
VARIANCE (between Universities) 994.43 904.56 762.30 796.36 684.40 1177.91 695.37 615.32 586.85
STDEV (between Universities) 31.53 30.08 27.61 28.22 26.16 34.32 26.37 24.81 24.22
CV (between Universities) 0.0315 0.0301 0.0276 0.0282 0.0262 0.0343 0.0264 0.0248 0.0242
VARIANCE (between 3 Geographic Areas) 485.30 508.09 462.48 481.32 294.81 493.43 247.86 349.41 206.20
STDEV (between 3 Geographic Areas) 22.03 22.54 21.51 21.94 17.17 22.21 15.74 18.69 14.36
CV (between 3 Geographic Areas) 0.0220 0.0226 0.0215 0.0219 0.0172 0.0222 0.0158 0.0187 0.0144
VARIANCE (between 2 Geographic Areas) 803.98 768.42 658.25 667.58 528.77 840.71 379.11 487.36 379.26
STDEV (between 2 Geographic Areas) 28.35 27.72 25.66 25.84 23.00 29.00 19.47 22.08 19.47
CV (between 2 Geographic Areas) 0.0284 0.0277 0.0257 0.0258 0.0230 0.0290 0.0195 0.0221 0.0195
880
920
960
1000
1040
1080
agr.
al
arch ar
t
cult
bio
chim
com
un
eco
n
farm filo
form ge
o
giu
ing
lett
ling
mat
.fis
.sta
t
med
od
on
po
lit
psi
c
soc
sto
terr
vet
ITA
12
TEC
O
F M
TABLE 4.2: TECO results per Disciplinary group, broken down by Gender
Source: See TABLE A-4.1
151
TABLE 4.3: TECO results and variation coefficients per Disciplinary Group in the NORTH Geographic Area, highlighted according to the proportion of admission tests, the level of grades, and the significance of the distance
versus the ITA12 meanDisciplinary Group
(Macro-group)DIPL UN DIPL/UN
TECO median
TECO mean
agr.al 0.984 0.989 0.995 1016 987.33arch (*)(+) 1.044 0.994 1.051 1087.5 1068.76 (**)
art 0.909 1.058 0.859 966 950.75bio (*)(+) 0.966 0.978 0.988 1018.5 1028.48 (**)
chim (*)(+) 1.070 1.000 1.070 1052 1029.74comun 0.922 0.982 0.939 985 986.90
cult (*)(+) 0.930 1.019 0.912 1017 1003.53econ (*)(+) 1.005 0.942 1.068 1017 1003.03farm (*)(+) 1.015 0.981 1.034 1034.5 1025.98 (**)filo (*)(+) 1.006 1.090 0.923 1017.5 1030.86
form 0.940 1.007 0.933 931 932.79 (**)geo 0.930 0.990 0.939 980 936.27 (**)
giu (*)(+) 0.982 0.995 0.987 1052 1036.65 (**)ing 1.070 0.984 1.088 1014 992.18
lett (*)(+) 1.031 1.061 0.972 1017 1030.04ling (*) 0.980 1.009 0.971 1016 1003.00
mat.fis.stat (*)(+) 1.043 0.998 1.046 1055 1060.42 (**)med (*)(+) 1.109 1.028 1.079 1088 1105.49 (**)odon (*)(+) 1.024 1.014 1.009 1087 1087.38 (**)polit (*)(+) 0.928 0.985 0.942 1050 1028.92psic (*)(+) 1.000 1.014 0.986 1050 1045.78 (**)
soc 0.924 0.994 0.929 930 974.39sto (*)(+) 0.952 1.064 0.894 1021 1074.90 (**)
terr 0.996 0.989 1.007 983 988.61vet (*)(+) 1.011 0.986 1.025 1052 1036.67
NORTH (*)(+) 1 1 1 1020 1024.01 (**)ITA12 1016 999.53
(+): Disciplinary Groups with a TECO median higher than the ITA12 median
(*): Disciplinary Groups with a TECO mean higher than the ITA12 meanDIPL: Ratio between mean diploma grade of eligible students in that Disciplinary Group and the NORTH Geographic Area meanUN: Ratio between mean grade in University exams sat so far of eligible students in that Disciplinary Group and the NORTH Geographic Area mean(**): TECO mean significantly different versus ITA12 mean (95% confidence interval) Disciplinary Groups with DIPL > 1 and UN < 1 are circledThe dark grey highlighting indicates the Disciplinary Groups for which there is a national admission test.
The light grey highlighting indicates the Disciplinary Groups for which there is a local admission test for more than 50% of the eligible students in the NORTH Geographic Area
Source: See TABLE 3
agr.al arch (*)(+)
art
bio (*)(+)
chim (*)(+)
comun
cult (*)(+)
econ (*)(+)
farm (*)(+)
filo (*)(+)
form geo
giu (*)(+)ing
lett (*)(+)
ling (*)
mat.fis.stat (*)(+)
med (*)(+) odon (*)(+)
polit (*)(+)
psic (*)(+)
soc
sto (*)(+)
terr
vet (*)(+)
NORD (*)(+)
ITA12
-20
-10
0
10
20
30
40
50
60
-20 -10 0 10 20 30 40
P
R
NORTH
TABLE 4.4: TECO results and variation coefficients per Disciplinary
Group in the CENTRE Geographic Area, highlighted according to the proportion of admission tests, the level of grades, and the significance
of the distance versus the ITA12 meanDisciplinary Group
(Macro-group)DIPL UN DIPL/UN
TECO median
TECO mean
agr.al (*)(+) 1.002 0.998 1.003 1019 1019.56arch (*) 0.998 1.017 0.981 986 999.91
art 0.942 1.017 0.926 982.5 969.79bio 0.954 0.975 0.979 983 982.99
chim 0.992 0.957 1.037 985 959.20comun (+) 0.950 0.966 0.984 1017 970.08
cult 0.951 1.047 0.908 984 973.23econ 1.009 0.945 1.068 1015 992.04farm 1.026 1.011 1.015 982 963.19 (**)
filo (*)(+) 1.009 1.079 0.935 1051 1023.00form 0.936 1.016 0.921 879 896.08 (**)geo 0.953 0.972 0.980 981 942.62
giu (*)(+) 1.012 1.002 1.010 1018 1007.82ing (*)(+) 1.037 0.950 1.091 1018 1010.26
lett (*) 1.004 1.045 0.961 985 1002.91ling 0.963 1.028 0.937 982 971.44 (**)
mat.fis.stat (*)(+) 1.027 0.983 1.045 1052 1038.01 (**)med (*)(+) 1.065 1.052 1.012 1054 1067.79 (**)
odon 1.030 1.049 0.981 895.5 965.60polit (*)(+) 0.978 0.981 0.997 1050 1022.01psic (*)(+) 0.972 0.966 1.006 1019 1028.21 (**)
soc 0.973 0.996 0.977 983 999.19sto 0.975 1.058 0.921 986 970.52terr 0.987 0.993 0.993 947 933.94 (**)
vet (*)(+) 1.046 1.013 1.032 1069.5 1049.93CENTRE 1 1 1 1016 997.07ITA12 1016 999.53
(+): Disciplinary Groups with a TECO median higher than the ITA12 median(*): Disciplinary Groups with a TECO mean higher than the ITA12 meanDIPL: Ratio between mean diploma grade of eligible students in that Disciplinary Group and the CENTRE Geographic Area meanUN: Ratio between mean grade in University exams sat so far of eligible students in that Disciplinary Group and the CENTRE Geographic Area mean(**): TECO mean significantly different versus ITA12 mean (95% confidence interval) Disciplinary Groups with DIPL > 1 and UN < 1 are circledThe dark grey highlighting indicates the Disciplinary Groups for which there is a national admission test.The light grey highlighting indicates the Disciplinary Groups for which there is a local admission test for more than 50% of the eligible students in the CENTRE Geographic AreaSource: See TABLE 3
agr.al (*)(+)
arch (*)
art
bio
chim
comun (+)
cult
econ
farm
filo (*)(+)
form
geo
giu (*)(+)
ing (*)(+)
lett (*)
ling
mat.fis.stat (*)(+)
med (*)(+)
odon
polit (*)(+)
psic (*)(+)soc
sto
terr
vet (*)(+)
CENTRO
ITA12
-25
-20
-15
-10
-5
0
5
10
15
20
25
-10 -5 0 5 10 15 20 25
P
R
CENTRE
152
TABLE 4.5: TECO results and variation coefficients per Disciplinary Group in the SOUTH Geographic Area, highlighted according to the proportion
of admission tests, the level of grades, and the significance of the distance versus the ITA12 mean
Disciplinary Group (Macro-group)
DIPL UN DIPL/UNTECO
medianTECO mean
agr.al 0.936 0.978 0.957 898.5 885.94 (**)arch 0.981 1.032 0.951 914.5 955.55artbio 0.992 0.995 0.997 984 969.50
chim (*) 1.023 0.963 1.062 950 1004.29comun 0.930 1.009 0.922 1014 971.62
cult 0.994 1.054 0.943 949 948.30 (**)econ 0.995 0.972 1.023 949 958.56 (**)farm 0.990 0.989 1.001 880 885.93 (**)filo 0.990 1.064 0.931 985 945.91
form 0.927 1.029 0.901 879 864.96 (**)geo 0.993 1.013 0.981 808.5 808.50giu 1.001 0.979 1.022 952 962.21 (**)ing 1.070 0.996 1.074 982 975.81lett 1.036 1.042 0.994 949 989.00ling 0.942 0.991 0.951 986 979.53
mat.fis.stat (*)(+) 0.997 0.983 1.014 1086 1023.19med (*)(+) 1.097 1.046 1.048 1054 1050.61 (**)
odon 1.045 1.031 1.014 986 994.33polit 0.949 1.002 0.948 930.5 933.74 (**)
psic (*) 0.996 1.015 0.981 1001.5 1003.08soc 0.926 1.001 0.925 897 882.33 (**)
sto (+) 0.962 1.060 0.908 1050 987.14terr 1.037 0.999 1.038 929.5 886.25 (**)vet 0.978 1.024 0.954 811 836.71 (**)
SOUTH 1 1 1 951 958.90 (**)ITA12 1016 999.53
(+): Disciplinary Groups with a TECO median higher than the ITA12 median(*): Disciplinary Groups with a TECO mean higher than the ITA12 meanDIPL: Ratio between mean diploma grade of eligible students in that Disciplinary Group and the SOUTH Geographic Area meanUN: Ratio between mean grade in University exams sat so far of eligible students in that Disciplinary Group and the SOUTH Geographic Area mean(**): TECO mean significantly different versus ITA12 mean (95% confidence interval) Disciplinary Groups with DIPL > 1 and UN < 1 are circledThe dark grey highlighting indicates the Disciplinary Groups for which there is a national admission test.The light grey highlighting indicates the Disciplinary Groups for which there is a local admission test for more than 50% of the eligible students in the SOUTH Geographic AreaSource: See TABLE 3
agr.al
arch
bio
chim (*)
comun
cult
econ
farm
filo
form
geo
giu
inglett
ling
mat.fis.stat (*)(+)
med (*)(+)
odon
polit
psic (*)
soc
sto (+)
terr
vet
SUD
ITA12
-25
-20
-15
-10
-5
0
5
10
15
20
25
-20 -10 0 10 20 30
P
R
SOUTH
SAN (+)(*)
SOC (+)(*)
SC
H
ITA12
y = 0,4333x + 564,74
940
960
980
1000
1020
1040
1060
950 970 990 1010 1030 1050
PT
SRQCorrelation = 0,21
0,150
0,160
0,170
0,180
0,190
0,200
0,210
SAN SC SOC H ITA12
Co
effi
cien
te d
i var
iazi
on
e
CV PT CV SRQ CV TECO
TABLE 4.9: PT and SRQ results, variation coefficients , mean correlation between them and significance of the difference between them, per Macro-group
Macro-group PT mean PT median PT CV SRQ mean SRQ median SRQ CV
SAN (+)(*) 1014.33 988 0.201 1028.34 1048 0.194
SC 983.99 988 0.205 1007.37 (**) 1048 0.199
SOC (+)(*) 1007.55 (**) 988 0.198 995.25 1048 0.199
H 1001.06 (**) 988 0.193 962.56 978 0.205
ITA12 999.46 988 0.200 999.48 1048.00 0.200
(+): Macro-groups with a TECO median higher than the ITA12 TECO median
(*): Macro-groupswith a TECO mean higher than the ITA12 TECO mean
(**): PT and SRQ means are significantly different (95% confidence interval)
Source: See TABLE, see TABLE A-4.3 for CV
153
H
SAN
SC
SOC
800
900
1000
1100
1200
800 900 1000 1100 1200
SRQ
PT
TABLE 4.10: Individual and mean correlations between PT and SRQ, per Macro-group
MACROAREAS (mean values)
mean correlation
individual correlation
H
SAN
SC
SOC
Macro-group
0,14
0,16
0,18
0,20
0,22
0,24
NORD CENTRO SUD CENTRO-NORD CENTRO-SUD ITA12
Co
eff
icie
nte
di v
aria
zio
ne
CV PT CV SRQ CV TECO
TABLE 4.15: PT and SRQ results, variation coefficients, mean correlation between them and significance of the difference between
them, per Geographic Area
Geographic Area T PT meanPT
medianPT CV SRQ mean
SRQ median
SRQ CV
NORTH (+)(*) 1953 1027.53 1056 0.195 1020.37 1048 0.188
CENTRE 2899 993.06 988 0.201 1000.94 1048 0.198
SOUTH 1001 963.20 988 0.202 954.45 978 0.222
CENTRE-NORTH (+)(*) 3012 1026.38 988 0.194 1027.64 1048 0.186
CENTRE-SOUTH 2841 970.91 988 0.203 969.62 978 0.211
ITA12 5853 999.46 988 0.200 999.48 1048 0.200
(+): Geographic Area with a TECO median higher than the ITA12 TECO median
(*): Geographic Area with a TECO mean higher than the ITA12 TECO mean
CV: Variation coefficient
Source: See TABLE 3, see TABLE A-4.5 for CV
NORD (+)(*)
CENTRO
SUD
CENTRO-NORD (+)(*)
CENTRO-SUD
ITA12
y = 1,0315x - 32,769
950
970
990
1010
1030
1050
900 950 1000 1050 1100
SRQ
PT
Correlation = 0,98
154
CENTRE
NORTH
SOUTH
CENTRE-NORTH
CENTRE-SOUTH
800
850
900
950
1000
1050
1100
1150
1200
800 850 900 950 1000 1050 1100 1150 1200
SRQ
PT
TABLE 4.16: Individual and mean correlations between PT and SRQ, per Geographic Area
CENTRE-NORTH
SOUTHCENTRE-SOUTH
NORTHCENTRE
Geographic Area
Mean correlation between PT and SRQ
Individual correlations between PT and SRQ
TABLE A-4.1: TECO, PT and SRQ results per Disciplinary group, Macro-group, University and Geographic Area, broken down by Gender
Disciplinary group
F MMacro-group
F M
TECO PT SRQ TECO PT SRQ TECO PT SRQ TECO PT SRQ
agr.al 1010.84 1009.51 1012.11 965.35 961.56 969.07 SAN 1015.95 (**) 1014.12 1017.63 1030.39 1014.68 1045.99
arch 1005.76 989.59 1021.78 1006.24 990.66 1021.66 SC 995.7 991.8 999.48 995.77 977.49 1013.94
art 956.42 954.08 958.6 990.88 1038.94 942.5 SOC 994.61 (**) 1003.25 985.79 1013.92 1015.35 1012.4
cult 970.84 984.34 957.21 1002.59 992.25 1012.75 H 975.96165 994.41 957.37 1000.48 1021.97 978.87
bio 1012.97 1001.3 1024.6 993.72 990.39 996.99 ITA12 994.65 1000.23 988.92 1006.66 998.33 1014.88
chim 961.96 980.51 943.2 1020.16 957.92 1082.38
comun 962.8 970.85 954.52 1000.62 1016.81 984.35University
F M
econ 977.49 (**) 979.44 975.32 1006.35 985.57 1027.06 TECO PT SRQ TECO PT SRQ
farm 976.01 984.49 967.36 973.95 966.26 981.59 PO 996.87 1023.54 970.03 990.47 1002.73 978.3
filo 1028.41 1043.51 1013.17 998.62 1010.05 987.08 MI 1032.31 1038.77 1025.75 1038.13 1032.93 1043.17
form 901.83 931.6 871.73 948.25 971 924.75 PD 1034.99 1031.82 1038.04 1008.52 1010.42 1006.46
geo 945.72 1007.21 883.97 901.21 925.07 877.36 UD 1018.25 1018.42 1017.93 1034.58 1023.91 1045.21
giu 1002.58 1012.08 992.94 1023.56 1040.45 1006.57 BO 1036.67 1027.25 1045.91 1065.13 1059.25 1070.88
ing 995.09 985.37 1004.65 1003.51 978.11 1028.78 FI 1020.05 1017.67 1022.26 1030.97 1013.81 1048.03
lett 1015.11 1040.57 989.53 1005.04 1035.16 974.78 RM1 970.36 979.84 960.7 983.03 967.01 998.96
ling 981.08 994.86 967.24 1004.71 1025.24 984.29 RM2 951.81 (**) 951.55 951.96 1011.62 1018.22 1004.91
mat.fis.stat
1048.74 1037.13 1060.23 1037.25 1022.11 1052.32 NA 946.59 (**) 955.2 937.82 991.14 992.96 989.24
med 1070.31 1061.24 1079.29 1074.36 1053.37 1095.22 LE 933.11 943.71 922.16 950.35 936.58 964
odon 1033.8 1024.13 1043 1005.72 997.48 1013.79 ME 910.38 914.8 905.81 958.4 954.1 962.57
polit 995.65 1003.62 987.49 1019.69 1029.73 1009.51 CA 976.64 996.16 957.04 999.24 983.02 1015.26
psic 1029.3 1025.18 1033.23 1032 998.69 1065.41 ITA12 994.65 1000.23 988.92 1006.66 998.33 1014.88
soc 959 989.88 927.88 948.14 949.29 947.43
sto 1015.5 1035.65 995.15 1008.59 1037.51 979.51 Geographic Area
F M
terr 942.78 957.91 927.48 928.06 917.94 938.01 TECO PT SRQ TECO PT SRQ
vet 1017.31 995.1 1039.4 970.08 951.46 988.38 NORTH 1024.73 1031.15 1018.18 1022.74 1021.11 1024.26
ITA12 994.65 1000.23 988.92 1006.66 998.33 1014.88 CENTRE 990.55 (**) 993.98 986.95 1005.42 991.87 1018.86
(**) TECO means are significantly different for Females versus Males (95% confidence interval), if the number of observations (T) is higher or equal to 30
SOUTH 942.81 (**) 952.49 932.96 982.58 978.98 986.08
CENTRE-NORTH
1025.18 1027.83 1022.38 1030.18 1024 1036.24
CENTRE-SOUTH
959.04 (**) 968.03 949.88 984.96 974.65 995.18
See TABLES 4.2, 4.8, 4.11, 4.14 ITA12 994.65 1000.23 988.92 1006.66 998.33 1014.88
155
TABLE A-4.2: Means and variation coefficients on TECO and its components’ results within Disciplinary Groups, and variances between Disciplinary Groups of TECO and its components’ results
Disciplinary Group
TECOTECO
CVPT PT CV APS APS CV WE WE CV WM WM CV SRQ SRQ CV CRE CRE CV CA CA CV SQR SQR CV
agr.al 984.01 0.167 981.22 0.224 974.24 0.217 986.07 0.215 989.24 0.225 986.72 0.180 986.39 0.201 1017.40 0.189 973.27 0.186
arch 1005.94 0.137 989.99 0.186 986.03 0.193 991.69 0.191 995.65 0.183 1021.73 0.177 1044.28 0.175 996.24 0.189 995.00 0.199
art 965.03 0.167 975.30 0.214 970.39 0.216 974.72 0.205 988.72 0.219 954.58 0.198 972.56 0.196 982.50 0.183 950.95 0.202
bio 1006.43 0.160 997.59 0.213 1005.42 0.205 995.93 0.206 992.29 0.214 1015.21 0.195 1007.99 0.192 1013.79 0.189 1010.64 0.195
chim 995.45 0.152 967.51 0.202 984.47 0.212 977.39 0.202 950.89 0.197 1023.29 0.189 994.17 0.209 1027.00 0.176 1033.77 0.192
comun 977.81 0.160 989.09 0.197 986.87 0.194 985.04 0.209 998.92 0.190 966.36 0.204 975.89 0.187 967.89 0.232 984.36 0.208
cult 977.99 0.139 986.12 0.180 988.07 0.176 986.43 0.175 988.32 0.205 969.73 0.191 984.82 0.202 986.89 0.182 964.53 0.187
econ 991.15 0.158 982.34 0.191 992.63 0.194 986.26 0.186 973.73 0.197 999.80 0.206 994.91 0.201 1005.37 0.208 1000.93 0.204
farm 975.45 0.168 979.59 0.205 983.70 0.202 977.73 0.212 983.85 0.205 971.19 0.207 981.96 0.203 983.63 0.205 974.03 0.204
filo 1018.20 0.149 1032.05 0.195 1010.53 0.196 1024.79 0.216 1051.06 0.182 1004.23 0.176 988.09 0.217 1008.87 0.163 1015.77 0.177
form 903.28 0.175 932.83 0.182 944.66 0.167 947.85 0.179 927.02 0.206 873.38 0.240 886.45 0.250 951.91 0.223 908.91 0.212
geo 933.96 0.176 985.51 0.202 991.38 0.196 992.75 0.200 976.81 0.211 882.23 0.243 890.13 0.259 948.08 0.251 926.49 0.189
giu 1009.73 0.158 1021.76 0.202 1021.93 0.197 1022.88 0.199 1013.74 0.204 997.59 0.201 1001.53 0.200 1009.49 0.204 984.65 0.198
ing 1001.20 0.146 980.10 0.203 988.17 0.204 979.54 0.206 978.98 0.194 1022.16 0.179 1020.65 0.184 997.97 0.207 1023.39 0.205
lett 1012.51 0.145 1039.17 0.187 1035.37 0.190 1038.25 0.179 1031.64 0.189 985.73 0.182 1017.03 0.187 982.97 0.174 962.73 0.193
ling 985.38 0.150 1000.38 0.188 983.35 0.191 999.08 0.193 1018.78 0.190 970.34 0.196 964.15 0.217 978.07 0.206 998.55 0.186
mat.fis.stat 1041.43 0.161 1027.57 0.210 1022.77 0.209 1021.66 0.212 1029.87 0.201 1055.19 0.188 1047.43 0.176 1025.16 0.196 1038.29 0.197
med 1072.25 0.139 1057.48 0.193 1041.82 0.198 1058.27 0.195 1054.49 0.192 1086.91 0.161 1065.36 0.161 1036.28 0.155 1074.58 0.182
odon 1015.30 0.171 1006.57 0.196 1010.68 0.191 1007.11 0.196 999.89 0.200 1023.75 0.232 1016.27 0.200 998.68 0.221 1031.82 0.213
polit 1006.18 0.162 1015.05 0.217 1009.90 0.218 1010.20 0.213 1020.57 0.214 997.13 0.185 994.92 0.186 1018.59 0.191 984.18 0.188
psic 1029.75 0.124 1020.74 0.167 1023.79 0.173 1021.95 0.179 1010.10 0.170 1038.62 0.158 1005.59 0.180 1007.90 0.178 1068.47 0.156
soc 958.04 0.171 986.28 0.203 1000.47 0.206 970.92 0.188 992.18 0.204 929.61 0.221 907.00 0.247 960.53 0.219 996.34 0.196
sto 1011.02 0.181 1036.86 0.203 1054.32 0.184 1028.40 0.201 1016.53 0.201 985.00 0.255 994.88 0.227 972.35 0.209 997.12 0.215
terr 935.70 0.172 938.69 0.192 939.90 0.199 940.04 0.191 955.55 0.194 932.54 0.241 943.61 0.232 960.76 0.238 957.70 0.222
vet 1004.11 0.164 982.90 0.187 975.71 0.213 993.27 0.179 985.17 0.180 1025.14 0.200 1021.27 0.192 1022.22 0.186 1008.66 0.204
ITA12 999.53 0.159 999.46 0.200 999.49 0.200 999.66 0.200 999.53 0.200 999.48 0.200 999.38 0.200 999.83 0.200 999.42 0.200
VAR BETWEEN 1272.4 890.6 750.8 743.4 873.4 2404.8 1958.1 622.1 1534.9
STDEV 35.67 29.84 27.40 27.26 29.55 49.04 44.25 24.94 39.18
The acronyms for TECO and its components indicate mean results; CV indicate the respective variation coefficients calculated on the internal variance (between individual students)VAR BETWEEN indicates the variance of means within TECO and its components (variance per column)STDEV indicates the standard deviation within TECO and its components (standard deviation per column)See TABLE 4.1
TABLE A-4.3: Means and variation coefficients on TECO and its components’ results within Macro-groups, and variances between Macro-groups of TECO and its components’ results
Macro-group TECOTECO
CVPT PT CV APS APS CV WE WE CV WM WM CV SRQ SRQ CV CRE CRE CV CA CA CV SQR SQR CV
SAN 1021.40 0.161 1014.33 0.201 1008.90 0.203 1014.95 0.204 1014.79 0.199 1028.34 0.194 1023.02 0.187 1010.63 0.185 1023.04 0.200
SC 995.74 0.160 983.99 0.205 986.04 0.206 983.74 0.205 987.37 0.201 1007.37 0.199 1008.10 0.197 1000.53 0.204 1005.01 0.204
SOC 1001.47 0.157 1007.55 0.198 1010.59 0.197 1007.75 0.197 1002.06 0.200 995.25 0.199 992.49 0.200 1003.36 0.206 995.88 0.196
H 981.88 0.158 1001.06 0.193 995.73 0.190 1001.83 0.192 1005.41 0.199 962.56 0.205 971.58 0.216 980.01 0.192 971.31 0.196
ITA12 999.53 0.159 999.46 0.200 999.49 0.200 999.66 0.200 999.53 0.200 999.48 0.200 999.38 0.200 999.83 0.200 999.42 0.200
VAR BETWEEN 268.9 169.3 134.7 178.0 129.6 757.2 484.7 172.2 463.4
STDEV 16.40 13.01 11.61 13.34 11.38 27.52 22.02 13.12 21.53
The acronyms for TECO and its components indicate mean results; CV indicate the respective variation coefficients calculated on the internal variance (between individual students)
VAR BETWEEN indicates the variance of means within TECO and its components (variance per column)
STDEV indicates the standard deviation within TECO and its components (standard deviation per column)
See TABLES 4.8 and 4.9
156
TABLE A-4.4: Means and variation coefficients on TECO and its components’ results within Universities, and variances between Universities of TECO and its components’ results
University TECOTECO
CVPT PT CV APS APS CV WE WE CV WM WM CV SRQ SRQ CV CRE CRE CV CA CA CV SQR SQR CV
PO 994.92 0.150 1017.21 0.192 1024.16 0.190 1009.81 0.191 1012.43 0.201 972.55 0.199 961.64 0.206 996.59 0.193 990.83 0.198
MI 1034.53 0.151 1036.55 0.196 1031.15 0.196 1033.39 0.194 1033.77 0.199 1032.39 0.189 1021.16 0.193 1028.78 0.188 1018.67 0.189
PD 1025.49 0.155 1024.14 0.197 1025.00 0.195 1026.41 0.194 1013.55 0.201 1026.71 0.188 1015.87 0.191 1023.92 0.189 1017.06 0.196
UD 1024.28 0.137 1020.45 0.189 1015.17 0.189 1025.70 0.195 1014.11 0.188 1028.01 0.168 1022.07 0.165 1040.33 0.194 998.43 0.192
BO 1047.73 0.143 1039.69 0.191 1035.41 0.193 1042.84 0.187 1028.49 0.193 1055.61 0.173 1048.72 0.170 1011.52 0.195 1049.02 0.180
FI 1024.71 0.150 1016.02 0.195 1014.23 0.202 1010.33 0.196 1018.75 0.189 1033.26 0.186 1025.29 0.188 1019.14 0.185 1023.60 0.190
RM1 976.04 0.162 974.09 0.202 976.26 0.201 977.99 0.203 976.25 0.202 977.86 0.204 987.90 0.202 977.85 0.214 985.82 0.206
RM2 981.22 0.167 984.34 0.209 993.00 0.207 982.23 0.206 982.79 0.199 978.00 0.212 978.81 0.204 1000.25 0.201 978.34 0.208
NA 965.43 0.171 971.17 0.200 969.80 0.201 968.91 0.204 984.03 0.199 959.57 0.229 973.02 0.223 974.27 0.198 968.06 0.212
LE 925.78 0.163 927.40 0.206 944.96 0.190 938.06 0.205 921.85 0.212 924.01 0.217 946.92 0.234 950.23 0.204 944.38 0.218
ME 939.92 0.173 940.89 0.206 936.24 0.201 948.82 0.203 956.20 0.209 938.68 0.222 955.36 0.227 955.01 0.218 961.29 0.220
CA 986.10 0.155 990.66 0.201 992.05 0.204 980.55 0.205 1002.60 0.193 981.41 0.192 960.71 0.220 1001.47 0.195 1006.76 0.196
ITA12 999.53 0.159 999.46 0.200 999.49 0.200 999.66 0.200 999.53 0.200 999.48 0.200 999.38 0.200 999.83 0.200 999.42 0.200
VAR BETWEEN 1484.1 1344.5 1127.0 1169.2 1066.9 1753.1 1133.8 832.7 885.1
STDEV 38.52 36.67 33.57 34.19 32.66 41.87 33.67 28.86 29.75
The acronyms for TECO and its components indicate mean results; CV indicate the respective variation coefficients calculated on the internal variance (between individual students)
VAR BETWEEN indicates the variance of means within TECO and its components (variance per column)
STDEV indicates the standard deviation within TECO and its components (standard deviation per column)
See TABLES 4.11 and 4.12
TABLE A-4.5: Means and variation coefficients of results on TECO and its components within Geographic Areas, and variances between Geographic Areas of mean TECO and components results
Geographic Area
TECOTECO
CVPT PT CV APS APS CV WE WE CV WM WM CV SRQ SRQ CV CRE CRE CV CA CA CV SQR SQR CV
NORTH 1024.01 0.150 1027.53 0.195 1025.93 0.194 1026.45 0.194 1021.71 0.199 1020.37 0.188 1010.08 0.192 1023.86 0.190 1010.69 0.193
CENTRE 997.07 0.159 993.06 0.201 993.88 0.202 994.20 0.201 993.42 0.199 1000.94 0.198 1003.96 0.196 993.38 0.204 1002.38 0.200
SOUTH 958.90 0.169 963.20 0.202 964.15 0.201 963.22 0.204 973.92 0.203 954.45 0.222 965.25 0.224 971.61 0.202 968.89 0.212
VAR BETWEEN 1070.3 1036.4 954.6 999.6 577.4 1147.3 591.1 688.7 489.8
STDEV 32.72 32.19 30.90 31.62 24.03 33.87 24.31 26.24 22.13
CENTRE-NORTH 1027.07 0.150 1026.38 0.194 1024.41 0.195 1024.75 0.193 1021.86 0.196 1027.64 0.186 1018.29 0.189 1021.27 0.190 1018.34 0.191
CENTRE-SOUTH 970.34 0.165 970.91 0.203 973.07 0.201 973.06 0.204 975.85 0.202 969.62 0.211 979.33 0.210 977.10 0.209 979.37 0.208
VAR BETWEEN 1609.3 1538.2 1317.6 1336.3 1058.5 1682.9 758.9 975.6 759.2
STDEV 40.12 39.22 36.30 36.56 32.53 41.02 27.55 31.23 27.55
ITA12 999.53 0.159 999.46 0.200 999.49 0.200 999.66 0.200 999.53 0.200 999.48 0.200 999.38 0.200 999.83 0.200 999.42 0.200
The acronyms for TECO and its components indicate mean results; CV indicate the respective variation coefficients calculated on the internal variance (between individual students)
VAR BETWEEN indicates the variance of means within TECO and its components (variance per column)
STDEV indicates the standard deviation within TECO and its components (standard deviation per column)
See TABLES 4.14 and 4.15
157
TABLE A-4.6: PT, SRQ and TECO variation coefficients, per Disciplinary Group
0,12
0,14
0,16
0,18
0,20
0,22
0,24
0,26ag
r.al
arch ar
t
bio
chim
com
un
cult
eco
n
farm filo
form ge
o
giu
ing
lett
ling
mat
.fis
.sta
t
med
od
on
po
lit
psi
c
soc
sto
terr
vet
ITA
12
Co
eff
icie
nte
di v
aria
zio
ne
CV PT CV SRQ CV TECO
Source: See TABLE A-4.2
TABLE A-4.7: PT and SRQ results and variation coefficients, per Disciplinary Group and Macro-group
agr.al
arch(*)
art
bio (*)
chim
comuncultecon farm
filo (+)(*)
form
geo
giu (+)(*)
ing (*)
lett (*)
ling
mat.fis.stat (+)(*)
med (+)(*)
odon (+)(*)
polit (+)(*)psic (+)(*)
soc
sto (+)(*)
terr
vet (+)(*)
ITA12
-80
-60
-40
-20
0
20
40
60
80
-0,040 -0,020 0,000 0,020 0,040
CV PT
PT Correlazione= 0,01
agr.al
arch(*)
art
bio (*)chim
comuncult
econ
farm
filo (+)(*)
formgeo
giu (+)(*)
ing (*)
lett (*)
ling
mat.fis.stat (+)(*)
med (+)(*)
odon (+)(*)
polit (+)(*)
psic (+)(*)
soc
sto (+)(*)
terr
vet (+)(*)
ITA12
-150
-100
-50
0
50
100
-0,050 0,000 0,050 0,100
CV SRQ
SRQ Correlazione= - 0,69
SAN (+)(*)
SC
SOC (+)(*)
H ITA12
-20
-15
-10
-5
0
5
10
15
20
-0,010 -0,005 0,000 0,005 0,010
CV PT
PT Correlazione= - 0,41
SAN (+)(*)
SC
SOC (+)(*)
H
ITA12
-50
-40
-30
-20
-10
0
10
20
30
40
-0,008 -0,006 -0,004 -0,002 0,000 0,002 0,004 0,006
CV SRQ
SRQCorrelazione= - 0,98
Disciplinary Group
Macro-group
Source: TABLE A-4.2
Source: TABLE A-4.3
158
TABLE A-4.8: PT and SRQ results and variation coefficients, per University and Geographic Area
PO
MI (+)(*)
PD (+)(*)UD (+)(*)
BO (+)(*)
FI (+)(*)
RM1
RM2NA
LE
ME
CA
ITA12
-80
-60
-40
-20
0
20
40
60
-0,015 -0,010 -0,005 0,000 0,005 0,010
CV PT
PTCorrelazione= - 0,84
PO
MI (+)(*)
PD (+)(*)UD (+)(*)
BO (+)(*)FI (+)(*)
RM1 RM2
NA
LE
ME
CA
ITA12
-100
-80
-60
-40
-20
0
20
40
60
80
-0,040 -0,020 0,000 0,020 0,040
CV SRQ
SRQCorrelazione= - 0,88
NORD
CENTRO
SUD
CENTRO-NORD
CENTRO-SUD
ITA12
-40
-30
-20
-10
0
10
20
30
40
-0,008 -0,006 -0,004 -0,002 0,000 0,002 0,004
CV PT
PTCorrelazione= - 0,96
NORD
CENTRO
SUD
CENTRO-NORD
CENTRO-SUD
ITA12
-50
-40
-30
-20
-10
0
10
20
30
40
-0,020 -0,010 0,000 0,010 0,020 0,030
CV SRQ
SRQ Correlazione= - 0,99
University
Geographic Area
Source: TABLE A-4.4
Source: TABLE A-4.5
0
1
2
3
4
5
6
7
8
9
10
SAN (+)(*) SC SOC (+)(*) H
Top performersM + F F M
0
2
4
6
8
10
12
14
16
18
20
SAN (+)(*) SC SOC (+)(*) H
High performers
0
2
4
6
8
10
12
14
16
SAN (+)(*) SC SOC (+)(*) H
Low performers
% o
f to
tal
M + F F M M + F F M M + F F M
SAN (+)(*) 1021,40 4,55 3,48 6,30 12,66 10,61 16,05 8,55 8,17 9,17
SC 995,74 4,01 3,92 4,08 8,81 8,71 8,88 10,14 9,69 10,52
SOC (+)(*) 1001,47 3,03 2,51 3,98 8,54 6,97 11,36 9,75 10,11 9,09
H 981,88 2,69 1,85 5,36 5,81 5,26 7,59 10,44 11,36 7,59
ITA12 999,53 3,55 2,91 4,50 8,85 7,69 10,55 9,81 9,93 9,62
Source: See TAB A-5.1
High performer: student with a test result above the national average of the 4th quartile (> 1196,71)
Low performer: student with a test result below the national average of the 1st quartile (< 793,24)
(**): Percentages calculated on respective total students (M+F, F, M). Upward and downward arrows indicate,
respectively, the best and the worst percentage in the column.
(+): Macro-group with a TECO median higher than than the ITA12 TECO median(*): Macro-group with a TECO mean higher than than the ITA12 TECO mean
Top performer: student with a test result above the national average of the 10th decile (> 1270,77)
TAB 5.2: Top, high e low performers per Macro-group, broken down by Gender
Macro-
groupTECO
% Top performers (**) % High performers (**) % Low performers (**)
159
M + F F M M + F F M M + F F M
PO 994,92 2,82 1,80 5,15 8,15 6,31 12,37 6,90 5,41 10,31MI (+)(*) 1034,53 5,01 3,44 7,57 11,65 11,13 12,50 6,64 6,48 6,91PD (+)(*) 1025,49 5,10 4,55 6,09 12,75 13,07 12,18 8,20 7,10 10,15UD (+)(*) 1024,28 2,79 2,76 2,83 9,41 6,63 14,15 6,27 4,97 8,49BO (+)(*) 1047,73 5,98 3,11 10,49 14,95 11,56 20,28 4,62 4,00 5,59FI (+)(*) 1024,71 4,63 5,30 3,73 11,29 11,36 11,19 6,95 7,07 6,78
RM1 976,04 2,60 2,19 3,10 6,40 5,47 7,54 12,49 12,91 11,98RM2 981,22 1,64 0,00 3,33 6,56 3,23 10,00 12,02 12,90 11,11NA 965,43 2,23 1,48 3,24 5,65 2,08 10,53 14,04 17,51 9,31LE 939,92 1,91 3,16 0,00 4,46 5,26 3,23 17,20 18,95 14,52ME 925,78 1,53 1,12 2,38 2,29 1,12 4,76 14,50 15,73 11,90CA 986,10 3,88 2,67 5,56 6,20 4,00 9,26 10,85 12,00 9,26
ITA12 999,53 3,55 2,91 4,50 8,85 7,69 10,55 9,81 9,93 9,62
Source: See TAB A-5.1
High performer: student with a test result above the national average of the 4th quartile (> 1196,71)
Low performer: student with a test result below the national average of the 1st quartile (< 793,24)
(**): Percentages calculated on respective total students (M+F, F, M). Upward and downward arrows indicate,
respectively, the best and the worst percentage in the column.
(+): University with a TECO median higher than than the ITA12 TECO median(*): University with a TECO mean higher than than the ITA12 TECO mean
Top performer: student with a test result above the national average of the 10th decile (> 1270,77)
TAB 5.3: Top, high e low performers per University, broken down by Gender
University TECO
% Top performers (**) % High performers (**) % Low performers (**)
0
2
4
6
8
10
12
PO MI PD UD BO FI RM1 RM2 NA LE ME CA
Top performersM + F F M
0
5
10
15
20
25
PO MI PD UD BO FI RM1 RM2 NA LE ME CA
High performers
0
5
10
15
20
25
PO MI PD UD BO FI RM1 RM2 NA LE ME CA
Low performers
TABLE 5.3 (continued): Top, high and low
performers per University, broken down
by Gender
% o
f to
tal
% o
f to
tal
% o
f to
tal
Source: See TABLE 5.3
If a University does not have any top, high or low performers for a given gender, the corresponding bar is not shown in the graph – although students of both genders were tested in all Universities.
160
0
1
2
3
4
5
6
7
NORD (+)(*) CENTRO SUD
M + F F M
0
2
4
6
8
10
12
14
NORD (+)(*) CENTRO SUD
0
2
4
6
8
10
12
14
16
18
NORD (+)(*) CENTRO SUD
% o
f to
tal
Top performers High performers Low performers
TAB 5.4: Top, high e low performers per Geographic Area, broken down by Gender
Geographic Area TECO% Top performers (**) % High performers (**) % Low performers (**)
M + F F M M + F F M M + F F M
NORTH (+)(*) 1024,01 4,35 3,36 6,11 11,06 10,17 12,64 7,07 6,24 8,52
CENTRE 997,07 3,45 2,95 4,09 8,66 7,62 9,99 10,14 10,26 9,99
SOUTH 958,90 2,30 1,85 2,96 5,09 2,68 8,64 14,19 16,78 10,37
CENTRE-NORTH (+)(*) 1027,07 4,61 3,74 6,04 11,59 10,59 13,22 6,74 6,15 7,71
CENTRE-SOUTH 970,34 2,43 1,93 3,07 5,95 4,30 8,08 13,06 14,35 11,39
ITA12 999,53 3,55 2,91 4,50 8,85 7,69 10,55 9,81 9,93 9,62
(+): Geographic Area with a TECO median higher than than the ITA12 TECO median
(*): Geographic Area with a TECO mean higher than than the ITA12 TECO mean
Top performer: student with a test result above the national average of the 10th decile (> 1270,77)
High performer: student with a test result above the national average of the 4th quartile (> 1196,71)
Low performer: student with a test result below the national average of the 1st quartile (< 793,24)
(**): Percentages calculated on respective total students (M+F, F, M). Upward and downward arrows indicate, respectively, the best and the worst percentage in the column.
Source: See TAB A-5.1
TABLE 5.5: Granting of the superbonus to Disciplinary Groups, broken down by Gender
The superbonus would be granted when a majority of students score strictly higher on the TECO than the ITA12 median (>1016 for M+F, >1013 for F and >1017 for M).
Disciplinary Group (Macro-group)
TECO median% students scoring higher than the relevant ITA12 median (**)
SuperbonusM + F F M
med (SAN)(+)(*) 1085 66.67 66.83 66.49 YES
mat.fis.stat (SC)(+)(*) 1052 60.57 62.41 59.51 YES
sto (H)(+)(*) 1019 56.14 65.00 51.35 YES
psic (SOC)(+)(*) 1019 56.02 54.09 65.63 YES
odon (SAN)(+)(*) 1019 54.55 66.67 48.28 YES
filo (H)(+)(*) 1018 53.70 54.93 51.35 YES
giu (SOC)(+)(*) 1018 53.43 52.78 54.70 YES
polit (SOC)(+)(*) 1018 52.74 52.21 53.41 YES
vet (SAN)(+)(*) 1020 51.61 52.24 50.00 YES
bio (SC)(*) 1014 49.22 49.70 48.28 NO
chim (SC) 1001 49.06 35.56 59.02 NO
agr.al (SC) 1014 48.92 54.39 45.12 NO
ing (SC)(*) 1014 48.81 46.46 49.70 NO
arch (SC)(*) 1015 48.16 48.82 47.06 NO
comun (SOC) 1014 48.09 41.77 57.69 NO
lett (H)(*) 986 47.89 51.77 36.73 NO
econ (SOC) 986 47.10 43.67 50.91 NO
ling (H) 985 46.75 45.50 52.38 NO
art (H) 981 43.75 41.67 50.00 NO
farm (SAN) 982 43.15 43.06 43.40 NO
cult (H) 983 41.55 40.00 46.88 NO
terr (SC) 948 35.55 35.96 35.11 NO
geo (SOC) 978 33.96 35.90 28.57 NO
soc (SOC) 948 31.65 33.33 14.29 NO
form (H) 881 25.78 25.81 25.00 NO
ITA12 1016 49.46 48.20 51.30(+): Disciplinary Groups with a TECO median higher than than the ITA12 median(*): Disciplinary Groups with a TECO mean higher than than the ITA12 mean
(**): Percentages are calculated on the respective total number of tested students (M+F, F, M)
The grey highlighting indicates cases for which the superbonus would be granted based on the contribution of only one genderSource: See TABLE A-5.2
161
10
20
30
40
50
60
70M + F F M
% s
tud
ents
sco
rin
g h
igh
er t
han
th
e re
leva
nt
ITA
12
med
ian
(**
)
TABLE 5.6: Percentage of students scoring higher than the relevant ITA12 median in each Disciplinary Group, broken down by Gender
Source: See TABLE 5.5
TABLE 5.7: Granting of the superbonus to Macro-groups, broken down by Gender
The superbonus would be granted when a majority of students score strictly higher on the TECO than the ITA12
median (>1016 for M+F, >1013 for F and >1017 for M).
Macro-groupTECO
median
% students scoring higher than the relevant ITA12
median (**)Superbonus
M + F F M
SAN (+)(*) 1019 54.55 53.22 56.73 YES
SC 989 48.44 47.49 49.23 NO
SOC (+)(*) 1017 50.51 48.98 53.27 YES
H 984 44.40 43.61 46.88 NO
ITA12 1016 49.46 48.20 51.30
(+): Macro-groups with a TECO median higher than the ITA12 TECO median
(*): Macro-groups with a TECO mean higher than the ITA12 TECO mean
(**): Percentages are calculated on the respective total number of tested students (M+F, F, M)
Source: See TABLE A-5.2
40
42
44
46
48
50
52
54
56
58
60
SAN SC SOC H
M + F F M
% s
tud
ents
sco
rin
g h
igh
er t
han
th
e re
leva
nt
ITA
12
med
ian
(**
)
162
TABLE 5.8: Granting of the superbonus to Universities, broken down by Gender
The superbonus would be granted when a majority of students score strictly higher on the TECO than the ITA12 median (>1016
for M+F, >1013 for F and >1017 for M).
UniversityTECO
median
% students scoring higher than the relevant ITA12
median (**)Superbon
usM + F F M
PO 983 46.39 47.75 43.30 NO
MI (+)(*) 1052 59.27 58.30 60.86 YES
PD (+)(*) 1020 55.37 57.67 51.27 YES
UD (+)(*) 1021 58.54 56.91 61.32 YES
BO (+)(*) 1052 62.23 60.44 65.03 YES
FI (+)(*) 1019 56.44 54.55 58.98 YES
RM1 983 43.93 42.01 46.30 NO
RM2 983 42.62 33.33 52.22 NO
NA 980 40.24 37.39 44.13 NO
LE 950 29.94 30.53 29.03 NO
ME 911 27.48 21.35 40.48 NO
CA 986 45.74 44.00 48.15 NO
ITA12 1016 49.46 48.20 51.30
(+): Universities with a TECO median higher than than the ITA12 TECO median(*): Universities with a TECO mean higher than than the ITA12 TECO mean
(**): Percentages are calculated on the respective total number of tested students (M+F, F, M)
The grey highlighting indicates cases for which the superbonus would be granted based on the contribution of only one gender
Source: See TABLE A-5.2
% s
tud
ents
sco
rin
g h
igh
er t
han
th
e re
leva
nt
ITA
12
med
ian
(**
)
20
25
30
35
40
45
50
55
60
65
70
PO MI PD UD BO FI RM1 RM2 NA LE ME CA
M + F F M
TABLE 5.9: Granting of the superbonus to Geographic Areas, broken down by Gender
The superbonus would be granted when a majority of students score strictly higher on the TECO than the ITA12 median (>1016
for M+F, >1013 for F and >1017 for M).
Geographic AreaTECO
median
% students scoring higher than the relevant ITA12
median (**)Superbo
nusM + F F M
NORTH (+)(*) 1020 55.97 56.04 55.82 YES
CENTRE 1016 49.15 47.11 51.77 NO
SOUTH 951 37.66 34.73 41.98 NO
CENTRE-NORTH (+)(*)
1021 56.84 56.26 57.79 YES
CENTRE-SOUTH 983 41.64 38.80 45.32 NO
ITA12 1016 49.46 48.20 51.30
(+): Geographic Area with a TECO median higher than the ITA12 TECO median
(*): Geographic Area with a TECO mean higher than the ITA12 TECO mean
(**): Percentages are calculated on the respective total number of tested students (M+F, F, M)
The grey highlighting indicates cases for which the superbonus would be granted based on the contribution of only one gender
Source: See TABLE A-5.2
30
35
40
45
50
55
60
NORD (+)(*) CENTRO SUD
M+F F M
% s
tud
ents
sco
rin
g h
igh
er t
han
th
e re
leva
nt
ITA
12
med
ian
(**
)
163
TABLE A-5.1: Top, high and low performers per Disciplinary Group, Macro-group, University and Geographic Area, broken down by Gender
Disciplinary group # Top performers # High performers # Low performers
Macro-group# Top performers # High performers # Low performers
M + F F M M + F F M M + F F M M + F F M M + F F M M + F F M
agr.al 4 2 2 9 2 7 18 4 14 SAN 42 20 22 117 61 56 79 47 32
arch 8 5 3 20 12 8 12 7 5 SC 81 36 45 178 80 98 205 89 116
art 0 0 0 2 2 0 10 9 1 SOC 60 32 28 169 89 80 193 129 64
bio 17 10 7 29 21 8 23 11 12 H 25 13 12 54 37 17 97 80 17
chim 2 0 2 6 1 5 9 6 3 ITA12 208 101 107 518 267 251 574 345 229
comun 2 2 0 8 4 4 17 9 8
cult 4 2 2 5 3 2 12 9 3University
# Top performers # High performers # Low performers
econ 13 6 7 36 14 22 52 35 17 M + F F M M + F F M M + F F M
farm 11 5 6 30 18 12 50 32 18 PO 9 4 5 26 14 12 22 12 10
filo 5 3 2 8 6 2 8 3 5 MI 40 17 23 93 55 38 53 32 21
form 1 1 0 3 3 0 28 28 0 PD 28 16 12 70 46 24 45 25 20
geo 0 0 0 2 2 0 11 8 3 UD 8 5 3 27 12 15 18 9 9
giu 32 16 16 81 38 43 79 52 27 BO 22 7 15 55 26 29 17 9 8
ing 14 5 9 37 11 26 36 13 23 FI 32 21 11 78 45 33 48 28 20
lett 5 2 3 15 10 5 12 11 1 RM1 43 20 23 106 50 56 207 118 89
ling 6 5 1 14 11 3 20 17 3 RM2 3 0 3 12 3 9 22 12 10
mat.fis.stat 30 10 20 60 23 37 26 10 16 NA 13 5 8 33 7 26 82 59 23
med 28 14 14 72 33 39 15 8 7 LE 3 3 0 7 5 2 27 18 9
odon 1 1 0 5 2 3 5 2 3 ME 2 1 1 3 1 2 19 14 5
polit 6 1 5 20 11 9 22 13 9 CA 5 2 3 8 3 5 14 9 5
psic 4 4 0 17 15 2 4 4 0 ITA12 208 101 107 518 267 251 574 345 229
soc 3 3 0 6 6 0 11 11 0
sto 4 0 4 6 1 5 6 2 4Geographic Area
# Top performers # High performers # Low performers
terr 6 4 2 17 10 7 79 36 43 M + F F M M + F F M M + F F M
vet 2 0 2 10 8 2 9 5 4 NORTH 85 42 43 216 127 89 138 78 60
ITA12 208 101 107 518 267 251 574 345 229 CENTRE 100 48 52 251 124 127 294 167 127
A “top performer” is a student who scores higher than the ITA12 mean score in the 10th decile (> 1270.77)
SOUTH 23 11 12 51 16 35 142 100 42
A “high performer” is a student who scores higher than the ITA12 mean score in the 4th quartile (> 1196.71)
CENTRE-NORTH 139 70 69 349 198 151 203 115 88
A “low performer” is a student who scores lower than the ITA12 mean score in the 1st quartile (< 793.24)
CENTRE-SOUTH 69 31 38 169 69 100 371 230 141
See TABs 5.1, 5.2, 5.3, 5.4 ITA12 208 101 107 518 267 251 574 345 229
TABLE A-5.2: Number of students scoring higher than the relevant ITA12 median per Disciplinary Group, Macro-group, University and Geographic Area, broken down by
Gender
Disciplinary group M + F F M Macro-group M + F F M
agr.al 68 31 37 SAN 504 306 198
arch 131 83 48 SC 979 436 543
art 28 20 8 SOC 1000 625 375
bio 126 84 42 H 412 307 105chim 52 16 36 ITA12 2895 1674 1221
comun 63 33 30
cult 59 44 15 University M + F F M
econ 219 107 112 PO 148 106 42
farm 170 124 46 MI 473 288 185filo 58 39 19 PD 304 203 101
form 33 32 1 UD 168 103 65
geo 18 14 4 BO 229 136 93
giu 467 304 163 FI 390 216 174
ing 226 59 167 RM1 728 384 344lett 91 73 18 RM2 78 31 47
ling 108 86 22 NA 235 126 109
mat.fis.stat 235 88 147 LE 47 29 18
med 262 137 125 ME 36 19 17
odon 24 10 14 CA 59 33 26polit 106 59 47 ITA12 2895 1674 1221
psic 107 86 21
soc 25 24 1 Geographic Area M + F F M
sto 32 13 19 NORTH 1093 700 393
terr 139 73 66 CENTRE 1425 767 658vet 48 35 13 SOUTH 377 207 170
ITA12 2895 1674 1221 CENTRE-NORTH 1712 1052 660
CENTRE-SOUTH 1183 622 561
See TABs 5.5, 5.6, 5.7, 5.8 ITA12 2895 1674 1221
164
TABLE 6.1.3: PT, SRQ and TECO results, mean diploma grade (VMD) and mean grade in university exams sat so
far (VME), by language spoken at home
Language spoken at
homeT PT SRQ TECO VMD VME
Italian 54771004.28
(**)1004.58
(**)1004.50
(**)83.04 (**)
26.64 (**)
Not Italian 376 929.18 925.15 927.25 79.83 25.85
ITA12 5853 999.46 999.48 999.53 82.84 26.59
(**): Means are significantly different (95% confidence interval) for PT, SRQ, TECO, VMD and VME between the pairs “Italian”/”Not Italian”
Source: See TABLE 3
800
850
900
950
1000
1050
PT SRQ TECO
Italiano Non italiano
0
10
20
30
40
50
60
70
80
90
VMD VME800
850
900
950
1000
1050
PT SRQ TECO
Italiana Non italiana
0
10
20
30
40
50
60
70
80
90
VMD VME
TABLE 6.1.4: PT, SRQ and TECO results, mean diploma grade (VMD) and mean grade in university exams sat so far
(VME), by citizenship
Citizenship T PT SRQ TECO VMD VME
Italian 57441002.53
(**)1002.68
(**)1002.67
(**)82.93 (**)
26.62 (**)
Not Italian 109 837.39 830.77 834.17 77.08 25.18
ITA12 5853 999.46 999.48 999.53 82.84 26.59
(**): Means are significantly different (95% confidence interval) for PT, SRQ, TECO, VMD and VME between the pairs “Italian”/”Not Italian”
Source: See TABLE 3
TABLE 6.1.6: PT, SRQ and TECO results, by residence in the same region as the university (*) or elsewhere
Residence in the same region as the university
T PT SRQ TECO
NO 965 988.96 985.76 987.41
YES 4888 1001.531002.18
(**)1001.93
(**)
ITA12 5853 999.46 999.48 999.53
(*): Residence in the same region as the university is calculated based on the student’s region of residence and the region where the university has its main facilities.
(**): Means are significantly different (95% confidence interval) for PT, SRQ and TECO between the pairs “NO”/”YES”
Source: See TABLE 3
980
985
990
995
1000
1005
1010
PT SRQ TECO
NO SI
TABLE 6.1.5: PT, SRQ and TECO results, by off-site condition (*)
Off-site student
T PT SRQ TECO
NO 3119 1000.34 1003.68 1002.08
YES 2734 998.44 994.68 996.62
ITA12 5853 999.46 999.48 999.53
(*): Off-site students are defined as those whose declared official place of residence is different from the location of the university, at a distance of more than 20 km. There are no significant differences between the mean TECO results (95% confidence interval).
Source: See TABLE 3
980
985
990
995
1000
1005
1010
PT SRQ TECO
NO SIYES
165
TABLE 6.1.7: PT, SRQ and TECO results, by citizenship, language spoken at home and residence
CitizenshipLanguage spoken
at home
Residence in the same region as the
university
Citizenship; Language spoken at home; residence in the same
region T PT SRQ TECO mean
TECO median
Italian Italian YES CIT;LIT;SI (+)(*) 4562 1006.79(**) 1008.12(**) 1007.53(**) 1017
Italian Italian NO CIT;LIT;NO 889 993.92 990.09 992.06 985
Italian Not Italian YES CIT;LNIT;SI 240 957.46(**) 950.98(**) 954.32(**) 980
Italian Not Italian NO CIT;LNIT;NO 53 984.21 978.98 981.64 1013
Not Italian Italian YES CNIT;LIT;SI 22 914.05 891.41 902.77 967
Not Italian Italian NO CNIT;LIT;NO 4 937.25 802.00 869.75 842
Not Italian Not Italian YES CNIT;LNIT;SI 64 821.55(**) 808.70(**) 815.25(**) 810
Not Italian Not Italian NO CNIT;LNIT;NO 19 781.00(**) 840.95(**) 810.95(**) 847
ITA12 5853 999.46 999.48 999.53 1016CIT: Italian citizenship; CNIT: Foreign citizenship; LIT: Italian language; LNIT: Foreign language
Residence in the same region as the university is calculated based on the student’s region of residence and the region where the university has its main facilities.
(**): Means significantly different (95% confidence interval) versus the ITA12 mean, if sample size > 30 Moreover, a grey background is used to show significantly different means (95% confidence interval), if sample size > 30, between the following combinations: Italian citizenship, Italian spoken at home, residence YES/NO; Italian citizenship, foreign language spoken at home, residence YES/NO; foreign citizenship, Italian spoken at home, residence YES/NO; foreign citizenship, foreign language spoken at home, residence YES/NO – within PT, SRQ and TECO
Source: See TABLE 3.
CIT;LIT;SI (+)(*)
CIT;LIT;NO
CIT;LNIT;SI
CIT;LNIT;NO
CNIT;LIT;SI
CNIT;LIT;NOCNIT;LNIT;SI
CNIT;LNIT;NO
ITA12
-250
-200
-150
-100
-50
0
50
-250 -200 -150 -100 -50 0 50
SRQPT
TABLE 6.1.12 bis: Note on preceding TABLE 6.1.12 Classification of parent’s study qualification
STUDY QUALIFICATIONAGGREGATED STUDY QUALIFICATION AND ASSOCIATED SYMBOL
SUFFIXES
None NO (no parent) : NONOm=missing mother; NOp=missing father
Primary or lower secondary school certificate E-M certificate: EM
EMm=mother with primary school certificate; EMp=father with primary school certificate
High school diploma Diploma: DDm=mother with diploma; Dp=father with diploma
Degree
Degree (or post-degree): LLm=mother with at least a degree; Lp=father with at least a degree
Specialist degree
Doctorate
Post-graduate degree
Master
Classification of parent’s profession
PROFESSIONAGGREGATED PROFESSION AND
ASSOCIATED SYMBOLSUFFIXES
if STUDY QUALIFICATION = none and PROFESSION = any NO (neither parent): NONOm=missing mother; NOp=missing father
if STUDY QUALIFICATION >< none, the PROFESSION is:
Legislators, senior officials, corporate managers Managerial/professional: DIRDIRm=mother managerial/professional; DIRp=father managerial/professional
Intellectual, scientific and highly specialized professions
Armed forces
White-collar worker IIm=mother white-collar worker;Ip=father white-collar worker
Clerical support workers
Service and sales workers
Technicians
Craft and related trades workers, agricultural workers
Labourer: OOm=mother labourer; Op=father labourer
Plant and machine operators, assemblers, drivers
Elementary occupations
None Unemployed ININm=mother unemployed; INp=father unemployed
166
TABLE 6.1.13: Mean diploma grade (VMD) and mean grade in university exams sat so far (VME), by parent’s profession and study qualification
Mother Father Mother; Father T VMD VME
Managerial/professional or white-
collar workerAny DIR_Im;Qp(+)(*) 3389 82.85 26.68 (**)
Labourer or unemployed
Any O_INm;Qp 2333 82.91 26.48(**)
Degree or diploma
Any L_Dm;Qp(+)(*) 4413 82.93 26.66
Primary or lower secondary school
certificateAny EMm;Qp 1309 82.69 26.39(**)
AnyManagerial/profess
ional or white-collar worker
Qm;DIR_Ip(+)(*) 3937 82.82 26.67 (**)
AnyLabourer or unemployed
Qm;O_INp 1777 82.98 26.42(**)
Any Degree or diploma Qm;L_Dp(+)(*) 4319 82.91 26.65 (**)
AnyPrimary or lower secondary school
certificateQm;EMp 1395 82.77 26.42(**)
NO Any NOm;Qp 126 81.39 26.43
Any NO Qm;NOp 134 81.55 26.49
ITA12 5853 82.84 26.59
The classification used is detailed in the preceding TABLE 6.1.12bis.Father (or Mother) = "Any” indicates an aggregate of all possible conditions concerning the father’s (or mother’s) profession or study qualification.
(**): Means significantly different versus the mean for ITA12 (95% confidence interval) In addition, a grey background is used to show significantly different means (95% confidence interval) between the pairs <mother managerial/professional or white-collar worker/father any, mother labourer or unemployed/father any>, <mother degree or diploma/father any, mother primary or lower secondary certificate/father any> and the other equivalent pairs (<mother any/father’s profession>, <mother any/father’s study qualification>) –within VME. The differences are not significant for VMD.
Source: See TABLE 3
DIR_Im;Qp(+)(*)
O_INm;Qp
L_Dm;Qp(+)(*
)
EMm;Qp
NOm;Qp
Qm;DIR_Ip(+)(*)
Qm;O_INp
Qm;L_Dp(+)(*
)
Qm;EMp
Qm;NOp
ITA12
-0,25
-0,20
-0,15
-0,10
-0,05
0,00
0,05
0,10
-2,0 -1,5 -1,0 -0,5 0,0 0,5
VMD
VME
TABLE 6.1.14: PT, SRQ and TECO results, by parents’ type of employment contract (aggregated)
Mother’s type of contract Father’s type of contractMother’s contract; Father’s contract
T PT SRQ TECO TECO median
Permanent Permanent Ind; Ind(+)(*) 1963 1017.50** 1017.08** 1017.35** 1018
Permanent Fixed-term Ind; Det(+)(*) 66 1027.12 983.92 1005.64 1018
Permanent Self-employed Ind; Aut(+)(*) 627 1013.49 1007.37 1010.50 1018
Permanent None Ind; N(+)(*) 262 1005.13 1012.69 1008.97 1018.5
Fixed-term Permanent Det; Ind 130 1011.00 975.93 993.57 1016
Fixed-term Fixed-term Det; Det 54 945.28 951.69 948.52** 950
Fixed-term Self-employed Det; Aut(+)(*) 84 1013.10 1008.51 1010.85 1017
Fixed-term None Det; N(+) 34 992.03 1006.53 999.35 1018.5
Self-employed Permanent Aut; Ind(+)(*) 233 1004.36 1004.74 1004.59 1018
Self-employed Fixed-term Aut; Det 19 845.16 925.89 885.53 882
Self-employed Self-employed Aut; Aut(+) 390 994.82 1003.32 999.14 1017
Self-employed None Aut; N 52 933.10** 964.10 948.75** 948
None Permanent N; Ind 918 992.09 997.27 994.75 985
None Fixed-term N; Det 79 971.72 945.57** 958.75** 982
None Self-employed N; Aut 446 979.05** 998.55 988.84 986
None None N; N 491 954.85** 938.12** 946.57** 950
ITA12 5853 999.46 999.48 999.53 1016
The information on mother’s and father’s type of employment contract is missing in 5 cases
(**): Means significantly different (95% confidence interval) versus the ITA12 mean, if sample size > 30
Source: See TABLE A-6.1.12 and A-6.1.13
Acronyms for type of employment contract: Ind = permanent; Det = fixed-term; Aut = self-employed; N = none
167
850
900
950
1000
1050
Ind;Ind
Ind;Det
Ind;Aut
Ind;N
Det;Ind
Det;Det
Det;Aut
Det;N
Aut;Ind
Aut;Det
Aut;Aut
Aut;N
N;Ind
N;Det
N;Aut
N;N
TECO
800
850
900
950
1000
1050
Ind;Ind
Ind;Det
Ind;Aut
Ind;N
Det;Ind
Det;Det
Det;Aut
Det;N
Aut;Ind
Aut;Det
Aut;Aut
Aut;N
N;Ind
N;Det
N;Aut
N;N
PT
850
900
950
1000
1050
Ind;Ind
Ind;Det
Ind;Aut
Ind;N
Det;Ind
Det;Det
Det;Aut
Det;N
Aut;Ind
Aut;Det
Aut;Aut
Aut;N
N;Ind
N;Det
N;Aut
N; N
SRQ
TABLE 6.1.14 (continued): PT, SRQ and TECO results, by parents’ type of employment contract
(aggregated)
Ind; Ind(+)(*)
Ind; Det(+)(*)
Ind; Aut(+)(*)
Ind; N(+)(*)
Det; Ind
Det; Det
Det; Aut(+)(*)Det; N(+)
Aut; Ind(+)(*)
Aut; Det
Aut; Aut(+)
Aut; N
N; Ind
N; Det
N; Aut
N; N
ITA12
Correlation = 0,79y = 0,5188x - 5,8276
-100
-80
-60
-40
-20
0
20
40
-200 -150 -100 -50 0 50
PT
SRQ
TABLE 6.1.20: PT, SRQ and TECO results, by working student condition and entitlement to different types of supports for studying (aggregated)
Working student Types of supportWorking student; types of
supportT PT SRQ TECO mean TECO median
NO None LAV_NO;DS_NO (+)(*) 4176 1004.09 1005.97(**) 1005.10(**) 1017
NO At least one LAV_NO; DS_SI 920 993.56 985.24(**) 989.47 984
YES None LAV_SI; DS_NO 620 984.75 982.81(**) 983.84(**) 985
YES At least one LAV_SI; DS_SI 137 964.26 972.46 968.44(**) 984
ITA12 5853 999.46 999.48 999.53 1016
LAV_NO: Non working student; LAV_SI Working student; DS_NO: no entitlement to any form of support; DS_SI: beneficiary of at least one type of support (scholarship, student residence, meal vouchers, student collaboration contracts, other)
(**): Means significantly different (95% confidence interval) versus the ITA12 mean Moreover, a grey background is used to show significantly different means (95% confidence interval), between the pairs <Working student NO/Entitlement to types of support None, Working student NO/Entitlement to types of support At least one> and <Working student YES/Entitlement to types of support None, Working student YES/Entitlement to types of support At least one>– within PT, SRQ, TECO.
Source: See TABs A-6.1.14 to A-6.1.18 for non-aggregated results.
LAV_NO;DS_NO (+)(*)
LAV_NO; DS_SILAV_SI; DS_NO
LAV_SI; DS_SI
ITA12
-30
-25
-20
-15
-10
-5
0
5
10
-40 -35 -30 -25 -20 -15 -10 -5 0 5 10
SRQ
PT
168
TABLE A-6.1.1: PT, SRQ and TECO results, by number of household members
Number of household members
T PT SRQ TECOTECO
median
One(*) 83 1019.17 996.19 1007.71 1016
Two 332 1002.13 973.99 988.15 1014
Three(+)(*) 1439 1013.02 1013.20 1013.19 1018
Four 2858 1000.50 996.61 998.63 1016
Five 831 987.63 998.64 993.19 1011
Six 159 961.57 991.73 976.71 985
More than 6 41 925.07 919.41 922.29 949
ITA12 5853 999.46 999.48 999.53 1016
Source: See TABLE 3. See TABLE 6.1.1 for aggregated results
900
950
1000
1050
1100
Uno(*) Due Tre(+)(*) Quattro Cinque Sei più di 6
PT
900
950
1000
1050
1100
Uno(*) Due Tre(+)(*) Quattro Cinque Sei più di 6
SRQ
900
950
1000
1050
1100
Uno(*) Due Tre(+)(*) Quattro Cinque Sei più di 6
TECO
One(*)
Due
Three(+)(*)
Four
Five
Six
More than six
ITA12
-100
-80
-60
-40
-20
0
20
-80 -60 -40 -20 0 20 40
SRQ
PT
TABLE A-6.1.2: PT, SRQ and TECO results, by number of household members
Source: TABLE A-6.1.1
169
TABLE A-6.1.3: PT, SRQ and TECO results, by number of siblings in university or other higher education studies
Number of siblings T PT SRQ TECOTECO
median
None(*) 3330 1000.41 999.41 999.98 1016
One(+)(*) 2179 1000.08 1002.83 1001.53 1017
Two 305 995.82 984.06 989.97 1013
Three 31 909.19 963.97 936.58 950
Four(+)(*) 4 1157.50 942.50 1050.50 1067.5
Five 4 682.75 732.00 707.00 724
ITA12 5853 999.46 999.48 999.53 1016
Source: See TABLE 3. See TABLE 6.1.2 for aggregated results
900
950
1000
1050
1100
1150
1200PT
900
950
1000
1050
1100
1150
1200SRQ
900
950
1000
1050
1100
1150
1200TECO
None(*) One(+)(*)
TwoThree
Four(+)(*)
Five
ITA12
-300
-250
-200
-150
-100
-50
0
50
-400 -300 -200 -100 0 100 200
SRQ
PT
TABLE A-6.1.4: PT, SRQ and TECO results, by number of siblings in university or other higher education studies
Source: TABLE 6.1.3
170
TABLE A-6.1.5: PT, SRQ and TECO results, by number of technological devices used by students
Technological devices used
T PT SRQ TECO TECO median
PC;TA;SP(+)(*) 266 1013.78 1024.83 1019.39 1017.5
PC;TA(+)(*) 100 1037.60 1004.97 1021.36 1036
PC;SP(+)(*) 1199 1031.06 1031.00 1031.08 1021
TA;SP(+)(*) 10 974.60 1096.90 1035.70 1036.5
PC 3150 993.48 992.60 993.11 1000
TA 58 993.90 987.21 990.67 981.5
SP 155 977.97 996.63 987.34 984
None 915 974.54 974.07 974.38 983
ITA12 5853 999.46 999.48 999.53 1016
PC: personal computer; TA: tablet; SP: smartphone.
Source: See TABLE 3. See TABLE 6.1.9 for aggregated results
900
920
940
960
980
1000
1020
1040
1060
1080
1100
TECO
900
920
940
960
980
1000
1020
1040
1060
1080
1100PT
900
920
940
960
980
1000
1020
1040
1060
1080
1100 SRQ
TABLE A-6.1.6: PT and SRQ results, by number of technological devices used by students
PC;TA;SP(+)(*)
PC;TA(+)(*)
PC;SP(+)(*)
TA;SP(+)(*)
PC
TA
SP
None
ITA12
-40
-20
0
20
40
60
80
100
120
-30 -20 -10 0 10 20 30 40 50
PT
SRQ
Source: TABLE A-6.1.5
171
TABLE A-6.1.7: Mean diploma grade (VMD) and mean grade in university exams sat so far (VME), by number of
technological devices used by students
Technological devices used
T VMD VME
PC;TA;SP(+)(*) 266 82.01 26.33
PC;TA(+)(*) 100 85.10 26.63
PC;SP(+)(*) 1199 82.88 26.52
TA;SP(+)(*) 10 82.22 26.48
PC 3150 82.92 26.69
TA 58 83.60 26.27
SP 155 82.66 26.62
None 915 82.52 26.45
ITA12 5853 82.84 26.59
PC: personal computer; TA: tablet; SP: smartphone.
Source: See TABLE 3. See TABLE 6.1.9 for aggregated results
PC;TA;SP(+)(*)
PC;TA(+)(*)
PC;SP(+)(*)
TA;SP(+)(*)
PC
TA
SP
None
ITA12
-0,35
-0,30
-0,25
-0,20
-0,15
-0,10
-0,05
0,00
0,05
0,10
0,15
-1,0 -0,5 0,0 0,5 1,0 1,5 2,0 2,5
VMD
VME
80
81
82
83
84
85
86VMD
26,0
26,1
26,2
26,3
26,4
26,5
26,6
26,7
26,8VME
TABLE A-6.1.8: PT, SRQ and TECO results, by number of trips outside the region per year
Number of trips outside the region
per yearT PT SRQ TECO
None 1675 975.26 974.75 975.09
One 828 1002.21 992.26 997.31
Two 1057 1012.76 1015.60 1014.23
Three 749 1011.33 1006.18 1008.82
Four 413 1000.67 1009.74 1005.29
Five 394 1015.95 1012.51 1014.28
Six 141 1008.26 1010.91 1009.60
More than six 596 1011.29 1023.53 1017.47
ITA12 5853 999.46 999.48 999.53
Source: See TABLE 3. See TABLE 6.1.10 for aggregated results
940
950
960
970
980
990
1000
1010
1020
1030 PT
940
950
960
970
980
990
1000
1010
1020
1030 SRQ
940
950
960
970
980
990
1000
1010
1020
1030 TECO
None
One
Two(+)(*)
Three(+)(*)
Quattro (+)(*)Five(+)(*)
Six(+)(*)
More than six(+)(*)
ITA12
-30
-20
-10
0
10
20
30
-30 -20 -10 0 10 20
SRQ
PT
172
TABLE A-6.1.9: Mean diploma grade (VMD) and mean grade in university exams sat so far (VME),
by number of trips outside the region per year
Number of trips outside the region
per yearT VMD VME
None 1675 83.10 26.41
One 828 84.29 26.73
Two 1057 82.67 26.62
Three 749 82.83 26.65
Four 413 82.03 26.58
Five 394 82.00 26.62
Six 141 80.12 26.60
More than six 596 82.11 26.78
ITA12 5853 82.84 26.59
Source: See TABLE 3. See TABLE 6.1.10 for aggregated results
78
79
80
81
82
83
84
85VMD
26,2
26,3
26,4
26,5
26,6
26,7
26,8
26,9VME
None
One
Two(+)(*)
Three(+)(*)
Four(+)(*)
Five(+)(*)Six(+)(*)
More than six(+)(*)
ITA12
-0,25
-0,20
-0,15
-0,10
-0,05
0,00
0,05
0,10
0,15
0,20
0,25
-3,0 -2,5 -2,0 -1,5 -1,0 -0,5 0,0 0,5 1,0 1,5 2,0
VME
VMD
TABLE A-6.1.10: PT, SRQ and TECO results, by number of trips abroad per year
Number of trips abroad per year
T PT SRQ TECO
None 2597 979.16 978.82 979.08
One 2118 1014.18 1014.85 1014.57
Two 771 1010.85 1009.84 1010.40
Three 202 1041.48 1042.96 1042.26
Four 70 1046.30 1032.71 1039.50
Five 46 998.37 1011.13 1004.85
Six 15 1006.13 1038.33 1022.33
More than six 34 1025.97 1025.09 1025.65
ITA12 5853 999.46 999.48 999.53
Source: See TABLE 3. See TABLE 6.1.10 for aggregated results
940
960
980
1000
1020
1040
1060
PT
940
960
980
1000
1020
1040
1060
SRQ
940
960
980
1000
1020
1040
1060
TECO
None
One(+)(*)
Two(+)(*)
Three (+)(*)
Quattro (+)(*)
Five(+)(*)
Six(+)(*)
More than six(+)(*)
ITA12
Correlation = 0,80y = 0,7739x + 6,6434
-30
-20
-10
0
10
20
30
40
50
-30 -20 -10 0 10 20 30 40 50 60
SRQ
PT
173
TABLE A-6.1.11: Mean diploma grade (VMD) and mean grade in university exams sat so far (VME),
by number of trips abroad per year
Number of trips abroad per year
T VMD VME
None 2597 83.12 26.47
One 2118 82.80 26.68
Two 771 82.07 26.62
Three 202 82.18 26.89
Four 70 83.29 27.19
Five 46 82.46 26.58
Six 15 87.13 26.39
More than six 34 83.27 26.93
ITA12 5853 82.84 26.59
Source: See TABLE 3. See TABLE 6.1.10 for aggregated results
79
80
81
82
83
84
85
86
87
88VMD
25,8
26,0
26,2
26,4
26,6
26,8
27,0
27,2
27,4VME
None
One(+)(*)Two(+)(*)
Three(+)(*)
Four(+)(*)
Five(+)(*)
Six(+)(*)
More than six(+)(*)
ITA12
-0,3
-0,2
-0,1
0,0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
-2 -1 0 1 2 3 4 5
VME
VMD
TABLE A-6.1.12: PT, SRQ and TECO results, by parents’ type of employment contract
Father’s type of contract PT SRQ TECO TECO median T
Permanent (+)(*) 1009.10 (**) 1008.94 (**) 1009.09(**) 1017.5 2918
Fixed-term 97091(**) 956.98 (**) 964.03(**) 982 302
Self-employed (+)(*) 998.83 1003.87 1001.41 1017 694
None 970.71(**) 965.79(**) 968.33(**) 983 1934
ITA12 999.46 999.48 999.53 1016 5853
The information on father’s type of employment contract is missing in 5 cases
(**): Means significantly different (95% confidence interval) versus the ITA12 mean
Source: See TABLE 3. See TABLE 6.1.12 for aggregated results
950
960
970
980
990
1000
1010
1020
Tempoindeterminato
Tempodeterminato
Autonomo Nessuno
PT
920
940
960
980
1000
1020
Tempoindeterminato
Tempodeterminato
Autonomo Nessuno
SRQ
940
960
980
1000
1020
Tempoindeterminato
Tempodeterminato
Autonomo Nessuno
TECO
Permanent (+)(*)
Fixed-term
Self-employed (+)(*)
None
ITA12
Correlation = 0,98y = 1,3149x + 0,2297
-50
-40
-30
-20
-10
0
10
20
-40 -30 -20 -10 0 10 20
PT
SRQ
174
960
970
980
990
1000
1010
1020
Tempoindeterminato
Tempodeterminato
Autonomo Nessuno
PT
960
970
980
990
1000
1010
1020
Tempoindeterminato
Tempodeterminato
Autonomo Nessuno
SRQ
960
970
980
990
1000
1010
1020
Tempoindeterminato
Tempodeterminato
Autonomo Nessuno
TECO
TABLE A-6.1.13: PT, SRQ and TECO results, by mother’s type of employment contract
Mother’s type of contract PT SRQ TECOTECO
medianT
Permanent (+)(*) 1015.74(**) 1013.85(**) 1014.86(**) 1018 2918
Fixed-term 997.70 984.10 990.97 986 302
Self-employed (+) 989.30 998.74 994.09 1017 694
None 978.79(**) 980.44(**) 979.69(**) 983.5 1934
ITA12 999.46 999.48 999.53 1016 5853
The information on mother’s type of employment contract is missing in 5 cases
(**): Means significantly different (95% confidence interval) versus the ITA12 mean
Source: See TABLE 3. See TABLE 6.1.12 for aggregated results
Permanent (+)(*)
Fixed-term
Self-employed(+)
None
ITA12
Correlation = 0,81y = 0,7975x - 1,5582
-25
-20
-15
-10
-5
0
5
10
15
20
-30 -20 -10 0 10 20
SRQ
PT
TABLE A-6.1.15: PT, SRQ and TECO results, by scholarship holder status
SCHOLARSHIP
T PT SRQ TECO
NO 4982 1001.361002.17
(**)1001.83
(**)
YES 871 988.58 984.05 986.38
ITA12 5853 999.46 999.48 999.53
(**): Means are significantly different (95% confidence interval) for PT, SRQ and TECO between the pairs “NO”/”YES”
Source: See TABLE 3. See TABLE 6.1.16 for the corresponding graph
TABLE A-6.1.16: PT, SRQ and TECO results, by use of student residence status
USES STUDENT
RESIDENCET PT SRQ TECO
NO 5783 999.27 999.21 999.30
YES 70 1015.17 1021.79 1018.53
ITA12 5853 999.46 999.48 999.53
Source: See TABLE 3. See TABLE 6.1.17 for the corresponding graph
TABLE A-6.1.14: PT, SRQ and TECO results, by working student status
WORKING STUDENT
T PT SRQ TECO
NO 50961002.19
(**)1002.23
(**)1002.28
(**)
YES 757 981.05 980.94 981.05
ITA12 5853 999.46 999.48 999.53
(**): Means are significantly different (95% confidence interval) for PT, SRQ and TECO between the pairs “NO”/”YES” Source: See TABLE 3 (information provided by student). See TABLE 6.1.15 for the corresponding graph
TABLE A-6.1.17: PT, SRQ and TECO results, by entitlement to meal vouchers
MEAL VOUCHERS
T PT SRQ TECO
NO 5766 999.801000.61
(**)1000.27 (**)
YES 87 976.36 924.37 950.39
ITA12 5853 999.46 999.48 999.53
(**): Means are significantly different (95% confidence interval) for PT, SRQ and TECO between the pairs “NO”/”YES”
Source: See TABLE 3. See TABLE 6.1.18 for the corresponding graph
TABLE A-6.1.18: TECO, PT and SRQ results, by student collaboration contract status
STUDENT COLLABORATION CONTRACTS
T PT SRQ TECO
NO 5728 998.49 998.76 998.69
YES 1251043.92
(**)1032.06
1038.06 (**)
ITA12 5853 999.46 999.48 999.53
(**): Means are significantly different (95% confidence interval) for PT, SRQ and TECO between the pairs “NO”/”YES”
Source: See TABLE 3. See TABLE 6.1.19 for the corresponding graph
See TABLE 6.1.20 for aggregated data on all types of support
175
TABLE A-6.1.19: Percentage of working students among those who pre-registered, per Disciplinary group, Macro-group, University and Geographic Area, broken down by Gender
Disciplinary group % F+M % F % M Macro-group % F+M % F % M
agr.al 16.13 20.25 13.08 SAN 7.28 6.95 7.82
arch 11.04 10.83 11.45 SC 11.27 11.70 10.88
art 24.86 26.76 16.13 SOC 17.99 16.88 20.15
bio 14.32 12.81 17.04 H 18.15 19.02 15.26
chim 15.29 16.25 14.29 ITA12 14.11 14.42 13.63
comun 25.96 24.44 28.77
cult 15.98 17.05 11.63 University % F+M % F % M
econ 16.72 14.78 18.71 PO 16.01 15.13 18.18
farm 9.72 9.81 9.49 MI 23.26 23.77 22.44
filo 11.60 10.53 13.43 PD 12.33 13.41 10.34
form 27.81 27.44 40.00 UD 10.38 12.68 6.19
geo 20.39 22.73 16.22 BO 15.04 14.42 16.06
giu 15.16 15.63 14.19 FI 9.71 10.90 8.01
ing 6.81 6.25 7.05 RM1 14.52 14.34 14.78
lett 18.96 18.44 20.48 RM2 14.74 17.39 11.92
ling 14.85 15.38 12.12 NA 10.62 10.85 10.26
mat.fis.stat 13.00 14.71 12.18 LE 10.66 9.76 12.16
med 4.16 2.88 5.78 ME 7.73 8.13 6.90
odon 6.74 2.63 9.80 CA 10.20 9.40 11.39
polit 25.65 19.88 32.85 ITA12 14.11 14.42 13.63
psic 17.14 15.11 28.33
soc 24.03 21.74 42.86 Geographic Area % F+M % F % M
sto 16.85 21.88 14.04 NORTH 17.49 18.03 16.53
terr 10.58 10.46 10.73 CENTRE 13.66 13.86 13.37
vet 16.81 16.28 18.18 SOUTH 10.22 10.16 10.32
ITA12 14.11 14.42 13.63 CENTRE-NORTH 15.21 15.87 14.08
CENTRE-SOUTH 13.28 13.26 13.32
Source: See TABLE 3 ITA12 14.11 14.42 13.63
850
900
950
1000
1050
1100
SAN SC SOC H
Media TECO prog Media TECO non prog
850
900
950
1000
1050
1100
SAN SC SOC H
Media PT prog Media PT non prog
850
900
950
1000
1050
1100
SAN SC SOC H
Media SRQ prog
TABLE 6.2.2: PT, SRQ and TECO results per Macro-group and type of course followed (with admission test or not)
Prog PT, SRQ and TECO mean: mean PT, SRQ and TECO results for students following courses with a national or local admission testNon Prog PT, SRQ and TECO mean: mean PT, SRQ and TECO results for students following courses without an admission testThe differences between means are not significantSource: See TABLE A-6.2.3
176
TABLE 6.2.3: TECO result per University and type of course followed (with admission test or not)
900
920
940
960
980
1000
1020
1040
1060
1080
1100
PO MI PD BO UD FI RM1 RM2 NA LE ME CA ITA12
Media TECO Prog Media TECO non Prog
Prog TECO mean: mean TECO results for students following courses with a national or local admission testNon Prog TECO mean: mean TECO results for students following courses without a national or local admission testDifferences between means are significant for: PD, UDSource: See TABLE A-6.2.5. The graphs for PT and SRQ are shown respectively in TABs A-6.2.9 and A-6.2.10
Prog PT, SRQ and TECO mean: mean PT, SRQ and TECO results for students following courses with a national or local admission testNon Prog PT, SRQ and TECO mean: mean PT, SRQ and TECO results for students following courses without an admission testThe differences between means are not significantSource: See TABLE A-6.2.6
TABLE 6.2.4: PT, SRQ and TECO results per Geographic Area and type of course followed (with admission test or not)
900
920
940
960
980
1000
1020
1040
1060
1080 Media PT prog Media PT non prog
900
920
940
960
980
1000
1020
1040
1060
1080Media SRQ prog
900
920
940
960
980
1000
1020
1040
1060
1080
NORD CENTRO SUD CENTRO-NORD CENTRO-SUD
Media TECO prog
177
CLASSICAL (+)(*)SCIENTIFIC
(+)(*)
OTHER LYCEUM
TECH. INST.
VOCATIONAL INST.
OTHER INST. (*)
ITA12
-120
-100
-80
-60
-40
-20
0
20
40
-80 -60 -40 -20 0 20 40
SRQ
PT
CLASSICAL (+)(*)
SCIENTIFIC (+)(*)
OTHER LYCEUM
TECH. INST.
VOCATIONAL INST.
OTHER INST. (*)ITA12
-1,2
-1,0
-0,8
-0,6
-0,4
-0,2
0,0
0,2
0,4
0,6
-1,0 -0,5 0,0 0,5 1,0 1,5 2,0 2,5
VME
VMD
TABLE 6.2.5: PT, SRQ and TECO results, mean diploma grade (VMD) and mean grade in university exams sat so far (VME), by type of high school
Source: See TABLE A-6.2.11 Source: See TABLE A-6.2.12
TABLE 6.2.6: PT, SRQ and TECO results and mean diploma grade (VMD), per Disciplinary Group
agr.al
arch (*)
art
bio (*)
chim
comuncult
econ
farm
filo (+)(*)
form
geo
giu (+)(*)
ing (*)
lett (*)
ling
mat.fis.stat (+)(*)
med (+)(*)
odon (+)(*)
polit (+)(*)
psic (+)(*)
soc
sto (+)(*)
terr
vet (+)(*)
ITA12
y = 5,9576x - 1,3669
-120
-100
-80
-60
-40
-20
0
20
40
60
80
100
-10 -8 -6 -4 -2 0 2 4 6 8 10
Correlation: 0,61 TECO
VMD
agr.al
arch (*)
art
bio (*)
chim
comun cultecon
farm
filo (+)(*)
form
geo
giu (+)(*)
ing (*)
lett (*)
ling
mat.fis.stat (+)(*)
med (+)(*)
odon (+)(*)
polit (+)(*)psic (+)(*)
soc
sto (+)(*)
terr
vet (+)(*)
ITA12
y = 2,9888x - 0,2209
-80
-60
-40
-20
0
20
40
60
80
-10 -5 0 5 10
Correlation: 0,37
VMD
PT
agr.al
arch (*)
art
bio (*)chim
comun cult
econ
farm
filo (+)(*)
formgeo
giu (+)(*)
ing (*)
lett (*)
ling
mat.fis.stat (+)(*)
med (+)(*)
odon (+)(*)
polit (+)(*)
psic (+)(*)
soc
sto (+)(*)
terr
vet (+)(*)
ITA12
y = 8,9307x - 2,5045
-150
-100
-50
0
50
100
-10 -5 0 5 10
Correlation: 0,66 SRQ
VMD
Source: See TABLE A-6.2.13
178
TABLE 6.2.7: PT, SRQ and TECO results and mean grade in university exams sat so far (VME), per Disciplinary Group
agr.al
arch (*)
art
bio (*)
chim
comun
cult
econ
farm
filo (+)(*)
form
geo
giu (+)(*)
ing (*)
lett (*)
ling
mat.fis.stat (+)(*)
med (+)(*)
odon (+)(*)
polit (+)(*)
psic (+)(*)
soc
sto (+)(*)
terr
vet (+)(*)ITA12
y = 8,1789x - 8,6193
-120
-100
-80
-60
-40
-20
0
20
40
60
80
100
-1,5 -1,0 -0,5 0,0 0,5 1,0 1,5 2,0 2,5
Correlation: 0,20TECO
VME
agr.alarch (*)
art
bio (*)
chim
comun cultecon
farm
filo (+)(*)
form
geo
giu (+)(*)
ing (*)
lett (*)
ling
mat.fis.stat (+)(*)
med (+)(*)
odon (+)(*)
polit (+)(*)psic (+)(*)
soc
sto (+)(*)
terr
vet (+)(*)
ITA12
y = 14,872x - 6,5469
-80
-60
-40
-20
0
20
40
60
80
-2 -1 0 1 2 3
Correlation: 0,43PT
VME
agr.al
arch (*)
art
bio (*)chim
comuncult
econ
farm
filo (+)(*)
formgeo
giu (+)(*)
ing (*)
lett (*)
ling
mat.fis.stat (+)(*)
med (+)(*)
odon (+)(*)
polit (+)(*)
psic (+)(*)
soc
sto (+)(*)
terr
vet (+)(*)
ITA12
y = 1,4728x - 10,684
-150
-100
-50
0
50
100
-2 -1 0 1 2 3
Correlation: 0,03 SRQ
VME
Source: See TABLE A-6.2.13
agr.al
arch (*)
art
bio (*)
chim
comun
cult
econ
farm
filo (+)(*)
formgeo
giu (+)(*)
ing (*)lett (*)
ling
mat.fis.stat (+)(*)
med (+)(*)
odon (+)(*)
polit (+)(*)
psic (+)(*)
soc
sto (+)(*)
terr
vet (+)(*)
ITA12
-10
-8
-6
-4
-2
0
2
4
6
8
10
-1,5 -1,0 -0,5 0,0 0,5 1,0 1,5 2,0 2,5
VME
VMD
TABLE 6.2.8: Tested students’ mean diploma grade (VMD) and mean grade in university exams sat so far (VME), per Disciplinary Group
Source: See TABLE A-6.2.13
179
TABLE 6.2.9: PT, SRQ and TECO results and mean diploma grade (VMD), per Macro-group
SAN (+)(*)
SOC (+)(*)
SC
H
ITA12
-20
-15
-10
-5
0
5
10
15
20
25
-4 -2 0 2 4 6
TECO
VMD
SAN (+)(*)
SOC (+)(*)
SC
H
ITA12
-20
-15
-10
-5
0
5
10
15
20
-4 -2 0 2 4 6
VMD
PT
SAN (+)(*)
SOC (+)(*)
SC
H
ITA12
-50
-40
-30
-20
-10
0
10
20
30
40
-4 -2 0 2 4 6
SRQ
VMD
Source: See TABLE A-6.2.13
TABLE 6.2.10: PT, SRQ and TECO results and mean grade in university exams sat so far
(VME), per Macro-group
SAN (+)(*)
SOC (+)(*)
SC
H
ITA12
-20
-15
-10
-5
0
5
10
15
20
25
-0,6 -0,4 -0,2 0,0 0,2 0,4 0,6 0,8 1,0 1,2
TECO
VME
SAN (+)(*)
SOC (+)(*)
SC
HITA12
-20
-15
-10
-5
0
5
10
15
20
-0,5 0,0 0,5 1,0 1,5
PT
VME
SAN (+)(*)
SOC (+)(*)
SC
H
ITA12
-50
-40
-30
-20
-10
0
10
20
30
40
-0,5 0,0 0,5 1,0 1,5
SRQ
VME
Source: See TABLE A-6.2.13
180
PO
MI (+)(*)
PD (+)(*)UD (+)(*)
BO (+)(*)
FI (+)(*)
RM1RM2
NA
LE
ME
CA
ITA12
y = -3,8709x + 2,1144
-80
-60
-40
-20
0
20
40
60
-8 -6 -4 -2 0 2 4 6 8
Correlation: - 0,39 TECO
VMD
PO
MI (+)(*)
PD (+)(*)UD (+)(*)
BO (+)(*)
FI (+)(*)
RM1RM2
NA
LE
ME
CAITA12
y = -3,6997x + 3,1242
-80
-60
-40
-20
0
20
40
60
-10 -5 0 5 10
Correlation: - 0,39PT
VMD
PO
MI (+)(*)PD (+)(*)UD (+)(*)
BO (+)(*)FI (+)(*)
RM1RM2
NA
LE
ME
CAITA12
y = -4,0464x + 1,1083
-100
-80
-60
-40
-20
0
20
40
60
80
-10 -5 0 5 10
Correlation: - 0,37SRQ
VMD
TABLE 6.2.11: PT, SRQ and TECO results and mean diploma grade (VMD), per University
Source: See TABLE A-6.2.13
PO
MI (+)(*)
PD (+)(*)UD (+)(*)
BO (+)(*)
FI (+)(*)
RM1RM2
NA
LE
ME
CA
ITA12
y = -16,233x - 3,7208
-80
-60
-40
-20
0
20
40
60
-0,6 -0,4 -0,2 0,0 0,2 0,4 0,6 0,8
Correlation: - 0,16 TECO
VME
PO
MI (+)(*)
PD (+)(*)UD (+)(*)BO (+)(*)
FI (+)(*)
RM1RM2
NA
LE
ME
CA
ITA12
y = -23,444x - 1,7064
-80
-60
-40
-20
0
20
40
60
-1,0 -0,5 0,0 0,5 1,0
Correlation: - 0,24PT
VME
PO
MI (+)(*)
PD (+)(*)
UD (+)(*)
BO (+)(*)FI (+)(*)
RM1RM2
NA
LE
ME
CA
ITA12
y = -9,0762x - 5,7345
-100
-80
-60
-40
-20
0
20
40
60
80
-1,0 -0,5 0,0 0,5 1,0
Correlation: - 0,08SRQ
VME
TABLE 6.2.12: PT, SRQ and TECO results and mean grade in university exams sat so far (VME), per University
Source: See TABLE A-6.2.13
181
TABLE 6.2.13: Tested students’ mean diploma grade (VMD) and mean grade in university exams sat so far (VME), per University
PO MI (+)(*)
PD (+)(*)
UD (+)(*) BO (+)(*)
FI (+)(*)
RM1
RM2
NALE
ME
CA
ITA12
-8
-6
-4
-2
0
2
4
6
8
-0,6 -0,4 -0,2 0,0 0,2 0,4 0,6 0,8
VME
VMD
Source: See TABLE A-6.2.13
TABLE 6.2.14: PT, SRQ and TECO results and mean diploma grade (VMD), per
Geographic Area
NORTH(+)(*)
CENTRE
SOUTH
CENTRE-NORTH (+)(*)
CENTRE-SOUTH
ITA12
-50
-40
-30
-20
-10
0
10
20
30
40
-4 -2 0 2 4 6 8
TECO
VMD
NORTH (+)(*)
CENTRE
SOUTH
CENTRE-NORTH (+)(*)
CENTRE-SOUTH
ITA12
-40
-30
-20
-10
0
10
20
30
40
-4 -2 0 2 4 6 8
PT
VMD
NORTH (+)(*)
CENTRE
SOUTH
CENTRE-NORTH (+)(*)
CENTRE-SOUTH
ITA12
-50
-40
-30
-20
-10
0
10
20
30
40
-4 -2 0 2 4 6 8
SRQ
VMD
Source: See TABLE A-6.2.13
182
TABLE 6.2.15: PT, SRQ and TECO results and mean grade in university exams sat so far (VME), per Geographic Area
NORTH (+)(*)
CENTRE
SOUTH
CENTRE-NORTH (+)(*)
CENTRE-SOUTH
ITA12
-50
-40
-30
-20
-10
0
10
20
30
40
-0,2 -0,1 0,0 0,1 0,2 0,3 0,4
TECO
VME
NORTH (+)(*)
CENTRE
SOUTH
CENTRE-NORTH (+)(*)
CENTRE-SOUTH
ITA12
-40
-30
-20
-10
0
10
20
30
40
-0,2 -0,1 0,0 0,1 0,2 0,3 0,4
PT
VME
NORTH (+)(*)
CENTRE
SOUTH
CENTRE-NORTH (+)(*)
CENTRE-SOUTH
ITA12
-50
-40
-30
-20
-10
0
10
20
30
40
-0,2 -0,1 0,0 0,1 0,2 0,3 0,4
SRQ
VME
Source: See TABLE A-6.2.13
950
960
970
980
990
1000
1010
1020
PT SRQ TECO
Nessuna Almeno una
0
10
20
30
40
50
60
70
80
90
VMD VME
TABLE 6.2.16: PT, SRQ and TECO results, mean diploma grade (VMD) and mean grade in university exams sat so far (VME), by number of
other languages known (aggregated)Other languages
knownT PT SRQ TECO VMD VME
None 1641 974.33 970.84 972.67 82.00 26.45
At least one 4212 1009.24(**) 1010.63(**) 1010.00(**) 83.18(**) 26.65(**)
ITA12 5853 999.46 999.48 999.53 82.84 26.59
(**): Means are significantly different (95% confidence interval) for PT, SRQ, TECO, VMD and VME between the pairs “None”/”At least one”
Source: See TABLE A-6.2.14
None At least one
183
TABLE 6.2.17: PT, SRQ and TECO results, mean diploma grade (VMD) and mean grade in university exams sat so far (VME), by number of courses
taught in foreign language followed in Italy (aggregated)Number of courses
taught in foreign language followed
in Italy
T PT SRQ TECO VMD VME
None 4374 1003.46(**) 1007.88(**) 1005.74(**) 83.20(**) 26.65(**)
At least one 1479 987.61 974.61 981.19 81.78 26.42
ITA12 5853 999.46 999.48 999.53 82.84 26.59
(**): Means are significantly different (95% confidence interval) for PT, SRQ, TECO, VMD and VME between the pairs “None”/”At least one”
Source: See TABLE A-6.2.16
950
960
970
980
990
1000
1010
1020
PT SRQ TECO
Nessuno Almeno uno
0
10
20
30
40
50
60
70
80
90
VMD VME
None At least one
TABLE 6.2.18: PT, SRQ and TECO results, mean diploma grade (VMD) and mean grade in university exams sat so far (VME), by
number of courses followed abroad (aggregated)Number of
courses followed abroad
T PT SRQ TECO VMD VME
None 5593 999.06 999.44 999.32 82.90 26.58
At least one 260 1007.90 1000.27 1004.14 81.59 26.95(**)
ITA12 5853 999.46 999.48 999.53 82.84 26.59
(**): Means are significantly different (95% confidence interval) for PT, SRQ, TECO, VMD and VME between the pairs “None”/”At least one”
Source: See TABLE A-6.2.18
994
996
998
1000
1002
1004
1006
1008
1010
PT SRQ TECO
Nessuno Almeno uno
0
10
20
30
40
50
60
70
80
90
VMD VME
None At least one
184
TABLE 6.2.19: PT, SRQ and TECO results, mean diploma grade (VMD) and mean grade in university exams sat so far (VME), by number of months in
an Erasmus program abroad (aggregated)
Number of months in an
Erasmus programT PT SRQ TECO VMD VME
None 5556 998.04(**) 998.19(**) 998.19(**) 82.85 26.57(**)
At least one 297 1025.98 1023.43 1024.75 82.59 27.03
ITA12 5853 999.46 999.48 999.53 82.84 26.59
(**): Means are significantly different (95% confidence interval) for PT, SRQ, TECO, VMD and VME between the pairs “None”/”At least one”
Source: See following TABLE A-6.2.20
980
985
990
995
1000
1005
1010
1015
1020
1025
1030
PT SRQ TECO
Nessuno Almeno uno
0
10
20
30
40
50
60
70
80
90
VMD VME
None At least one
Number of months in an
Erasmus programT
% of total
Pre-registered
eligible students
% of total
none 5556 94.93 8654 94.37
one 223 3.81 372 4.06
two 18 0.31 32 0.35
three 7 0.12 11 0.12
four 4 0.07 8 0.09
five 11 0.19 23 0.25
six 14 0.24 26 0.28
More than 6 20 0.34 44 0.48
ITA12 5853 100.00 9170 100.00
Internationally mobile exiting students and participation percentages (academic years 2002/03 to 2011/12)
Academic yearExiting
students (*)
Total enrollment
Of which regularly
enrolled (**)
% exiting on enrolled
% exiting on
regularly enrolled
2002-03 16962 1768295 1003092 0.96 1.69
2003-04 14165 1102984 885595 1.28 1.6
2004-05 17546 1302432 950571 1.35 1.85
2005-06 18323 1451581 1007662 1.26 1.82
2006-07 20208 1538176 1033392 1.31 1.96
2007-08 21427 1602576 1046645 1.34 2.05
2008-09 22610 1647676 1040259 1.37 2.17
2009/10 26351 1668350 1038884 1.58 2.54
2010/11 30641 1676668 1031926 1.83 2.97
2011/12 30405 1668039 1024637 1.82 2.97
(*) Students in a LLP (Lifelong Learning Programme) consisting of:
• 4 sectoral programs
o Comenius: Schooling education
o Erasmus: Higher education and advanced training
o Leonardo da Vinci: vocational education and training
o Grundtvig: adult education
• A “Transversal” program aimed at coordinating the different sectoral programs,
• Jean Monnet program, to stimulate teaching, research and reflection activities in the field of European integration, and to support key European institutions
(**) Students enrolled since a number of years lower or equal to the course’s normal duration
Source: Household survey
TABLE 6.2.20: Note on preceding TABLE 6.2.19
185
TABLE A-6.2.1: PT, SRQ and TECO results per Disciplinary Group and type of course followed (with admission test or not)
Disciplinary group
TotalStudents enrolled in courses with a national or local
admission testStudents enrolled in courses without a national or
local admission test
T PT SRQ TECO T PT SRQ TECO T PT SRQ TECO
agr.al 139 981.22 986.72 984.01 16 1081.44 999.56 1040.50 123 968.19 985.05 976.66arch 272 989.99 1021.73 1005.94 272 989.99 1021.73 1005.94art 64 975.30 954.58 965.03 20 886.20 896.85 891.70 44 1015.80(**) 980.82 998.36(**)
bio 256 997.59 1015.21 1006.43 240 996.25 1016.85 1006.57 16 1017.75 990.75 1004.38chim 106 967.51 1023.29 995.45 45 1006.13 1085.18(**) 1045.73(**) 61 939.02 977.64 958.36
comun 131 989.09 966.36 977.81 25 1072.16(**) 1000.00 1036.24(**) 106 969.50 958.42 964.03cult 142 986.12 969.73 977.99 6 1044.67 1001.17 1023.00 136 983.54 968.34 976.01
econ 465 982.34 999.80 991.15 224 996.53 1015.20 1005.95 241 969.16 985.48 977.39farm 394 979.59 971.19 975.45 379 975.85 969.27 972.63 15 1073.93 1019.67 1046.87filo 108 1032.05 1004.23 1018.20 108 1032.05 1004.23 1018.20
form 128 932.83 873.38 903.28 30 906.57 851.20 879.07 98 940.87 880.17 910.69
geo 53 985.51 882.23 933.96 2 920.00 697.00 808.50 51 988.08 889.49(**) 938.88giu 874 1021.76 997.59 1009.73 874 1021.76 997.59 1009.73ing 463 980.10 1022.16 1001.20 191 968.46 994.85 981.74 272 988.28 1041.35(**) 1014.86(**)lett 190 1039.17 985.73 1012.51 190 1039.17 985.73 1012.51
ling 231 1000.38 970.34 985.38 81 962.07 978.48 970.23 150 1021.07(**) 965.94 993.56
mat.fis.stat 388 1027.57 1055.19 1041.43 5 1191.80 1047.60 1119.80 383 1025.42 1055.29 1040.40
med 393 1057.48 1086.91 1072.25 393 1057.48 1086.91 1072.25odon 44 1006.57 1023.75 1015.30 44 1006.57 1023.75 1015.30
polit 201 1015.05 997.13 1006.18 201 1015.05 997.13 1006.18psic 191 1020.74 1038.62 1029.75 191 1020.74 1038.62 1029.75soc 79 986.28 929.61 958.04 21 1007.43 1007.57(**) 1007.57 58 978.62 901.38 940.10sto 57 1036.86 985.00 1011.02 57 1036.86 985.00 1011.02terr 391 938.69 932.54 935.70 221 925.63 922.67 924.24 170 955.67 945.39 950.60
vet 93 982.90 1025.14 1004.11 93 982.90 1025.14 1004.11ITA12 5853 999.46 999.48 999.53 2499 994.27 1009.57 1001.99 3354 1003.32 991.96 997.70
The dark grey highlighting indicates the Disciplinary Groups for which there is a national admission test.The light grey highlighting indicates Disciplinary groups for which there is a local admission test for more than 50% of the eligible students in the 12 universities participating in the pilot test (ITA12). The Territory Group is considered to be in the same situation: 49.94% of students in the ITA12 are enrolled in courses with a local admission test and 21.39% of the same students are enrolled in courses with a national admission test. (**): indicates PT, SRQ and TECO means that are significantly different (95% confidence interval), betweenstudents enrolled in courses with an admission test and othersSource: Educational offer (CINECA OF), see TABLE 3. See TABLE 6.2.1 for the corresponding graph
none(+)(*)
onetwo
three
four
fivesix(+)(*)
more than six(+)(*)
ITA12
-80
-60
-40
-20
0
20
40
-20 -15 -10 -5 0 5 10
PT
SRQ
none(+)(*)
one
two
three
four
five
six(+)(*) more than six(+)(*)
ITA12
-0,6
-0,4
-0,2
0,0
0,2
0,4
0,6
0,8
1,0
-5 -4 -3 -2 -1 0 1 2 3
VMD
VME
TABLE A-6.2.17: PT, SRQ and TECO results, mean diploma grade (VMD) and mean grade in university exams sat so far (VME), by number of courses
taught in foreign language followed in Italy
Source: See TABLE A-6.2.16
186
TABLE A-6.2.2: Percentage of eligible students enrolled in courses with a national or local admission test per Disciplinary Group, broken down by University
Disciplinary group PO MI PD UD BO FI RM1 RM2 NA LE ME CA ITA12agr.al 5.00 25.53 0.00 0.00 0.00 0.00 0.00 5.47arch 100.00 100.00 100.00 100.00 100.00 100.00 100.00art 0.00 0.00 0.00 47.06 66.18 0.00 38.15cult 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 100.00 100.00 2.39bio 20.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 81.97 100.00 100.00 96.60
chim 0.00 85.37 100.00 0.00 0.00 0.00 0.00 93.75 100.00 44.49comun 100.00 100.00 0.00 0.00 0.00 0.00 0.00 0.00 50.00 9.52 10.54econ 0.00 100.00 100.00 100.00 91.32 0.00 34.07 100.00 100.00 100.00 0.00 0.00 60.66farm 98.68 93.97 100.00 20.45 96.77 100.00 100.00 100.00 100.00 100.00 91.39filo 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
form 0.00 31.25 0.00 100.00 91.30 0.00 100.00 35.81geo 0.00 0.00 0.00 0.00 0.00 0.00 0.00 100.00 0.00 100.00 9.66giu 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00ing 0.00 0.00 0.00 0.00 88.14 0.00 0.00 100.00 78.57 41.81lett 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00ling 0.00 26.85 0.00 15.15 100.00 0.00 93.70 0.00 0.00 100.00 100.00 0.00 41.73
mat.fis.stat 0.00 0.00 4.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 100.00 3.34med 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00odon 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00polit 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00psic 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00soc 0.00 75.00 29.13 0.00 44.59 48.08 19.35 0.00 100.00 32.10sto 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00terr 0.00 100.00 5.56 40.00 30.77 62.64 83.37 0.00 17.65 100.00 0.00 100.00 67.33vet 100.00 100.00 100.00 100.00 100.00 100.00
local admission test 12.85 14.62 12.63 16.29 29.34 10.54 20.06 17.22 17.78 0.00 20.95 21.57 17.98
national admission test
15.61 20.84 32.27 25.00 17.32 23.00 36.79 23.06 22.82 41.08 18.44 27.42 26.55
local or national admission test
28.46 35.46 44.90 41.29 46.65 33.54 56.85 40.28 40.59 41.08 39.39 48.99 44.52
The dark grey highlighting indicates the Disciplinary Groups for which there is a national admission test.The light grey highlighting indicates Disciplinary groups for which there is a local admission test for more than 50% of the eligible students in the 12 universities participating in the pilot test (ITA12). The Territory Group is considered to be in the same situation: 49.94% of students in the ITA12 are enrolled in courses with a local admission test and 21.39% of the same students are enrolled in courses with a national admission test. The data per column show the percentage of eligible students enrolled in courses with a local or national admission test in that University. The data per row show the percentage of eligible students enrolled in courses with a local or national admission test in that Disciplinary group for all 12 participating universities.
Source: See TABLE 3
TABLE A-6.2.3: PT, SRQ and TECO results per Macro-group and type of course followed (with admission test or not)
Macro-group
TotalStudents enrolled in courses with a
national or local admission testStudents enrolled in courses without a national
or local admission test
T PT SRQ TECO T PT SRQ TECO T PT SRQ TECO
SAN 924 1014.33 1028.34 1021.40 909 1013.3 1028.5 1021.0 15 1073.93 1019.67 1046.87
SC 2021 983.99 1007.37 995.74 990 976.2 995.9 986.1 1031 991.451018.38(**
)1004.97(**
)
SOC 1979 1007.59 995.33 1001.54 463 1010.8 1022.3(**) 1016.6(**) 1516 1006.63 987.09 996.93
H 929 1000.97 962.42 981.77 137 942.5 939.7 941.1 792 1011.09(**) 966.35 988.80(**)
ITA12 5853 999.46 999.48 999.53 2499 994.3 1009.6 1002.0 3354 1003.32 991.96 997.70
(**): indicates PT, SRQ and TECO means that are significantly different (95% confidence interval), betweenstudents enrolled in courses with an admission test and others
Source: See TABLE 3. See TABLE 6.2.2 for the corresponding graph
187
TABLE A-6.2.4: Percentage of eligible students enrolled in courses with a national or local admission test per Macro-group, broken down by University
NATIONAL ADMISSION TEST
Macro-group PO MI PD UD BO FI RM1 RM2 NA LE ME CA ITA12
SAN 46.10 65.39 40.78 100.00 85.71 78.17 83.45 95.51 46.51 68.18 71.78 71.13
SC 0.00 0.00 18.72 5.97 54.15 14.30 29.24 9.92 36.47 0.00 0.00 1.75 23.99
SOC 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
H 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Total University
12.85 14.62 12.63 16.29 29.34 10.54 20.06 17.22 17.78 0.00 20.95 21.57 17.98
LOCAL ADMISSION TEST
Macro-group PO MI PD UD BO FI RM1 RM2 NA LE ME CA ITA12
SAN 53.19 32.52 59.22 0.00 2.92 21.13 16.55 4.49 53.49 31.82 28.22 26.38
SC 9.52 61.61 22.37 34.33 8.52 31.40 41.41 25.47 17.81 97.14 58.82 92.98 34.45
SOC 0.00 3.97 47.66 35.75 30.52 20.64 35.66 45.79 19.02 21.81 8.24 12.86 24.00
H 0.00 6.56 0.00 10.99 8.84 4.71 42.51 0.00 0.00 71.56 11.48 20.51 20.72
Total University
15.61 20.84 32.27 25.00 17.32 23.00 36.79 23.06 22.82 41.08 18.44 27.42 26.55
The dark grey highlighting indicates the Disciplinary Groups for which there is a national admission test.
The light grey highlighting indicates the Disciplinary Groups for which there is a local admission test for more than 50% of the eligible students in that University
The data per column show the percentage of eligible students enrolled in courses with a local or national admission test in that University. The data per row show the percentage of eligible students enrolled in courses with a local or national admission test in that Disciplinary group for all 12 participating universities.
TABLE A-6.2.5: PT, SRQ and TECO results per University and type of course followed (with admission test or not)
University
TotalStudents enrolled in courses with a national
or local admission testStudents enrolled in courses without a
national or local admission test
T PT SRQ TECO T PT SRQ TECO T PT SRQ TECO
PO 319 1017.21 972.55 994.92 66 1040.52 1011.67 1026.11 253 1011.13 962.34 986.79
MI 798 1036.55 1032.39 1034.53 343 1027.82 1050.86(**) 1039.39 455 1043.12 1018.46 1030.86
PD 549 1024.14 1026.71 1025.49 249 1027.02 1054.83(**) 1041.00(**) 300 1021.75 1003.37 1012.62
BO 368 1039.69 1055.61 1047.73 214 1023.57 1069.43 1046.58 154 1062.09 1036.41 1049.32
UD 287 1020.45 1028.01 1024.28 108 1026.39 1086.83(**) 1056.65(**) 179 1016.86 992.51 1004.75
FI 691 1016.02 1033.26 1024.71 150 1018.35 1030.96 1024.71 541 1015.38 1033.90 1024.71
RM1 1657 974.09 977.86 976.04 879 965.30 976.09 970.77 778 984.01 979.86 982.00
RM2 183 984.34 978.00 981.22 46 974.74 976.04 975.54 137 987.56 978.66 983.13
NA 584 971.17 959.57 965.43 296 982.29 971.89 977.18 288 959.74 946.90 953.36
LE 157 940.89 938.68 939.92 48 955.50 946.96 951.29 109 934.46 935.04 934.91
ME 131 927.40 924.01 925.78 44 893.91 928.11 911.11 87 944.33 921.93 933.20
CA 129 990.66 981.41 986.10 56 996.52 987.66 992.18 73 986.16 976.62 981.44
ITA12 5853 999.46 999.48 999.53 2499 994.27 1009.57 1001.99 3354 1003.32 991.96 997.70
(**): indicates PT, SRQ and TECO means that are significantly different (95% confidence interval), betweenstudents enrolled in courses with an admission test and others
Source: See TABLE 3. See TABLE 6.2.3 for the corresponding graph
188
TABLE A-6.2.6: PT, SRQ and TECO results per Geographic Area and type of course followed (with admission test or not)
Geographic Area
TotalStudents enrolled in courses with a
national or local admission testStudents enrolled in courses without a national
or local admission test
T PT SRQ TECO T PT SRQ TECO T PT SRQ TECO
NORTH 1953 1027.53 1020.37 1024.01 766 1028.45 1053.85(**) 1041.20(**) 1187 1026.94 998.77 1012.92
CENTRE 2899 993.06 1000.94 997.07 1289 981.49 997.97 989.80 1610 1002.32(**) 1003.33 1002.89(**)
SOUTH 1001 963.20 954.45 958.90 444 972.43 966.85 969.72 557 955.85 944.57 950.28
CENTRE-NORTH
3012 1026.38 1027.64 1027.07 1130 1026.19 1053.76(**) 1040.03(**) 1882 1026.49 1011.95 1019.29
CENTRE-SOUTH
2841 970.91 969.62 970.34 1369 967.93 973.09 970.59 1472 973.69 966.39 970.10
ITA12 5853 999.46 999.48 999.53 2499 994.27 1009.57 1001.99 3354 1003.32 991.96 997.70
(**): indicates PT, SRQ and TECO means that are significantly different (95% confidence interval), betweenstudents enrolled in courses with an admission test and others
Source: See TABLE 3. See TABLE 6.2.4 for the corresponding graph
TABLE A-6.2.7: PT result per Disciplinary Group and type of course followed (with admission test or not)
800
850
900
950
1000
1050
1100
1150
1200Media PT Prog Media PT non Prog
Prog PT mean: mean PT results for students following courses with a national or local admission testNon Prog PT mean: mean PT results for students following courses without a national or local admission testDifferences between means are significant for: art, comun, linSource: See TABLE A-6.2.1
189
TABLE A-6.2.8: SRQ result per Disciplinary Group and type of course followed (with admission test or not)
600
700
800
900
1000
1100
1200Media SRQ Prog Media SRQ non Prog
Prog SRQ mean: mean SRQ results for students following courses with a national or local admission testNon Prog SRQ mean: mean SRQ results for students following courses without a national or local admission testDifferences between means are significant for: chim, geo, ing, socSource: See TABLE A-6.2.1
TABLE A-6.2.9: PT result per University and type of course followed (with admission test or not)
850
900
950
1000
1050
1100
PO MI PD BO UD FI RM1 RM2 NA LE ME CA ITA12
Media PT Prog Media PT non Prog
Prog PT mean: mean PT results for students following courses with a national or local admission testNon Prog PT mean: mean PT results for students following courses without a national or local admission testThe differences between means are not significantSource: See TABLE A-6.2.5
190
TABLE A-6.2.10: SRQ result per University and type of course followed (with admission test or not)
900
920
940
960
980
1000
1020
1040
1060
1080
1100
PO MI PD BO UD FI RM1 RM2 NA LE ME CA ITA12
Media SRQ Prog Media SRQ non Prog
Prog SRQ mean: mean SRQ results for students following courses with a national or local admission testNon Prog SRQ mean: mean SRQ results for students following courses without a national or local admission testDifferences between means are significant for: MI, PD, UDSource: See TABLE A-6.2.5
TABLE A-6.2.11: PT, SRQ and TECO results, by type of high school
Type of high school T PT SRQ TECOTECO
median
CLASSICAL LYCEUM (+)(*)
1433 1025.79 1021.10 1023.50 1020
SCIENTIFIC LYCEUM (+)(*)
2462 1006.36 1015.49 1011.00 1018
OTHER LYCEUM 451 986.22 955.03 970.73 982
TECHNICAL INSTITUTE 780 965.93 975.62 970.85 983
VOCATIONAL INSTITUTE
99 935.89 898.19 917.16 914
OTHER INSTITUTE (*) 325 1006.60 1004.38 1005.56 1016
ITA12 5853 999.46 999.48 999.53 1016
OTHER LYCEUM: artistic, European, linguistic, social sciencesTECHNICAL INSTITUTE: agricultural, aeronautical, commercial, industrial, nautical, building surveyors, tourism, social activitiesVOCATIONAL INSTITUTE: cinema and television, commercial/tourism/advertising, marine industries, trades industries, hotel, social services, agriculture, environmentOTHER INSTITUTE: boarding school, art institute, higher institute, foreign school, teacher training institute
Information on the type of high school is missing in 303 cases
Source: See TABLE 3.
880
920
960
1000
1040
PT
880
920
960
1000
1040
TECO
880
920
960
1000
1040SRQ
191
TABLE A-6.2.12: Mean diploma grade (VMD) and mean grade in university exams sat so far (VME), by type of high school
Type of high school T VMD VME
CLASSICAL LYCEUM (+)(*) 1433 83.58 27.11
SCIENTIFIC LYCEUM (+)(*) 2462 82.43 26.61
OTHER LYCEUM 451 82.64 26.50
TECHNICAL INSTITUTE 780 83.63 26.02
VOCATIONAL INSTITUTE 99 83.41 25.51
OTHER INSTITUTE (*) 325 85.16 26.63
ITA12 5853 82.84 26.59
Information on the type of high school is missing in 303 cases
Source: See TABLE 3
81
82
83
84
85
86VMD
25
25
26
26
27
27
28VME
TABLE A-6.2.13: PT, SRQ and TECO results, mean diploma grade (VMD) and mean grade in university exams sat so far (VME) per Disciplinary group, Macro-group, University and Geographic Area
Disciplinary group TECO PT SRQ VMD VME Macro-group TECO PT SRQ VMD VME
agr.al 984.01 981.22 986.72 82.35 26.51 SAN 1021.4 1014.33 1028.34 87.37 27.04
arch 1005.94 989.99 1021.73 80.15 27.03 SC 995.74 983.99 1007.37 82.77 26.26
art 965.03 975.3 954.58 76.61 27.61 SOC 1001.47 1007.55 995.25 81.83 26.21
bio 1006.43 997.59 1015.21 81.8 26.19 H 981.88 1001.06 962.56 80.7 27.67
chim 995.45 967.51 1023.29 84.51 26.16 ITA12 999.53 999.46 999.48 82.84 26.59
comun 977.81 989.09 966.36 75.16 25.94
cult 977.99 986.12 969.73 78.4 27.73 University TECO PT SRQ VMD VME
econ 991.15 982.34 999.8 82.59 25.49 PO 994.92 1017.21 972.55 82.84 26.08
farm 975.45 979.59 971.19 84.83 26.39 MI 1034.53 1036.55 1032.39 82.46 26.67
filo 1018.2 1032.05 1004.23 84 28.72 PD 1025.49 1024.14 1026.71 86.5 26.61
form 903.28 932.83 873.38 78.26 27.21 UD 1024.28 1020.45 1028.01 83.61 26.08
geo 933.96 985.51 882.23 78.39 26.35 BO 1047.73 1039.69 1055.61 82.86 27.29
giu 1009.73 1021.76 997.59 83.42 26.51 FI 1024.71 1016.02 1033.26 85.57 26.67
ing 1001.2 980.1 1022.16 85.25 25.48 RM1 976.04 974.09 977.86 75.68 26.34
lett 1012.51 1039.17 985.73 84.68 28.03 RM2 981.22 984.34 978 85.05 26.73
ling 985.38 1000.38 970.34 80.7 27.03 NA 965.43 971.17 959.57 89.36 26.85
mat.fis.stat 1041.43 1027.57 1055.19 83.63 26.43 LE 939.92 940.89 938.68 90.08 26.96
med 1072.25 1057.48 1086.91 90.41 27.63 ME 925.78 927.4 924.01 87.82 26.95
odon 1015.3 1006.57 1023.75 88.11 27.66 CA 986.1 990.66 981.41 86.99 27.11
polit 1006.18 1015.05 997.13 78.4 26.23 ITA12 999.53 999.46 999.48 82.84 26.59
psic 1029.75 1020.74 1038.62 82.36 26.63
soc 958.04 986.28 929.61 79.34 26.44 Geographic Area TECO PT SRQ VMD VME
sto 1011.02 1036.86 985 78.08 28.43 NORTH 1024.01 1027.53 1020.37 83.82 26.47
terr 935.7 938.69 932.54 81.07 26.46 CENTRE 997.07 993.06 1000.94 79.72 26.57
vet 1004.11 982.9 1025.14 85.77 27.03 SOUTH 958.9 963.2 954.45 88.97 26.91
ITA12 999.53 999.46 999.48 82.84 26.59 CENTRE-NORTH 1027.07 1026.38 1027.64 84.15 26.62
CENTRE-SOUTH 970.34 970.91 969.62 81.4 26.57
Source: See TABLE 3 ITA12 999.53 999.46 999.48 82.84 26.59
192
950
1000
1050
1100
1150
1200TECO
950
1000
1050
1100
1150
1200PT
950
1000
1050
1100
1150
1200SRQ
TABLE A-6.2.14: PT, SRQ and TECO results, mean diploma grade (VMD) and mean grade in university exams sat so far (VME), by number of other
languages known
Other languages known
T PT SRQ TECOTECO
medianVMD VME
None 1641 974.33 970.84 972.67 983 82 26.45
One language(+)(*)
3091 999.94 1006.73 1003.4 1017 83.1 26.61
Two languages(+)(*)
869 1036.32 1024.3 1030.36 1051 83.54 26.74
Three languages(+)(*)
232 1021.98 1011.8 1016.97 1018 82.89 26.73
Four languages(+)(*)
20 1123.75 1005.7 1064.85 1053 81.9 26.91
ITA12 5853 999.46 999.48 999.53 1016 82.84 26.59
Source: See TABLE 3. See TABLE 6.2.16 for aggregated results
81,50
82,00
82,50
83,00
83,50
84,00VMD
26,3
26,4
26,5
26,6
26,7
26,8
26,9
27,0VME
None
One(+)(*)
Two (+)(*)
Three(+)(*)
Four(+)(*)
ITA12
-40
-30
-20
-10
0
10
20
30
-50 0 50 100 150
PT
SRQ
None
One(+)(*)
Two(+)(*)
Three(+)(*)
Four(+)(*)
ITA12
-0,20
-0,15
-0,10
-0,05
0,00
0,05
0,10
0,15
0,20
0,25
0,30
0,35
-2 -1 -1 0 1 1
VMD
VME
TABLE A-6.2.15: PT, SRQ results, mean diploma grade (VMD) and mean grade in university exams sat so far (VME), by number of other languages known
Source: See TABLE A-6.2.14
193
800
850
900
950
1000
1050
1100TECO
800
850
900
950
1000
1050
1100PT
800
850
900
950
1000
1050
1100SRQ
TABLE A-6.2.16: PT, SRQ and TECO results, mean diploma grade (VMD) and mean grade in university exams sat so far (VME), by number of courses taught in foreign
language followed in Italy
Number of courses taught in foreign language followed in Italy
T PT SRQ TECOTECO
medianVMD VME
None(+)(*) 4374 1003.46 1007.88 1005.74 1017 83.20 26.65
one 1003 985.86 970.6 978.31 984 82.15 26.38
two 253 994.47 973.69 984.17 984 80.39 26.17
three 73 984.30 987.25 985.81 983 78.77 26.69
four 35 991.89 933.54 962.77 950 82.53 26.49
five 28 983.14 1005.21 994.25 1001 83.42 27.52
six(+)(*) 27 990.48 1008.78 999.74 1020 80.04 27.02
More than six(+)(*) 60 990.33 1024.48 1007.42 1035 84.80 26.98
ITA12 5853 999.46 999.48 999.53 1016 82.84 26.59
Source: See TABLE 3. See TABLE 6.2.17 for aggregated results
75
76
77
78
79
80
81
82
83
84
85
86VMD
25,00
25,50
26,00
26,50
27,00
27,50
28,00VME
800850900950
10001050110011501200
TECO
800850900950
10001050110011501200
PT
800850900950
10001050110011501200 SRQ
TABLE A-6.2.18: PT, SRQ and TECO results, mean diploma grade (VMD) and mean grade in university exams sat so far (VME), by number of courses followed abroad
Number of courses followed
abroadT PT SRQ TECO
TECO median
VMD VME
none 5593 999.06 999.44 999.32 1016 82.90 26.58
one 91 974.63 937.49 956.12 949 81.61 26.77
two(+) 42 960.57 1011 985.86 1052 77.11 26.71
three(+)(*) 40 1061.05 1088.1 1074.63 1086 84.68 27.28
four(+)(*) 29 1051.34 1047.83 1049.59 1085 78.89 26.96
five(+)(*) 14 1022.00 1002.71 1012.43 1019 88.21 27.14
six(+)(*) 16 1009.19 1003.94 1006.63 1017 75.63 26.69
More than six(+)(*)
28 1058.36 1010.11 1034.32 1052 86.48 27.46
ITA12 5853 999.46 999.48 999.53 1016 82.84 26.59
Source: See TABLE 3. See TABLE 6.2.18 for aggregated results
687072747678808284868890
VMD
26,0
26,2
26,4
26,6
26,8
27,0
27,2
27,4
27,6VME
194
Source: See TABLE A-6.2.18
TABLE A-6.2.19: PT, SRQ and TECO results, mean diploma grade (VMD) and mean grade in university exams sat so far (VME), by number of courses followed abroad
none
one
two(+)
three(+)(*)
four(+)(*)
five(+)(*)six(+)(*)
more than six(+)(*)
ITA12
-80
-60
-40
-20
0
20
40
60
80
100
-60 -40 -20 0 20 40 60 80
PT
SRQ
none
one
two(+)
three(+)(*)
four(+)(*)
five(+)(*)
six(+)(*)
more than six(+)(*)
ITA12
-0,2
0,0
0,2
0,4
0,6
0,8
1,0
-8 -6 -4 -2 0 2 4 6 8
VMD
VME
900
950
1000
1050
1100 TECO
900
950
1000
1050
1100
1150
1200 PT
900
950
1000
1050
1100SRQ
TABLE A-6.2.20: PT, SRQ and TECO results, mean diploma grade (VMD) and mean grade in university exams sat so far (VME), by number of months in an Erasmus
program abroadNumber of
months in an Erasmus program
T PT SRQ TECOTECO
medianVMD VME
none 5556 998.04 998.19 998.19 1016 82.85 26.57
one(+)(*) 223 1037.06 1036.45 1036.79 1052 83.65 27.2
two(+)(*) 18 1010.61 1055.56 1033.17 1052 82.38 26.75
three(+)(*) 7 1162.57 977.57 1070.14 1122 80.57 27.01
four(+)(*) 4 1005.00 1030.5 1017.75 1035 80.75 26.81
five 11 926.27 1028.64 977.45 1014 80.70 26.24
six 14 983.14 932.43 957.86 966 79.79 26.74
More than 6 20 957.50 924.9 941.35 966 74.94 26.13
ITA12 5853 999.46 999.48 999.53 1016 82.84 26.59
Source: See TABLE 3. See TABLE 6.2.19 for aggregated results
74
76
78
80
82
84 VMD
26,00
26,50
27,00
27,50VME
195
none
one(+)(*)
two(+)(*)
three(+)(*)
four(+)(*)five
sei
more than six
ITA12
-100
-80
-60
-40
-20
0
20
40
60
80
-100 -50 0 50 100 150 200
PT
SRQ
none
one(+)(*)
two(+)(*)
three(+)(*)
four(+)(*)
five
six
more than six
ITA12
-0,6
-0,4
-0,2
0,0
0,2
0,4
0,6
0,8
-10 -8 -6 -4 -2 0 2
VMD
VME
Source: See TABLE A-6.2.20
TABLE A-6.2.21: PT, SRQ and TECO results, mean diploma grade (VMD) and mean grade in university exams sat so far (VME), by number of months in an Erasmus program abroad
TABLE 6.3.1: PT, SRQ and TECO results and mean VQR 2004-2010 grade (measured by R12) of teachers actively involved in
teaching courses, per Disciplinary Group
agr.al
arch(*)
art
bio (*)
chim
comun cult
econ
farm
filo (+)(*)
form
geo
giu (+)(*)ing (*)
lett (*)
ling
mat.fis.stat (+)(*)
med (+)(*)
odon (+)(*)polit (+)(*)
psic (+)(*)
soc
sto (+)(*)
terr
vet (+)(*)ITA12
y = 219,57x - 2,345
-120
-100
-80
-60
-40
-20
0
20
40
60
80
100
-0,20 -0,15 -0,10 -0,05 0,00 0,05 0,10 0,15 0,20
TECO
R12
Correlation = 0,58
agr.al
arch(*)
art
bio (*)
chim
comun cult
econ farm
filo (+)(*)
form
geo
giu (+)(*)
ing (*)
lett (*)
ling
mat.fis.stat (+)(*)
med (+)(*)
odon (+)(*)
polit (+)(*) psic (+)(*)
soc
sto (+)(*)
terr
vet (+)(*)
ITA12
y = 169,52x + 0,433
-80
-60
-40
-20
0
20
40
60
80
-0,20 -0,15 -0,10 -0,05 0,00 0,05 0,10 0,15 0,20
PT
R12
Correlation = 0,53
agr.al
arch(*)
art
bio (*)chim
comuncult
econ
farm
filo (+)(*)
formgeo
giu (+)(*)
ing (*)
lett (*)
ling
mat.fis.stat (+)(*)
med (+)(*)
odon (+)(*)
polit (+)(*)
psic (+)(*)
soc
sto (+)(*)
terr
vet (+)(*)
ITA12
y = 269,9x - 5,1323
-150
-130
-110
-90
-70
-50
-30
-10
10
30
50
70
90
-0,20 -0,15 -0,10 -0,05 0,00 0,05 0,10 0,15 0,20
SRQ
R12
Correlation = 0,51
Each teacher (teaching a course) is assigned his/her mean VQR 2004-2010 grade consisting of: (total points for the teacher’s products)/(expected number of products). The expected number of products is 1, 2 or 3 as per the cases defined in the VQR Notice.
Each teacher (associated to a course) is assigned a full-time equivalent (ETP) according to the formula ETP = min(1, HOURS/60).
Each teacher is associated to a CUN Group within VQR 2004-2010 and hence to the ETP-weighted average grade for each CUN Group present in a course/group/university.
R12 represents the ETP-weighted average of the ratios between the mean VQR 2004-2010 grade obtained by each teacher involved in teaching each course within the Disciplinary Group and the mean grade obtained by all teachers in the 12 participating universities in the CUN Group to which the teacher belongs.
Source: See TABLE A-6.3.1
196
SAN (+)(*)
SC
SOC (+)(*)
H
ITA12
-25
-20
-15
-10
-5
0
5
10
15
20
25
-0,06 -0,04 -0,02 0,00 0,02 0,04 0,06 0,08 0,10
TECO
R12
SAN (+)(*)
SC
SOC (+)(*)
HITA12
-20
-15
-10
-5
0
5
10
15
20
-0,06 -0,04 -0,02 0,00 0,02 0,04 0,06 0,08 0,10
PT
R12
SAN (+)(*)
SC
SOC (+)(*)
H
ITA12
-50
-40
-30
-20
-10
0
10
20
30
40
-0,06 -0,04 -0,02 0,00 0,02 0,04 0,06 0,08 0,10
SRQ
R12
TABLE 6.3.2: PT, SRQ and TECO results and mean VQR 2004-2010 grade (measured by R12) of teachers actively involved in teaching courses, per Macro-group
Each teacher (teaching a course) is assigned his/her mean VQR 2004-2010 grade consisting of: (total points for the teacher’s products)/(expected number of products). The expected number of products is 1, 2 or 3 as per the cases defined in the VQR Notice.
Each teacher (associated to a course) is assigned a full-time equivalent (ETP) according to the formula ETP = min(1, HOURS/60).
Each teacher is associated to a CUN Group within VQR 2004-2010 and hence to the ETP-weighted average grade for each CUN Group present in a course/group/university.
R12 represents the ETP-weighted average of the ratios between the mean VQR 2004-2010 grade obtained by each teacher involved in teaching each course within the Macro-group and the mean grade obtained by all teachers in the 12 participating universities in the CUN Group to which the teacher belongs.
Source: See TABLE A-6.3.1
PO
MI (+)(*)PD (+)(*)
UD (+)(*)
BO (+)(*)
FI (+)(*)
RM1
RM2
NA
LE
ME
CA
ITA12
y = 240,19x - 4,8724
-80
-60
-40
-20
0
20
40
60
-0,30 -0,20 -0,10 0,00 0,10 0,20 0,30
TECO
R12
Correlation = 0,76
PO
MI (+)(*)
PD (+)(*)UD (+)(*)
BO (+)(*)
FI (+)(*)
RM1
RM2
NA
LE
ME
CA
ITA12
y = 246,06x - 3,5276
-80
-60
-40
-20
0
20
40
60
-0,30 -0,20 -0,10 0,00 0,10 0,20 0,30
PT
R12
Correlation = 0,82
PO
MI (+)(*)PD (+)(*)
UD (+)(*)
BO (+)(*)
FI (+)(*)
RM1RM2
NA
LE
ME
CA
ITA12
y = 234,42x - 6,2213
-100
-80
-60
-40
-20
0
20
40
60
80
-0,30 -0,20 -0,10 0,00 0,10 0,20 0,30
SRQ
R12
Correlation = 0,69
TABLE 6.3.3: PT, SRQ and TECO results and mean VQR 2004-2010 grade (measured by R12) of teachers actively involved in teaching courses, per University
Each teacher (teaching a course) is assigned his/her mean VQR 2004-2010 grade consisting of: (total points for the teacher’s products)/(expected number of products). The expected number of products is 1, 2 or 3 as per the cases defined in the VQR Notice.Each teacher (associated to a course) is assigned a full-time equivalent (ETP) according to the formula ETP = min(1, HOURS/60).Each teacher is associated to a CUN Group within VQR 2004-2010 and hence to the ETP-weighted average grade for each CUN Group present in a course/group/university.R12 represents the ETP-weighted average of the ratios between the mean VQR 2004-2010 grade obtained by each teacher involved in teaching each course within the University and the mean grade obtained by all teachers in the 12 participating universities in the CUN Group to which the teacher belongs.Source: See TABLE A-6.3.1
197
TABLE A-6.2.12: Average diploma grade (VMD) and average grade in university exams sat so far (VME), by type of high school
Type of high school T VMD VME
CLASSICAL LYCEUM (+)(*) 1433 83.58 27.11
SCIENTIFIC LYCEUM (+)(*) 2462 82.43 26.61
OTHER LYCEUM 451 82.64 26.50
TECHNICAL INSTITUTE 780 83.63 26.02
VOCATIONAL INSTITUTE 99 83.41 25.51
OTHER INSTITUTE (*) 325 85.16 26.63
ITA12 5853 82.84 26.59
Information on the type of high school is missing in 303 cases
Source: See TABLE 3
81
82
83
84
85
86VMD
25
25
26
26
27
27
28VME
TABLE A-6.2.13: PT, SRQ and TECO results, average diploma grade (VMD) and average grade in university exams sat so far (VME) per Disciplinary Group, Macro-group, University and Geographic Area
Disciplinary Group TECO PT SRQ VMD VME Macro-group TECO PT SRQ VMD VME
agr.al 984.01 981.22 986.72 82.35 26.51 SAN 1021.4 1014.33 1028.34 87.37 27.04
arch 1005.94 989.99 1021.73 80.15 27.03 SC 995.74 983.99 1007.37 82.77 26.26
art 965.03 975.3 954.58 76.61 27.61 SOC 1001.47 1007.55 995.25 81.83 26.21
bio 1006.43 997.59 1015.21 81.8 26.19 H 981.88 1001.06 962.56 80.7 27.67
chim 995.45 967.51 1023.29 84.51 26.16 ITA12 999.53 999.46 999.48 82.84 26.59
comun 977.81 989.09 966.36 75.16 25.94
cult 977.99 986.12 969.73 78.4 27.73 University TECO PT SRQ VMD VME
econ 991.15 982.34 999.8 82.59 25.49 PO 994.92 1017.21 972.55 82.84 26.08
farm 975.45 979.59 971.19 84.83 26.39 MI 1034.53 1036.55 1032.39 82.46 26.67
filo 1018.2 1032.05 1004.23 84 28.72 PD 1025.49 1024.14 1026.71 86.5 26.61
form 903.28 932.83 873.38 78.26 27.21 UD 1024.28 1020.45 1028.01 83.61 26.08
geo 933.96 985.51 882.23 78.39 26.35 BO 1047.73 1039.69 1055.61 82.86 27.29
giu 1009.73 1021.76 997.59 83.42 26.51 FI 1024.71 1016.02 1033.26 85.57 26.67
ing 1001.2 980.1 1022.16 85.25 25.48 RM1 976.04 974.09 977.86 75.68 26.34
lett 1012.51 1039.17 985.73 84.68 28.03 RM2 981.22 984.34 978 85.05 26.73
ling 985.38 1000.38 970.34 80.7 27.03 NA 965.43 971.17 959.57 89.36 26.85
mat.fis.stat 1041.43 1027.57 1055.19 83.63 26.43 LE 939.92 940.89 938.68 90.08 26.96
med 1072.25 1057.48 1086.91 90.41 27.63 ME 925.78 927.4 924.01 87.82 26.95
odon 1015.3 1006.57 1023.75 88.11 27.66 CA 986.1 990.66 981.41 86.99 27.11
polit 1006.18 1015.05 997.13 78.4 26.23 ITA12 999.53 999.46 999.48 82.84 26.59
psic 1029.75 1020.74 1038.62 82.36 26.63
soc 958.04 986.28 929.61 79.34 26.44 Geographic Area TECO PT SRQ VMD VME
sto 1011.02 1036.86 985 78.08 28.43 NORTH 1024.01 1027.53 1020.37 83.82 26.47
terr 935.7 938.69 932.54 81.07 26.46 CENTRE 997.07 993.06 1000.94 79.72 26.57
vet 1004.11 982.9 1025.14 85.77 27.03 SOUTH 958.9 963.2 954.45 88.97 26.91
ITA12 999.53 999.46 999.48 82.84 26.59 CENTRE-NORTH 1027.07 1026.38 1027.64 84.15 26.62
CENTRE-SOUTH 970.34 970.91 969.62 81.4 26.57
Source: See TABLE 3 ITA12 999.53 999.46 999.48 82.84 26.59
198
950
1000
1050
1100
1150
1200TECO
950
1000
1050
1100
1150
1200PT
950
1000
1050
1100
1150
1200SRQ
TABLE A-6.2.14: PT, SRQ and TECO results, average diploma grade (VMD) and average grade in university exams sat so far (VME), by number of other
languages known
Other languages known
T PT SRQ TECOTECO
medianVMD VME
None 1641 974.33 970.84 972.67 983 82 26.45
One language(+)(*)
3091 999.94 1006.73 1003.4 1017 83.1 26.61
Two languages(+)(*)
869 1036.32 1024.3 1030.36 1051 83.54 26.74
Three languages(+)(*)
232 1021.98 1011.8 1016.97 1018 82.89 26.73
Four languages(+)(*)
20 1123.75 1005.7 1064.85 1053 81.9 26.91
ITA12 5853 999.46 999.48 999.53 1016 82.84 26.59
Source: See TABLE 3. See TABLE 6.2.16 for aggregated results
81,50
82,00
82,50
83,00
83,50
84,00VMD
26,3
26,4
26,5
26,6
26,7
26,8
26,9
27,0VME
None
One(+)(*)
Two(+)(*)
Three(+)(*)
Four(+)(*)
ITA12
-40
-30
-20
-10
0
10
20
30
-50 0 50 100 150
PT
SRQ
None
One(+)(*)
Two(+)(*)
Three(+)(*)
Four(+)(*)
ITA12
-0,20
-0,15
-0,10
-0,05
0,00
0,05
0,10
0,15
0,20
0,25
0,30
0,35
-2 -1 -1 0 1 1
VMD
VME
TABLE A-6.2.15: PT, SRQ results, average diploma grade (VMD) and average grade in university exams sat so far (VME), by number of other languages known
Source: See TABLE A-6.2.14
199
800
850
900
950
1000
1050
1100TECO
800
850
900
950
1000
1050
1100PT
800
850
900
950
1000
1050
1100SRQ
TABLE A-6.2.16: PT, SRQ and TECO results, average diploma grade (VMD) and average grade in university exams sat so far (VME), by number of courses taught in
foreign language followed in Italy
Number of courses taught in foreign
language followed in Italy
T PT SRQ TECOTECO
medianVMD VME
None(+)(*) 4374 1003.46 1007.88 1005.74 1017 83.20 26.65
one 1003 985.86 970.6 978.31 984 82.15 26.38
two 253 994.47 973.69 984.17 984 80.39 26.17
three 73 984.30 987.25 985.81 983 78.77 26.69
four 35 991.89 933.54 962.77 950 82.53 26.49
five 28 983.14 1005.21 994.25 1001 83.42 27.52
six(+)(*) 27 990.48 1008.78 999.74 1020 80.04 27.02
More than six(+)(*) 60 990.33 1024.48 1007.42 1035 84.80 26.98
ITA12 5853 999.46 999.48 999.53 1016 82.84 26.59
Source: See TABLE 3. See TABLE 6.2.17 for aggregated results
75
76
77
78
79
80
81
82
83
84
85
86VMD
25,00
25,50
26,00
26,50
27,00
27,50
28,00VME
800850900950
10001050110011501200
TECO
800850900950
10001050110011501200
PT
800850900950
10001050110011501200 SRQ
TABLE A-6.2.18: PT, SRQ and TECO results, average diploma grade (VMD) and average grade in university exams sat so far (VME), by
number of courses followed abroadNumber of
courses followed abroad
T PT SRQ TECOTECO
medianVMD VME
none 5593 999.06 999.44 999.32 1016 82.90 26.58
one 91 974.63 937.49 956.12 949 81.61 26.77
two(+) 42 960.57 1011 985.86 1052 77.11 26.71
three(+)(*) 40 1061.05 1088.1 1074.63 1086 84.68 27.28
four(+)(*) 29 1051.34 1047.83 1049.59 1085 78.89 26.96
five(+)(*) 14 1022.00 1002.71 1012.43 1019 88.21 27.14
six(+)(*) 16 1009.19 1003.94 1006.63 1017 75.63 26.69
More than six(+)(*)
28 1058.36 1010.11 1034.32 1052 86.48 27.46
ITA12 5853 999.46 999.48 999.53 1016 82.84 26.59
Source: See TABLE 3. See TABLE 6.2.18 for aggregated results
687072747678808284868890
VMD
26,0
26,2
26,4
26,6
26,8
27,0
27,2
27,4
27,6VME
200
Source: See TABLE A-6.2.18
TABLE A-6.2.19: PT, SRQ and TECO results, average diploma grade (VMD) and average grade in university exams sat so far (VME), by number of courses followed
abroad
none
one
two(+)
three(+)(*)
four(+)(*)
five(+)(*)six(+)(*)
more than six(+)(*)
ITA12
-80
-60
-40
-20
0
20
40
60
80
100
-60 -40 -20 0 20 40 60 80
PT
SRQ
none
one
two(+)
three(+)(*)
four(+)(*)
five(+)(*)
six(+)(*)
more than six(+)(*)
ITA12
-0,2
0,0
0,2
0,4
0,6
0,8
1,0
-8 -6 -4 -2 0 2 4 6 8
VMD
VME
900
950
1000
1050
1100 TECO
900
950
1000
1050
1100
1150
1200 PT
900
950
1000
1050
1100SRQ
TABLE A-6.2.20: PT, SRQ and TECO results, average diploma grade (VMD) and average grade in university exams sat so far (VME), by number of months in an
Erasmus program abroadNumber of
months in an Erasmus program
T PT SRQ TECOTECO
medianVMD VME
none 5556 998.04 998.19 998.19 1016 82.85 26.57
one(+)(*) 223 1037.06 1036.45 1036.79 1052 83.65 27.2
two(+)(*) 18 1010.61 1055.56 1033.17 1052 82.38 26.75
three(+)(*) 7 1162.57 977.57 1070.14 1122 80.57 27.01
four(+)(*) 4 1005.00 1030.5 1017.75 1035 80.75 26.81
five 11 926.27 1028.64 977.45 1014 80.70 26.24
six 14 983.14 932.43 957.86 966 79.79 26.74
More than 6 20 957.50 924.9 941.35 966 74.94 26.13
ITA12 5853 999.46 999.48 999.53 1016 82.84 26.59
Source: See TABLE 3. See TABLE 6.2.19 for aggregated results
74
76
78
80
82
84 VMD
26,00
26,50
27,00
27,50VME
201
none
one(+)(*)
two(+)(*)
three(+)(*)
four(+)(*)five
six
more than six
ITA12
-100
-80
-60
-40
-20
0
20
40
60
80
-100 -50 0 50 100 150 200
PT
SRQ
none
one(+)(*)
two(+)(*)
Three(+)(*)
four(+)(*)
five
sei
more than six
ITA12
-0,6
-0,4
-0,2
0,0
0,2
0,4
0,6
0,8
-10 -8 -6 -4 -2 0 2
VMD
VME
Source: See TABLE A-6.2.20
TABLE A-6.2.21: PT, SRQ and TECO results, average diploma grade (VMD) and average grade in university exams sat so far (VME), by number of months in an Erasmus program abroad
SAN (+)(*)
SC
SOC (+)(*)
H
ITA12
-25
-20
-15
-10
-5
0
5
10
15
20
25
-0,06 -0,04 -0,02 0,00 0,02 0,04 0,06 0,08 0,10
TECO
R12
SAN (+)(*)
SC
SOC (+)(*)
HITA12
-20
-15
-10
-5
0
5
10
15
20
-0,06 -0,04 -0,02 0,00 0,02 0,04 0,06 0,08 0,10
PT
R12
SAN (+)(*)
SC
SOC (+)(*)
H
ITA12
-50
-40
-30
-20
-10
0
10
20
30
40
-0,06 -0,04 -0,02 0,00 0,02 0,04 0,06 0,08 0,10
SRQ
R12
TABLE 6.3.2: PT, SRQ and TECO results and average VQR 2004-2010 grade (measured by R12) of staff actively involved in teaching courses, per Macro-group
Each teacher (teaching a course) is assigned his/her mean VQR 2004-2010 grade consisting of: (total points for the teacher’s products)/(expected number of products). The expected number of products is 1, 2 or 3 as per the cases defined in the VQR Notice.
Each teacher (associated to a course) is assigned a full-time equivalent (ETP) according to the formula ETP = min(1, HOURS/60).
Each teacher (teaching a course) is associated to a CUN Group within VQR 2004-2010 and hence the ETP-weighted average grade for each CUN Group present in a course/group/university.
R12 represents the ETP-weighted average of the ratios between the mean VQR 2004-2010 grade obtained by each teacher involved in teaching each course within the Macro-group and the mean grade obtained by all teachers in the 12 participating universities in the CUN Group to which the teacher belongs.
Source: See TABLE A-6.3.1
202
TABLE 6.3.4: TECO, PT and SRQ results and mean VQR 2004-2010 grade (measured by R12) of teachers actively involved in teaching courses, per Geographic Area
NORTH (+)(*)
CENTRE
SOUTH
CENTRE-NORTH (+)(*)
CENTRE-SOUTH
ITA12
-50
-40
-30
-20
-10
0
10
20
30
40
-0,15 -0,10 -0,05 0,00 0,05 0,10 0,15
TECO
R12
NORTH (+)(*)
CENTRE
SOUTH
CENTRE-NORTH (+)(*)
CENTRE-SOUTH
ITA12
-40
-30
-20
-10
0
10
20
30
40
-0,15 -0,10 -0,05 0,00 0,05 0,10 0,15
PT
R12
NORTH (+)(*)
CENTRE
SOUTH
CENTRE-NORTH (+)(*)
CENTRE-SOUTH
ITA12
-50
-40
-30
-20
-10
0
10
20
30
40
-0,15 -0,10 -0,05 0,00 0,05 0,10 0,15
SRQ
R12
Each teacher (teaching a course) is assigned his/her mean VQR 2004-2010 grade consisting of: (total points for the teacher’s products)/(expected number of products). The expected number of products is 1, 2 or 3 as per the cases defined in the VQR Notice.
Each teacher (associated to a course) is assigned a full-time equivalent (ETP) according to the formula ETP = min(1, HOURS/60).
Each teacher (teaching a course) is associated to a CUN Group within VQR 2004-2010 and hence the ETP-weighted average grade for each CUN Group present in a course/group/university.
R12 represents the ETP-weighted average of the ratios between the mean VQR 2004-2010 grade obtained by each teacher involved in teaching each course within the Universities in the Geographic Area and the mean grade obtained by all teachers in the 12 participating universities in the CUN Group to which the teacher belongs.
Source: See TABLE A-6.3.1
TABLE 6.3.6: TECO, PT, SRQ results and Merit Index (M) calculated for all eligible and ineligible students taken together, per Disciplinary Group
agr.al
arch (*)
art
bio (*)chim
comuncult
econ
farm
filo (+)(*)
form
geo
giu (+)(*)
ing (*)
lett (*)
ling
mat.fis.stat (+)(*)
med (+)(*)
odon (+)(*)
polit (+)(*)
psic (+)(*)
soc
sto (+)(*)
terr
vet (+)(*)
ITA12
Correlation = 0,35y = 2,3141x - 1,7088
-120
-100
-80
-60
-40
-20
0
20
40
60
80
100
-15 -10 -5 0 5 10 15
TECO
M
agr.al
arch (*)
art
bio (*)
chim
comuncult
econfarm
filo (+)(*)
form
geo
giu (+)(*)
ing (*)
lett (*)
ling
mat.fis.stat (+)(*)
med (+)(*)
odon (+)(*)polit (+)(*)
psic (+)(*)
soc
sto (+)(*)
terr
vet (+)(*)
ITA12
Correlation = -0,02y = -0,0872x - 3,0186
-80
-60
-40
-20
0
20
40
60
80
-15 -10 -5 0 5 10 15
PT
M
agr.al
arch (*)
art
bio (*)
chim
comun
cult
econ
farm
filo (+)(*)
formgeo
giu (+)(*)
ing (*)
lett (*)
ling
mat.fis.stat (+)(*)
med (+)(*)
odon (+)(*)
polit (+)(*)
psic (+)(*)
soc
sto (+)(*)
terr
vet (+)(*)
ITA12
Correlation = 0,53y = 4,7193x - 0,4059
-150
-100
-50
0
50
100
-15 -10 -5 0 5 10 15
SRQ
M
Source: See TABLE 4.1 for TECO results, see TABLE 6.3.5 for M values
203
TABLE 6.3.5: Student environment quality, as shown by the mean diploma grade (VMD) and mean grade in university exams sat so far (VME) for all eligible and ineligible students, per Disciplinary
GroupDisciplinary Group VMD VME DIPL UN M
agr.al 75.95 24.58 0.956 0.975 -1.95arch (*) 80.54 26.34 1.014 1.045 -3.19
art 75.37 26.48 0.949 1.051 -10.24bio (*) 77.66 24.70 0.977 0.980 -0.31chim 80.30 24.61 1.011 0.977 3.39
comun 75.23 25.28 0.947 1.003 -5.64cult 76.91 26.77 0.968 1.062 -9.44econ 78.46 23.74 0.987 0.942 4.54farm 79.14 24.48 0.996 0.971 2.45
filo (+)(*) 80.12 28.00 1.008 1.111 -10.27form 75.76 26.18 0.953 1.039 -8.57geo 77.15 25.40 0.971 1.008 -3.70
giu (+)(*) 79.66 25.03 1.003 0.993 0.93ing (*) 82.87 23.92 1.043 0.949 9.35lett (*) 81.08 27.10 1.020 1.075 -5.49
ling 79.03 26.11 0.995 1.036 -4.17mat.fis.stat (+)(*) 81.03 24.58 1.020 0.976 4.41
med (+)(*) 86.95 26.42 1.094 1.049 4.56odon (+)(*) 84.26 26.80 1.060 1.064 -0.32polit (+)(*) 76.20 24.96 0.959 0.991 -3.16psic (+)(*) 78.96 25.37 0.994 1.007 -1.32
soc 76.60 25.73 0.964 1.021 -5.71sto (+)(*) 77.15 27.50 0.971 1.091 -12.04
terr 78.60 24.98 0.989 0.991 -0.19vet (+)(*) 81.79 25.59 1.029 1.015 1.39
ITA12 79.46 25.20 1.000 1.000 0.00DIPL: Ratio between mean diploma grade of students in that group of classes and the ITA12 mean diploma grade (= 82.10)UN: Ratio between mean grade in University exams sat so far of students in that group of classes and the ITA12 mean grade in University exams sat so far (= 26.46)M: Merit Index (DIPL – UN)*100Source: See TABLE 3.
agr.al
arch (*)art
bio (*)chim
comun
cult
econ
farm
filo (+)(*)
form
geo
giu (+)(*)
ing (*)
lett (*)
ling
mat.fis.stat (+)(*)
med (+)(*)
odon (+)(*)
polit (+)(*)
psic (+)(*)
soc
sto (+)(*)
terr
vet (+)(*)
ITA12
0,92
0,94
0,96
0,98
1,00
1,02
1,04
1,06
1,08
1,10
1,12
0,92 0,94 0,96 0,98 1,00 1,02 1,04 1,06 1,08 1,10 1,12
DIPL
UN
TABLE 6.3.7: Student environment quality, as shown by the average diploma grade (VMD) and
average grade in university exams sat so far (VME) for all eligible and ineligible students, per
Macro-group
Macro-group VMD VME DIPL UN M
SAN (+)(*) 83.45 25.64 1.050 1.018 3.26
SC 79.94 24.65 1.006 0.978 2.76
SOC (+)(*) 78.32 24.77 0.986 0.983 0.26
H 78.24 26.65 0.985 1.057 -7.28
ITA12 79.46 25.20 1.000 1.000 0.00
In this table, “students” refers to all students enrolled in the 3rd or 4th year of a three-year first-cycle course or single-cycle master course, excluding health care professions, in the 12 universities participating in the pilot test
DIPL: Ratio between mean diploma grade of students in that group of classes and the ITA12 mean diploma grade (= 82.10)
UN: Ratio between mean grade in University exams sat so far of students in that group of classes and the ITA12 mean grade in University exams sat so far (= 26.46)
M: Merit Index (DIPL – UN)*100
Source: See TABLE 3
SAN (+)(*)
SC
SOC (+)(*)
H
ITA12
0,97
0,98
0,99
1,00
1,01
1,02
1,03
1,04
1,05
1,06
1,07
0,98 1,00 1,02 1,04 1,06
DIPL
UN
204
SAN (+)(*)
SC
SOC (+)(*)
HITA12
-20
-15
-10
-5
0
5
10
15
20
-8 -6 -4 -2 0 2 4
PT
M
SAN (+)(*)
SC
SOC (+)(*)
H
ITA12
-50
-40
-30
-20
-10
0
10
20
30
40
-8 -6 -4 -2 0 2 4
SRQ
M
SAN (+)(*)
SC
SOC (+)(*)
H
ITA12
-20
-15
-10
-5
0
5
10
15
20
25
-8 -6 -4 -2 0 2 4
TECO
M
TABLE 6.3.8: TECO, PT, SRQ results and Merit Index (M) calculated for all eligible and ineligible student, per Macro-group
Source: See TABLE 4.2 for TECO results, see TABLE 6.3.7 for M values
TABLE 6.3.9: Student environment quality, as shown by the average diploma grade (VMD) and average grade in university exams sat so far (VME) for all eligible and ineligible students,
per University
University VMD VME DIPL UN M
PO 77.66 24.82 0.977 0.985 -0.77
MI (+)(*) 77.01 25.25 0.969 1.002 -3.29
PD (+)(*) 81.14 24.93 1.021 0.989 3.17
UD (+)(*) 78.09 24.64 0.983 0.978 0.47
BO (+)(*) 79.62 25.58 1.002 1.015 -1.32
FI (+)(*) 78.91 25.55 0.993 1.014 -2.08
RM1 76.04 25.20 0.957 1.000 -4.29
RM2 80.50 25.02 1.013 0.993 2.02
NA 82.33 24.93 1.036 0.989 4.67
LE 80.97 25.23 1.019 1.001 1.79
ME 82.79 25.39 1.042 1.008 3.42
CA 78.95 25.25 0.994 1.002 -0.86
ITA12 79.46 25.20 1.000 1.000 0.00
In this table, “students” refers to all students enrolled in the 3rd or 4th year of a three-year first-cycle course or single-cycle master course, excluding health care professions, in the 12 universities participating in the pilot test
DIPL: Ratio between mean diploma grade of students in that group of classes and the ITA12 mean diploma grade (= 82.10)
UN: Ratio between mean grade in University exams sat so far of students in that group of classes and the ITA12 mean grade in University exams sat so far (= 26.46)
M: Merit Index (DIPL – UN)*100
Source: See TABLE 3
PO
MI (+)(*)
PD (+)(*)
UD (+)(*)
BO (+)(*)
FI (+)(*)
RM1
RM2
NA
LE
ME
CA
ITA12
0,98
0,98
0,99
0,99
1,00
1,00
1,01
1,01
1,02
1,02
0,94 0,96 0,98 1,00 1,02 1,04 1,06
DIPL
UN
205
TABLE 6.3.11: Student environment quality, as shown by the average diploma grade (VMD) and average grade in university exams sat so far (VME) for all eligible and
ineligible students, per Geographic Area
Geographic Area VMD VME DIPL UN M
NORTH (+)(*) 78.79 25.03 0.992 0.993 -0.17
CENTRE 78.38 25.35 0.986 1.006 -1.98
SOUTH 81.65 25.10 1.028 0.996 3.15
CENTRE-NORTH (+)(*)
79.05 25.28 0.995 1.003 -0.86
CENTRE-SOUTH 79.85 25.12 1.005 0.997 0.80
ITA12 79.46 25.20 1.000 1.000 0.00
In this table, “students” refers to all students enrolled in the 3rd or 4th year of a three-year first-cycle course or single-cycle master course, excluding health care professions, in the 12 universities participating in the pilot test
DIPL: Ratio between mean diploma grade of students in that group of classes and the ITA12 mean diploma grade (= 82.10)
UN: Ratio between mean grade in University exams sat so far of students in that group of classes and the ITA12 mean grade in University exams sat so far (= 26.46)
M: Merit Index (DIPL – UN)*100
Source: See TABLE 3
NORTH (+)(*)
CENTRE
SOUTH
CENTRE-NORTH (+)(*)
CENTRE-SOUTH
ITA12
0,992
0,994
0,996
0,998
1,000
1,002
1,004
1,006
1,008
0,98 0,99 1,00 1,01 1,02 1,03
DIPL
UN
TABLE 6.3.12: TECO, PT, SRQ results and Merit Index (M) calculated for all eligible
and ineligible students, per Geographic Area
NORTH (+)(*)
CENTRE
SOUTH
CENTRE-NORTH (+)(*)
CENTRE-SOUTH
ITA12
-50
-40
-30
-20
-10
0
10
20
30
40
-3 -2 -1 0 1 2 3 4
TECO
M
NORTH (+)(*)
CENTRE
SOUTH
CENTRE-NORTH (+)(*)
CENTRE-SOUTH
ITA12
-40
-30
-20
-10
0
10
20
30
40
-3 -2 -1 0 1 2 3 4
PT
M
NORTH (+)(*)
CENTRE
SOUTH
CENTRE-NORTH (+)(*)
CENTRE-SOUTH
ITA12
-50
-40
-30
-20
-10
0
10
20
30
40
-3 -2 -1 0 1 2 3 4
SRQ
M
Source: See TABLE 4.4 for TECO results, see TABLE 6.3.11 for M values
206
Disciplinary Group
Macro-group
University
TABLE 6.3.13: Tested students’ mean diploma grade (VMD) and mean grade in university exams sat so far (VME), per Disciplinary Group, Macro-group, University and Geographic Area
Geographic Area
agr.al
arch (*)
art
bio (*)
chim
comun
cultecon
farm
filo (+)(*)
form
geo
giu (+)(*)
ing (*)
lett (*)
ling
mat.fis.stat (+)(*)
med (+)(*)
odon (+)(*)
polit (+)(*)
psic (+)(*)
soc sto (+)(*)
terr
vet (+)(*)
ITA12
-6
-4
-2
0
2
4
6
8
10
-2 -1 0 1 2 3 4
VME
VMD
SAN (+)(*)
SC
SOC (+)(*) H
ITA12
-2
-1
0
1
2
3
4
5
-1 -1 0 1 1 2 2
VME
VMD
POMI (+)(*)
PD (+)(*)
UD (+)(*)
BO (+)(*)
FI (+)(*)
RM1
RM2
NA
LE
ME
CA
ITA12
-4
-3
-2
-1
0
1
2
3
4
-1 -1 0 0 0 0 0 1
VME
VMD
NORD (+)(*)
CENTRO
SUD
CENTRO-NORD (+)(*)
CENTRO-SUD ITA12
-2
-1
-1
0
1
1
2
2
3
0 0 0 0 0
VME
VMD
Source: See TABLE 6.3.5
Source: See TABLE 6.3.7
Source: See TABLE 6.3.9
Source: See TABLE 6.3.11 TABLE A-6.3.1: Mean VQR 2004-2010 grade (VM) and mean teaching grade (R12) obtained by active teachers, per
Disciplinary Group, Macro-group, University and Geographic AreaDisciplinary Group VM R12 Macro-group VM R12
agr.al 0.66 0.97 SAN 0.65 1.08arch 0.5 0.85 SC 0.69 1art 0.64 0.97 SOC 0.49 0.95bio 0.7 1.04 H 0.67 1.02
chim 0.81 1 ITA12 0.64 1comun 0.49 0.83
cult 0.67 0.98 University VM R12econ 0.34 0.82 PO 0.67 1.13farm 0.75 1.03 MI 0.69 1.11filo 0.68 1.13 PD 0.75 1.21
form 0.56 0.95 UD 0.64 1.01geo 0.48 0.81 BO 0.68 1.07giu 0.58 1.09 FI 0.61 0.99ing 0.74 1.01 RM1 0.61 0.99lett 0.69 1.02 RM2 0.64 0.97ling 0.68 1.01 NA 0.6 0.92
mat.fis.stat 0.73 1.08 LE 0.61 0.93med 0.63 1.16 ME 0.48 0.75odon 0.5 0.92 CA 0.57 0.9polit 0.5 0.98 ITA12 0.64 1psic 0.6 1soc 0.44 0.84 Geographic Area VM R12sto 0.64 1.04 NORTH 0.71 1.14terr 0.61 0.97 CENTRE 0.64 1.01vet 0.66 0.99 SOUTH 0.57 0.88
ITA12 0.64 1 CENTRE-NORTH 0.68 1.09CENTRE-SOUTH 0.59 0.93
ITA12 0.64 1Each teacher (teaching a course) is assigned his/her average VQR 2004-2010 grade consisting of: (total points for the teacher’s products)/(expected number of products). The expected number of products is 1, 2 or 3 depending on cases, as per the VQR Notice.Each teacher (associated to a course) is assigned a full-time equivalent (ETP) according to the formula ETP = min(1, HOURS/60).Each teacher (teaching a course) is associated to a CUN Group within VQR 2004-2010 and hence the ETP-weighted average grade for each CUN Group present in a course/group/university.VM represents the ETP-weighted average of the average VQR 2004-2010 grades obtained by teachers who are active in the courses belonging to the Disciplinary groupR12 represents the ETP-weighted average of the ratios between the mean VQR 2004-2010 grade obtained by each teacher involved in teaching each course within the Disciplinary Group and the mean grade obtained by all teachers in the 12 participating universities in the CUN Group to which the teacher belongs.Source: See TABLE 3. See also TABLES 6.3.1 to 6.3.4 for graphs showing correlations between TECO and R12
207
TABLE A-6.3.2: TECO, PT, SRQ results and mean VQR 2004-2010 grades of active teachers (VM), per Disciplinary Group
agr.al
arch(*)
art
bio (*)
chim
comun cult
econ
farm
filo (+)(*)
form
geo
giu (+)(*)
ing (*)
lett (*)
ling
mat.fis.stat (+)(*)
med (+)(*)
odon (+)(*)
polit (+)(*)
psic (+)(*)
soc
sto (+)(*)
terr
vet (+)(*)ITA12
y = 76,577x - 4,6837
-120
-100
-80
-60
-40
-20
0
20
40
60
80
100
-0,40 -0,30 -0,20 -0,10 0,00 0,10 0,20
TECO
VM
Correlation = 0,24
agr.al
arch(*)
art
bio (*)
chim
comun cultecon
farm
filo (+)(*)
form
geo
giu (+)(*)
ing (*)
lett (*)
ling
mat.fis.stat (+)(*)
med (+)(*)
odon (+)(*)
polit (+)(*) psic (+)(*)
soc
sto (+)(*)
terr
vet (+)(*)
ITA12
y = 21,724x - 2,2978
-80
-60
-40
-20
0
20
40
60
80
-0,30 -0,20 -0,10 0,00 0,10 0,20
PT
VM
Correlation = 0,08
agr.al
arch(*)
art
bio (*)
chim
comun cult
econ
farm
filo (+)(*)
formgeo
giu (+)(*)
ing (*)
lett (*)
ling
mat.fis.stat (+)(*)
med (+)(*)
odon (+)(*)
polit (+)(*)
psic (+)(*)
soc
sto (+)(*)
terr
vet (+)(*)
ITA12
y = 131,72x - 7,0774
-150
-130
-110
-90
-70
-50
-30
-10
10
30
50
70
90
-0,30 -0,20 -0,10 0,00 0,10 0,20
SRQ
VM
Correlation = 0,30
Each teacher (teaching a course) is assigned his/her mean VQR 2004-2010 grade consisting of: (total points for the teacher’s products)/(expected number of products). The expected number of products is 1, 2 or 3 as per the cases defined in the VQR Notice.
Each teacher (associated to a course) is assigned a full-time equivalent (ETP) according to the formula ETP = min(1, HOURS/60).
Each teacher is associated to a CUN Group within VQR 2004-2010 and hence the ETP-weighted average grade for each CUN Group present in a course/group/university.
VM represents the ETP-weighted average of the mean VQR 2004-2010 grades obtained by teachers who are active in the courses belonging to the Disciplinary group.
Source: See TABLE A-6.3.1
agr.al
arch(*)
art
bio (*)
chim
comun
cult
econ
farm
filo (+)(*)
form
geo
giu (+)(*)
ing (*)lett (*)
ling
mat.fis.stat (+)(*)
med (+)(*)
odon (+)(*)
polit (+)(*)
psic (+)(*)
soc
sto (+)(*)
terr
vet (+)(*)
ITA12
y = 0,6036x - 0,4348
-20
-15
-10
-5
0
5
10
15
20
-30 -25 -20 -15 -10 -5 0 5 10 15 20
R12
VM
Correlation = 0,72
TABLE A-6.3.3: Mean VQR 2004-2010 grade (VM) and mean teaching grade (R12) obtained by active teachers, per Disciplinary group
Source: See TABLE A-6.3.1
208
SAN (+)(*)
SC
SOC (+)(*)
H
ITA12
-25
-20
-15
-10
-5
0
5
10
15
20
25
-0,20 -0,15 -0,10 -0,05 0,00 0,05 0,10
TECO
VM
SAN (+)(*)
SC
SOC (+)(*)H
ITA12
-20
-15
-10
-5
0
5
10
15
20
-0,20 -0,15 -0,10 -0,05 0,00 0,05 0,10
PT
VM
SAN (+)(*)
SC
SOC (+)(*)
H
ITA12
-50
-40
-30
-20
-10
0
10
20
30
40
-0,20 -0,15 -0,10 -0,05 0,00 0,05 0,10
SRQ
VM
TABLE A-6.3.4: TECO, PT, SRQ results and mean VQR 2004-2010 grades of active teachers (VM), per Macro-group
Each teacher (teaching a course) is assigned his/her mean VQR 2004-2010 grade consisting of: (total points for the teacher’s products)/(expected number of products). The expected number of products is 1, 2 or 3 as per the cases defined in the VQR Notice.
Each teacher (associated to a course) is assigned a full-time equivalent (ETP) according to the formula ETP = min(1, HOURS/60).
Each teacher is associated to a CUN group within VQR 2004-2010 and hence to the ETP-weighted average grade for each CUN group present in a course/group/university.
VM represents the ETP-weighted average of the mean VQR 2004-2010 grades obtained by teachers who are active in the courses belonging to the Macro-group.
Source: See TABLE A-6.3.1
SAN (+)(*)
SC
SOC (+)(*)
H
ITA12
y = 0,4041x + 1,5373
-6
-4
-2
0
2
4
6
8
10
-20 -15 -10 -5 0 5 10
VM
R12Correlation = 0,70
TABLE A-6.3.5: Mean VQR 2004-2010 grade (VM) and mean teaching grade (R12) obtained by active teachers, per Macro-group
Source: See TABLE A-6.3.1
209
PO
MI (+)(*)
PD (+)(*)UD (+)(*)
BO (+)(*)
FI (+)(*)
RM1RM2
NA
LE
ME
CA
ITA12
y = 420,83x - 2,1507
-80
-60
-40
-20
0
20
40
60
-0,20 -0,15 -0,10 -0,05 0,00 0,05 0,10 0,15
TECO
VM
Correlation = 0,75
PO
MI (+)(*)PD (+)(*)
UD (+)(*)
BO (+)(*)
FI (+)(*)
RM1RM2
NA
LE
ME
CA
ITA12
y = 420,57x - 0,8172
-80
-60
-40
-20
0
20
40
60
-0,20 -0,15 -0,10 -0,05 0,00 0,05 0,10 0,15
PT
VM
Correlation = 0,79
PO
MI (+)(*)PD (+)(*)
UD (+)(*)
BO (+)(*)
FI (+)(*)
RM1 RM2
NA
LE
ME
CA
ITA12
y = 421,21x - 3,4879
-100
-80
-60
-40
-20
0
20
40
60
80
-0,20 -0,15 -0,10 -0,05 0,00 0,05 0,10 0,15
SRQ
VM
Correlation = 0,69
TABLE A-6.3.6: TECO, PT, SRQ results and mean VQR 2004-2010 grades of active teachers (VM), per University
Each teacher (teaching a course) is assigned his/her mean VQR 2004-2010 grade consisting of: (total points for the teacher’s products)/(expected number of products). The expected number of products is 1, 2 or 3 as per the cases defined in the VQR Notice.
Each teacher (associated to a course) is assigned a full-time equivalent (ETP) according to the formula ETP = min(1, HOURS/60).
Each teacher is associated to a CUN group within VQR 2004-2010 and hence to the ETP-weighted average grade for each CUN grouppresent in a course/group/university.
VM represents the ETP-weighted average of the mean VQR 2004-2010 grades obtained by teachers who are active in the courses given in the University.
Source: See TABLE A-6.3.1
PO
MI (+)(*)
PD (+)(*)
UD (+)(*)
BO (+)(*)
FI (+)(*)RM1
RM2
NA
LE
ME
CA
ITA12
Correlation = 0,97y = 1,7311x + 1,1177
-30
-20
-10
0
10
20
-20 -15 -10 -5 0 5 10 15
R12
VM
TABLE A-6.3.7: Mean VQR 2004-2010 grade (VM) and mean teaching grade (R12) obtained by active teachers, per University
Source: See TABLE A-6.3.1
210
TABLE A-6.3.8: TECO, PT, SRQ results and mean VQR 2004-2010 grades of active teachers (VM), per Geographic Area
NORTH (+)(*)
CENTRE
SOUTH
CENTRE-NORTH (+)(*)
CENTRE-SOUTH
ITA12
-50
-40
-30
-20
-10
0
10
20
30
40
-0,08 -0,06 -0,04 -0,02 0,00 0,02 0,04 0,06 0,08
TECO
VM
NORTH(+)(*)
CENTRE
SOUTH
CENTRE-NORTH (+)(*)
CENTRE-SOUTH
ITA12
-40
-30
-20
-10
0
10
20
30
40
-0,08 -0,06 -0,04 -0,02 0,00 0,02 0,04 0,06 0,08
PT
VM
NORTH(+)(*)
CENTRE
SOUTH
CENTRE-NORTH(+)(*)
CENTRE-SOUTH
ITA12
-50
-40
-30
-20
-10
0
10
20
30
40
-0,08 -0,06 -0,04 -0,02 0,00 0,02 0,04 0,06 0,08
SRQ
VM
Each teacher (teaching a course) is assigned his/her mean VQR 2004-2010 grade consisting of: (total points for the teacher’s products)/(expected number of products). The expected number of products is 1, 2 or 3 as per the cases defined in the VQR Notice.
Each teacher (associated to a course) is assigned a full-time equivalent (ETP) according to the formula ETP = min(1, HOURS/60).
Each teacher is associated to a CUN group within VQR 2004-2010 and hence to the ETP-weighted average grade for each CUN group present in a course/group/university.
VM represents the ETP-weighted average of the mean VQR 2004-2010 grades obtained by teachers who are active in the courses given in the Universities of the Geographic Area.
Source: See TABLE A-6.3.1
NORTH (+)(*)
CENTRE
SOUTH
CENTRE-NORTH (+)(*)
CENTRE-SOUTH
ITA12
y = 1,8249x + 0,846
-15
-10
-5
0
5
10
15
-8 -6 -4 -2 0 2 4 6 8
R12
VM
Correlation = 1,00
TABLE A-6.3.9: Mean VQR 2004-2010 grade (VM) and mean teaching grade (R12) obtained by active teachers, per Geographic Area
Source: See TABLE A-6.3.1
211
Variable PT5 PT6 PT7 PT8 PT9 TABLE 7.2: PT results in some linear regression models
Column PT5 refers to the specification with the richest data, taking into consideration all the variables reported in the table. Column PT6 refers to the specification with only basic covariates (age, gender, time since diploma obtained, father’s and mother’s study qualification, father’s and mother’s profession, university in which the student is enrolled). The intercept of the basic specification thus represents the expected result for a “typical” student, i.e. 23-years-old, male, diploma obtained 4 years ago, father and mother with upper secondary education and white-collar jobs, studying at Rome “La Sapienza”.The PT7 column adds, versus the previous column, the variable distance between place of residence and University attended (the reference category is residence and university in the same municipality).The PT8 column adds, versus the previous column, the variables: working student status, foreign language spoken at home, foreign citizenship (the reference categories are non-working student, Italian spoken at home, Italian citizenship).Lastly, column PT9 adds, versus the previous column, the Disciplinary group variable (the reference category is Law). The coefficients associated with the binary indicators provide an estimate of the difference between the expected result and the reference value, keeping all other variables in the model the same.
Source: F. Peracchi (2014)
Age -5.190* -9.244*** -9.507*** -7.175** -7.283**
Female -7563 -1376 -0,979 0,432 1049
Time since diploma obtained 3818 6.418* 6.610* 4987 5048
Diploma grade 1.427***
Mean grade 15.261***
Tecn/Prof -34.270***
Classical 12.277*
Other lyceum -14709
Other Inst. -2508
Working student -6988 -15.204* -13.785*
Foreign language -32.259*** -43.681*** -41.526***
Foreign citizenship -100.035*** -120.236*** -119.951***
Outside Municipality 3,479 0,872 -1,074 1,541
Outside Province -2631 -6049 -8624 -3737
Outside Region -21.980*** -20.901** -22.303*** -20.854**
Mother with no diploma -3,222 -5,852 -5,793 -7,916 -6,163
Mother with degree 0,722 5,785 4,749 6,410 5,759
No mother -30.592* -36.535** -35.088* -32.165* -32.337*
Father without diploma -2,353 -4,294 -4,131 -6,915 -5,365
Father with degree -5,033 -1,205 -1,423 -0,313 -2,283
No father -7,449 -12,675 -12,998 -7,646 -8,584
Mother managerial/professional -2204 2,135 3,536 2,852 0,451
Mother labourer -12,815 -19.680** -19.008** -13,332 -12,615
Mother unemployed -9,380 -7,417 -7,109 -6,637 -5,767
Father managerial/professional 7742 12447 12689 11272 9540
Father labourer 1659 -5,111 -4,694 -0,700 -0,512
Father unemployed -4,506 -10,080 -10,674 -6,847 -5424
agr-al (SC) -45.189** -54.919***
arch (SC) -34.806*** -28.771**
art (H) -49.866* -35,907
cult (H) -42.969*** -26.564*
bio (SC) -24,179 -31.632**
chim (SC) -51.061*** -54.097***
comun (SOC) -9,261 -24,191
econ (SOC) -8,740 -28.384**
farm (SAN) -40.896*** -41.878***
filo (H) -32023 9,367
form (H) -70.699*** -74.675***
geo (SOC) -7,186 -20689
ing (SC) -16568 -28.876**
lett (H) -17176 13,910
ling (H) -13,220 -7,045
mat.fis.stat (SC) 8160 10809
med (SAN) 6844 34.462***
odon (SAN) -21,872 -2,212
polit (SOC) 21667 13719
psic (SOC) 4238 2454
soc (SOC) -1,871 -13,413
sto (H) -16,283 11,327
terr (SC) -68.859*** -69.610***
vet (SAN) -63.517*** -55.850***
BO 40.620*** 62.434*** 66.555*** 69.122*** 59.341***
CA -22,919 27730 23,591 18,081 0,577
FI 22.343** 41.382*** 40.412*** 39.902*** 39.804***
LE -76.245*** -21665 -26473 -29.610* -43.114**
ME -71.720*** -46.733*** -45.822*** -50.983*** -46.046***
MI 44.132*** 59.347*** 59.017*** 58.191*** 53.626***
NA -46.001*** -14355 -18.412* -17.027* -24.287**
PD 36.847*** 53.023*** 53.277*** 51.122*** 52.663***
PO 40.506*** 52.080*** 54.413*** 53.461*** 42.147***
RM2 -22503 9,032 6247 4,920 -2,542
UD 41.008*** 50.770*** 51.606*** 54.339*** 43.551***
Constant 1019.193*** 981.106*** 986.047*** 992.393*** 1011.187***
N 5853 5853 5853 5853 5853
df_m 64 26 29 32 56
r2_a .094 .031 .0319 .0445 .0598
F 11 8.01 7.43 9.75 7.93
rmse 190 197 197 196 194
legend: * p<1; ** p<.05; ***p<.01
Variable SRQ5 SRQ6 SRQ7 SRQ8 SRQ9 TABLE 7.3: SRQ results in some linear regression
models
Column SRQ5 refers to the specification with the richest data, taking into consideration all the variables reported in the table. Column SRQ6 refers to the specification with only basic covariates (age, gender, time since diploma obtained, father’s and mother’s study qualification, father’s and mother’s profession, university in which the student is enrolled). The intercept of the basic specification thus represents the expected result for a “typical” student, i.e. 23-years-old, male, diploma obtained 4 years ago, father and mother with upper secondary education and white-collar jobs, studying at Rome “La Sapienza”.The SRQ7 column adds, versus the previous column, the variable distance between place of residence and University attended (the reference category is residence and university in the same municipality).The SRQ8 column adds, versus the previous column, the variables: working student status, foreign language spoken at home, foreign citizenship (the reference categories are non-working student, Italian spoken at home, Italian citizenship).Lastly, column SRQ9 adds, versus the previous column, the Disciplinary group variable (the reference category is Law). The coefficients associated with the binary indicators provide an estimate of the difference between the expected result and the reference value, keeping all other variables in the model the same.
Source: F. Peracchi (2014)
Age -10.634*** -15.566*** -15.977*** -13.919*** -12.663***
Female -23.642*** -28.918*** -28.272*** -27.209*** -15.603***
Time since diploma obtained 7.150*** 10.396*** 10.648*** 9.105*** 8.297***
Diploma grade 1.271***
Mean grade 16.518***
Tecn/Prof -28.750***
Classical 14.978**
Other lyceum -23.539**
Other Inst. 0,851
Working student 6961 -5438 0,296
Foreign language -34.855*** -50.423*** -44.894***
Foreign citizenship -92.330*** -114.900*** -119.407***
Outside Municipality -11,434 -15.437** -17.370** -13.348*
Outside Province -15.904** -17.511** -19.912*** -16.881**
Outside Region -36.909*** -36.053*** -36.776*** -35.906***
Mother with no diploma 0,192 -5,224 -4,475 -6,677 -2,707
Mother with degree 2,351 10,654 8,286 10,015 7,717
No mother -14372 -24535 -22135 -18828 -16341
Father without diploma -1,949 -6,896 -6,323 -9,060 -4,938
Father with degree -8,941 -2,007 -3,716 -2,281 -6,251
No father -7,987 -11,169 -12,833 -7,444 -9,150
Mother managerial/professional 0,469 2,189 3,998 3,297 2,954
Mother labourer -1,741 -11439 -10347 -4,637 -1,790
Mother unemployed -0,518 1,147 1,761 2,230 3,074
Father managerial/professional 19.151** 25.119*** 25.440*** 24.119*** 21.400***
Father labourer -0,074 -5,798 -4,987 -1277 -2,068
Father unemployed 7,621 0,310 -0,478 3,839 6704
agr-al (SC) -23102 -31.328*
arch (SC) 23.336* 30.179**
art (H) -42.635* -27,645
cult (H) -30.251* -11,980
bio (SC) 21,494 13,688
chim (SC) 32.033* 28,075
comun (SOC) -5,935 -22,219
econ (SOC) 34.476*** 14,312
farm (SAN) -14567 -16067
filo (H) -30.162* 14,290
form (H) -105.146*** -109.869***
geo (SOC) -65.560** -78.664***
ing (SC) 47.578*** 33.161***
lett (H) -41.232*** -8,431
ling (H) -10,832 -5,195
mat.fis.stat (SC) 57.751*** 59.493***
med (SAN) 64.018*** 91.903***
odon (SAN) 25,890 45,975
polit (SOC) 36.728*** 28.520**
psic (SOC) 50.890*** 47.548***
soc (SOC) -14,192 -29,233
sto (H) -47,531 -18,010
terr (SC) -49.383*** -50.586***
vet (SAN) 8304 16,470
BO 53.087*** 75.791*** 83.557*** 85.664*** 75.325***
CA -22,279 19760 18,611 13,745 1,634
FI 45.778*** 55.361*** 56.683*** 56.387*** 63.302***
LE -56.837*** -24167 -23821 -26502 -23279
ME -60.134*** -52.584*** -50.167*** -55.173*** -35.301*
MI 49.260*** 52.868*** 55.222*** 53.322*** 58.762***
NA -65.259*** -36.201*** -38.118*** -36.605*** -43.761***
PD 50.430*** 55.834*** 60.037*** 57.838*** 66.210***
PO 11733 12286 15675 14803 13313
RM2 -31.020** -1,820 -5643 -7,103 -10,795
UD 61.800*** 57.535*** 62.516*** 65.625*** 64.872***
Constant 998.117*** 992.966*** 1006.206*** 1011.710*** 991.384***
N 5853 5853 5853 5853 5853
df_m 64 26 29 32 56
r2_a .142 .0564 .0591 .0719 .105
F 16.4 13.3 12.5 14 12.8
rmse 185 194 194 193 189
legend: * p<1; ** p<.05; ***p<.01
212
TABLE 8.3: Mean diploma grade (VMD) and mean grade in university exams sat so far (VME), for eligible
students who did/did not come to sit the test, per Macro-group
VMD VME
Macro-groupCAME TO SIT
THE TEST
ELIGIBLE, DID NOT
COME TO SIT THE TEST
CAME TO SIT THE TEST
ELIGIBLE, DID NOT
COME TO SIT THE TEST
SAN (+)(*) 87.37 86.56 27.04 27.12
SC 82.77 (**) 81.72 26.26 (**) 26.06
SOC (+)(*) 81.83 81.20 26.21 (**) 25.93
H 80.70 (**) 78.83 27.67 (**) 27.27
ITA12 82.84 (**) 81.83 26.59 (**) 26.42
(**): The mean grades are significantly different (95% confidence interval) between students who came to sit the test and those who did not.
Source: See TABLE 3
75
80
85
90
SAN SC SOC H ITA12
VM
D
VENUTI
IDONEI NON VENUTI
25,5
26,0
26,5
27,0
27,5
28,0
SAN SC SOC H ITA12
VM
E
Came
Did not come
75
80
85
90
VM
D
26,0
26,2
26,4
26,6
26,8
27,0
VM
E
70
75
80
85
90
95
PO MI PD UD BO FI RM1 RM2 NA LE ME CA ITA12
VM
D
VENUTI IDONEI NON VENUTI
25
26
27
28
PO MI PD UD BO FI RM1 RM2 NA LE ME CA ITA12
VM
E
TABLE 8.6: Mean diploma grade (VMD) and mean grade in university exams sat so far (VME), for eligible students who did/did not come to sit the test, per University and Geographic Area
Source: See TABLE 8.4 for VMD and TABLE 8.5 for VME
Came Did not come
213
6065707580859095
100105110
1 2 3 4
VM
D
QUARTILE
SAN
6065707580859095
100105110
1 2 3 4
VM
D
QUARTILE
SC
6065707580859095
100105110
1 2 3 4
VM
D
QUARTILE
SOC
60
65
70
75
80
85
90
95
100
105
110
1 2 3 4
VM
D
QUARTILE
H
VENUTI IDONEI NON VENUTI
TABLE 8.8: Mean diploma grade (VMD) and mean grade in university exams sat so far (VME), for eligible students who did/did not come to sit the test, broken down by quartiles within each Macro-group
22
24
26
28
30
1 2 3 4
VM
E
QUARTILE
SAN
22
24
26
28
30
1 2 3 4
VM
E
QUARTILE
SC
22
24
26
28
30
1 2 3 4
VM
E
QUARTILE
SOC
22
24
26
28
30
1 2 3 4
VM
E
QUARTILE
H
The VMD grades are always significantly different (95% confidence interval) for students who came to sit the test and those who did not, except the cases marked with (N) - Source: See TABLE A-8.5.
The VMD grades are always significantly different (95% confidence interval) for students who came to sit the test and those who did notSource: See TABLE A-8.7 for VMD and TABLE A-8.8 for VME
(N)
(N)
(N)
(N)
Came Did not come
Q2Q1
TABLE 8.9: Mean diploma grade (VMD) and mean grade in university exams sat so far (VME), for eligible students who did/did not come to sit the test, broken down by quartiles within each
University
60
65
70
75
80
85
90
95
100
105
110
PO MI
PD UD
BO FI
RM
1R
M2
NA LE ME
CA
ITA
12
6065707580859095
100105110
PO MI
PD UD
BO FI
RM
1R
M2
NA LE ME
CA
ITA
12
60
65
70
75
80
85
90
95
100
105
110
PO MI
PD
UD
BO FI
RM
1R
M2
NA LE ME
CA
ITA
1260
65
70
75
80
85
90
95
100
105
110
PO MI
PD
UD
BO FI
RM
1
RM
2
NA LE ME
CA
ITA
12
VM
D
VENUTI
NON VENUTI
Q4Q3
The VMD grades are always significantly different (95% confidence interval) for students who came to sit the test and those who did not, except: Q1, 2 and 4 for PO, Q3 for UD, Q1 and 2 for BO, Q 1, 2 and 3 for RM1, Q3 and 4 for RM2, Q2, 3 and 4 for ME, Q2 for CASource: See TABLE A-8.9
The VMD grades are always significantly different (95% confidence interval) for students who came to sit the test and those who did not, except: Q1 and 2 for PO, Q1 for MI, Q2 and 4 for RM1, Q1 for LE, Q 3 and 4 for ME, Q1 and 4 for CASource: See TABLE A-8.10
Q2Q1 Q4Q3
22
23
24
25
26
27
28
29
30
PO MI
PD
UD
BO FI
RM
1
RM
2
NA LE ME
CA
ITA
12
22
23
24
25
26
27
28
29
30
PO MI
PD
UD
BO FI
RM
1
RM
2
NA LE ME
CA
ITA
12
22
23
24
25
26
27
28
29
30
PO MI
PD UD
BO FI
RM
1
RM
2
NA LE ME
CA
ITA
12
22
23
24
25
26
27
28
29
30
PO MI
PD
UD
BO FI
RM
1
RM
2
NA LE ME
CA
ITA
12
VM
E
Came
Did not come
214
TABLE 8.11: Mean age, percentage non-Italian citizenship (%citt no ita), percentage living outside the Region (% dist3) and percentage females, among tested students and eligible students who did not sit the test, per Macro-group
Macro-group
mean age %citt no ita % dist3 % F
came to sit the test
eligible, did not come to sit the test
came to sit the test
eligible, did not come to sit the test
came to sit the test
eligible, did not come to sit the test
came to sit the test
eligible, did not come to sit the test
SAN 23.27 23.59 1.84 2.59 17.21 23.39 62.23 61.93
SC 23.05 23.51 1.39 2.81 15.44 17.18 45.42 46.17
SOC 23.56 23.74 2.58 2.23 17.48 18.65 64.43 65.11
H 23.72 23.92 1.40 3.26 15.93 21.99 75.89 76.86
ITA12 23.36 23.69 1.86 2.63 16.49 19.76 59.34 62.39
% dist3= percentage of students who live in a different Region than the University they attend
22,5
23,0
23,5
24,0
SAN SC SOC H ITA12
Me
anag
e
venuti idonei non venuti
0
1
2
3
4
SAN SC SOC H ITA12
% c
itt
no
ita
10
14
18
22
26
SAN SC SOC H ITA12
% d
ist3
40
45
50
55
60
65
70
75
80
SAN SC SOC H ITA12
% F
TABLE 8.12: Mean age, percentage non-Italian citizenship (%citt no ita), percentage living outside the Region (% dist3)
and percentage females, among tested students and eligible students who did not sit the test, per University
University
mean age %citt no ita % dist3 % F
came to sit the test
eligible, did not come to sit the test
came to sit the test
eligible, did not come to sit the test
came to sit the test
eligible, did not come to sit the test
came to sit the test
eligible, did not come to sit the test
PO 24.37 26.84 2.19 7.49 24.45 27.27 69.59 70.59
MI 23.03 23.50 0.88 1.58 11.90 14.13 61.90 60.64
PD 23.30 23.72 1.09 3.51 13.30 14.32 64.12 63.04
UD 23.08 23.99 3.83 4.35 23.69 34.16 63.07 63.98
BO 23.14 23.42 3.53 5.56 37.77 40.45 61.14 60.52
FI 23.09 23.41 1.88 3.96 11.58 14.95 57.31 62.68
RM1 23.57 23.97 2.53 3.57 21.24 26.26 55.16 64.35
RM2 22.96 23.72 0.55 2.68 10.93 18.73 50.82 53.07
NA 22.96 23.10 1.20 0.42 2.91 3.51 57.71 61.79
LE 24.33 24.43 1.27 0.50 1.91 3.27 60.51 72.86
ME 23.28 24.82 0.00 2.20 29.77 30.84 67.94 63.88
CA 24.57 24.85 0.00 0.24 0.78 0.72 58.14 65.79
ITA12 23.36 23.69 1.86 2.63 16.49 19.76 59.34 62.39
% dist3= percentage of students who live in a different Region than the University they attend
22
23
24
25
26
27
28
PO MI
PD
UD
BO FI
RM
1
RM
2
NA LE ME
CA
ITA
12
Me
anag
e
venuti idonei non venuti
0
2
4
6
8
PO MI
PD
UD
BO FI
RM
1
RM
2
NA LE ME
CA
ITA
12
% c
itt
no
ita
0
10
20
30
40
50
PO MI
PD
UD
BO FI
RM
1
RM
2
NA LE ME
CA
ITA
12
% d
ist3
50
55
60
65
70
75
PO MI
PD
UD
BO FI
RM
1
RM
2
NA LE ME
CA
ITA
12
% F
215
TABLE 8.13: Mean age, percentage non-Italian citizenship (%citt no ita), percentage living outside the Region (% dist3) and percentage females, among tested students and eligible students who did not sit the test, per Geographic
Area
Geographic Area
mean age %citt no ita % dist3 % F
came to sit the test
eligible, did not come to sit the test
came to sit the test
eligible, did not come to sit the test
came to sit the test
eligible, did not come to sit the test
came to sit the test
eligible, did not come to sit the test
NORTH 23.33 23.79 1.59 2.78 16.08 15.83 63.95 62.27
CENTRE 23.36 23.70 2.38 3.59 20.39 26.87 56.16 61.95
SOUTH 23.42 23.58 0.90 0.52 5.99 4.95 59.54 63.70
CENTRE-NORTH (C-N) 23.25 23.59 1.89 3.24 17.70 23.06 62.08 61.84
CENTRE-SOUTH (C-S) 23.48 23.79 1.83 2.24 15.21 16.83 56.42 62.89
ITA12 23.36 23.69 1.86 2.63 16.49 19.76 59.34 62.39
% dist3= percentage of students who live in a different Region than the University they attend
23,0
23,2
23,4
23,6
23,8
24,0
NORD CENTRO SUD C-N C-S ITA12
Me
anag
e
venuti idonei non venuti
0
1
2
3
4
NORD CENTRO SUD C-N C-S ITA12
% c
itt
no
ita
0
5
10
15
20
25
30
NORD CENTRO SUD C-N C-S ITA12
% d
ist3
52
54
56
58
60
62
64
66
NORD CENTRO SUD C-N C-S ITA12
% F
TABLE 8.14: Percentage of eligible students who did/did not sit the test, per Geographic Area of the University,
Disciplinary group and Macro-group
Disciplinary group
North Centre South
came to sit the test
eligible, did not come to sit the
test
came to sit the test
eligible, did not come to sit the
test
came to sit the test
eligible, did not come to sit the
testagr.al 62.59 54.50 25.90 36.04 11.51 9.46arch 13.97 6.37 77.94 67.94 8.09 25.69art 25.00 4.02 75.00 95.98 0.00 0.00bio 55.47 27.32 31.25 63.57 13.28 9.11
chim 47.17 6.98 46.23 50.00 6.60 43.02comun 45.04 13.98 45.04 69.92 9.92 16.09
cult 33.10 29.40 45.77 61.68 21.13 8.92econ 31.83 9.49 55.05 71.86 13.12 18.64farm 50.76 37.54 23.86 21.15 25.38 41.32filo 38.89 25.80 50.93 41.08 10.19 33.12
form 37.50 19.47 41.41 47.52 21.09 33.00geo 56.60 16.08 39.62 72.36 3.77 11.56giu 43.02 28.88 33.98 36.43 23.00 34.69ing 11.02 13.23 68.47 71.16 20.52 15.61lett 41.05 15.68 47.89 79.23 11.05 5.09ling 39.39 18.58 41.99 51.17 18.61 30.25
mat.fis.stat 18.81 14.29 75.77 69.61 5.41 16.10med 26.21 24.55 42.24 61.04 31.55 14.41odon 29.55 29.00 22.73 53.50 47.73 17.50polit 37.81 30.19 41.29 60.80 20.90 9.01psic 28.27 18.21 58.12 71.54 13.61 10.25soc 22.78 12.46 46.84 64.01 30.38 23.53sto 36.84 19.66 50.88 66.29 12.28 14.04terr 11.25 7.34 79.54 83.64 9.21 9.02vet 51.61 70.31 30.11 18.23 18.28 11.46
ITA12 33.37 21.81 49.53 56.75 17.10 21.44
Macro-group
North Centre South
came to sit the test
eligible, did not come to sit the
test
came to sit the test
eligible, did not come to sit the
test
came to sit the test
eligible, did not come to sit the
testSAN 39.39 31.28 32.25 47.46 28.35 21.26SC 24.25 15.11 64.32 68.12 11.43 16.77
SOC 37.95 22.85 43.41 52.05 18.65 25.10H 37.46 18.79 47.58 61.91 14.96 19.30
ITA12 33.37 21.81 49.53 56.75 17.10 21.44
216
TABLE 8.15: Percentage of eligible students who did/did not sit the test broken down by type of secondary school, per Disciplinary group, Macro-group, University and Geographic Area
Disciplinary group
Classical or Scientific lyceum
Technical or Vocational Institute
Other institute Other lyceum
Macro-group
Classical or Scientific lyceum
Technical or Vocational Institute
Other institute Other lyceum
came to sit the
test
eligible, did not come to sit the
test
came to sit the
test
eligible, did not come to sit the
test
came to sit the
test
eligible, did not come to sit the
test
came to sit the
test
eligible, did not come to sit the
test
came to sit the
test
eligible, did not come to sit the
test
came to sit the
test
eligible, did not come to sit the
test
came to sit the
test
eligible, did not come to sit the
test
came to sit the
test
eligible, did not come to sit the
test
agr.al 45.26 41.47 38.69 48.39 13.87 6.45 2.19 3.69 SAN 83.50 86.24 7.92 6.37 4.35 3.82 4.24 3.56
arch 82.75 77.23 11.37 10.02 3.92 5.94 1.96 6.81 SC 72.99 69.47 17.64 19.23 5.83 6.04 3.55 5.26
art 56.45 62.78 6.45 14.24 14.52 8.09 22.58 14.89 SOC 64.17 67.52 20.31 16.23 5.42 5.74 10.10 10.51
bio 71.08 66.60 16.87 19.81 5.62 5.28 6.43 8.30 H 63.46 61.36 10.52 12.81 8.37 6.86 17.65 18.97
chim 61.46 75.16 30.21 16.15 6.25 4.97 2.08 3.73 ITA12 70.18 70.17 15.84 14.55 5.86 5.67 8.13 9.61
comun 60.17 51.52 19.49 22.26 8.47 8.84 11.86 17.38
cult 66.42 64.71 8.76 9.52 9.49 7.84 15.33 17.93
University
Classical or Scientific lyceum
Technical or Vocational Institute
Other institute Other lyceum
econ 54.32 57.60 36.82 30.88 5.45 6.29 3.41 5.23came to sit the
test
eligible, did not come to sit the
test
came to sit the
test
eligible, did not come to sit the
test
came to sit the
test
eligible, did not come to sit the
test
came to sit the
test
eligible, did not come to sit the
test
farm 77.49 76.63 11.52 12.34 4.97 4.79 6.02 6.24 PO 50.78 48.33 27.27 30.00 8.46 11.11 13.48 10.56
filo 79.25 76.59 4.72 6.69 10.38 4.68 5.66 12.04 MI 69.11 66.47 15.82 16.68 5.82 4.89 9.24 11.96
form 32.26 25.94 18.55 21.50 9.68 13.99 39.52 38.57 PD 56.69 64.81 24.54 18.62 7.43 4.62 11.34 11.95
geo 22.00 29.41 50.00 53.53 4.00 4.71 24.00 12.35 UD 53.19 50.00 28.01 28.21 11.35 8.97 7.45 12.82
giu 74.47 77.42 12.82 10.16 4.94 4.83 7.76 7.60 BO 74.79 70.97 15.97 14.63 4.76 6.55 4.48 7.84
ing 80.59 80.75 14.38 14.50 2.97 3.77 2.05 0.98 FI 63.77 58.44 15.61 16.53 11.49 12.90 9.13 12.13
lett 77.42 81.40 5.38 5.69 6.99 2.19 10.22 10.72 RM1 82.09 80.37 10.37 10.35 1.69 1.80 5.85 7.49
ling 59.13 54.40 13.94 16.04 7.69 6.39 19.23 23.16 RM2 79.01 71.89 8.84 15.54 7.18 6.74 4.97 5.83
mat.fis.stat 78.53 64.39 15.54 30.98 4.24 2.93 1.69 1.71 NA 77.55 74.37 10.02 11.28 5.01 5.53 7.43 8.83
med 90.79 91.38 3.16 3.25 2.63 3.31 3.42 2.06 LE 66.45 56.52 18.71 21.23 6.45 5.88 8.39 16.37
odon 81.40 88.48 6.98 5.76 9.30 2.62 2.33 3.14 ME 71.43 70.97 11.90 12.44 3.97 6.45 12.70 10.14
polit 61.41 55.27 22.28 23.65 4.89 6.32 11.41 14.75 CA 65.87 62.99 22.22 18.87 3.17 5.15 8.73 12.99
psic 63.59 65.34 8.15 9.25 7.07 5.74 21.20 19.67 ITA12 70.18 70.17 15.84 14.55 5.86 5.67 8.13 9.61
soc 40.00 44.60 17.14 26.26 2.86 11.15 40.00 17.99
sto 82.69 66.07 11.54 14.29 0.00 8.93 5.77 10.71
Geographic Area
Classical or Scientific lyceum
Technical or Vocational Institute
Other institute Other lyceum
terr 66.29 60.47 17.28 19.09 9.35 11.99 7.08 8.45came to sit the
test
eligible, did not come to sit the
test
came to sit the
test
eligible, did not come to sit the
test
came to sit the
test
eligible, did not come to sit the
test
came to sit the
test
eligible, did not come to sit the
test
vet 79.35 75.40 13.04 11.76 6.52 5.88 1.09 6.95 NORTH 60.29 64.12 21.93 18.67 7.52 5.29 10.26 11.92
ITA12 70.18 70.17 15.84 14.55 5.86 5.67 8.13 9.61 CENTRE 76.17 72.46 12.37 13.30 5.01 5.87 6.45 8.37
SOUTH 73.53 70.72 13.18 13.42 4.87 5.59 8.42 10.28
CENTRE-NORTH 62.83 64.82 19.76 16.97 8.09 7.44 9.31 10.76
CENTRE-SOUTH 78.61 75.20 11.33 12.27 3.29 4.01 6.77 8.52
ITA12 70.18 70.17 15.84 14.55 5.86 5.67 8.13 9.61
TABLE 8.17: Percentage of married students, working students, students who do not speak Italian at home (%non ita) and students who do not know any language other than Italian (% no other lang) among tested students and pre-
registered students who did not sit the test, per Macro-group
Macro-group
% Married % Working students % nonita % no other lang
came to sit the test
pre-registered but did not come to sit
the test
came to sit the test
pre-registered but did not come to sit
the test
came to sit the test
pre-registered but did not come to sit
the test
came to sit the test
pre-registered but did not come to sit
the test
SAN 0.65 0.75 6.82 8.07 5.30 6.38 29.87 26.08
SC 0.69 0.71 10.59 12.66 5.29 6.79 25.98 30.19
SOC 1.87 2.87 16.32 20.87 8.39 10.87 29.36 33.48
H 2.48 2.16 16.90 19.94 5.81 10.82 27.88 26.12
ITA12 1.37 1.75 12.93 16.19 6.42 8.92 28.04 29.88
% nonita= percentage of students who do not speak Italian at home. No data was treated as “Language spoken at home = Italian”
0,0
0,5
1,0
1,5
2,0
2,5
3,0
3,5
SAN SC SOC H ITA12
% m
arri
ed
venuti preiscritti non venuti
0
5
10
15
20
25
SAN SC SOC H ITA12% w
ork
ing
stu
de
nts
0
2
4
6
8
10
12
SAN SC SOC H ITA12
% n
on
ita
0
10
20
30
40
SAN SC SOC H ITA12
% n
o o
the
rla
ng
217
TABLE 8.18: Percentage of married students, working students, students who do not speak Italian at home (%non ita) and students who do not know any language other than Italian (% no other lang) among tested students and pre-registered students who did not sit the test, per University
University
% Married % Working students % nonita % no other lang
came to sit the test
pre-registered but did not come
to sit the test
came to sit the test
pre-registered but did not come
to sit the test
came to sit the test
pre-registered but did not come
to sit the test
came to sit the test
pre-registered but did not come
to sit the test
PO 4.08 11.29 13.79 27.42 6.58 6.45 34.17 32.26
MI 0.75 1.78 21.30 28.83 4.14 7.47 25.31 28.83
PD 1.46 2.78 12.02 13.89 4.19 7.41 26.96 20.37
UD 0.70 0.00 9.06 22.58 9.76 6.45 29.97 32.26
BO 0.54 0.69 14.67 15.97 7.88 6.25 22.83 20.83
FI 0.87 1.30 9.55 10.06 6.51 7.47 25.62 24.35
RM1 1.39 1.44 12.37 16.56 7.60 9.83 26.92 30.65
RM2 1.09 2.33 14.75 14.73 7.65 10.08 25.68 27.91
NA 0.51 0.00 10.45 10.89 7.19 8.66 34.76 33.80
LE 3.18 5.00 10.19 12.50 4.46 20.00 41.40 45.00
ME 3.05 6.00 8.40 6.00 3.05 4.00 28.24 46.00
CA 4.65 7.46 8.53 13.43 3.10 5.97 28.68 32.84
ITA12 1.37 1.75 12.93 16.19 6.42 8.92 28.04 29.88
% nonita= percentage of students who do not speak Italian at home. No data was treated as “Language spoken at home = Italian”
0
2
4
6
8
10
12
% m
arri
ed
venuti preiscritti non venuti
0
5
10
15
20
25
% n
on
ita
05
101520253035
% w
ork
ing
stu
de
nts
10
20
30
40
50
% n
o o
the
rla
ng
TABLE 8.19: Percentage of married students, working students, students who do not speak Italian at home (%non ita) and students who do not know any language other than Italian (% no other lang) among tested students and pre-registered students
who did not sit the test, per Geographic Area
Geographic Area
% Married % Working students % nonita % no other lang
came to sit the test
pre-registered but did not come to sit
the test
came to sit the test
pre-registered but did not come to sit
the test
came to sit the test
pre-registered but did not come to sit
the test
came to sit the test
pre-registered but did not come to sit
the test
NORTH 1.48 3.11 15.67 24.90 5.38 7.26 26.01 29.05
CENTRE 1.14 1.42 12.14 15.56 7.38 9.31 27.91 27.59
SOUTH 1.80 1.94 9.89 10.87 5.69 8.74 34.17 35.73
CENTRE-NORTH (C-N) 1.23 2.14 14.14 18.63 5.94 7.17 26.76 25.48
CENTRE-SOUTH (C-S) 1.51 1.59 11.65 15.23 6.93 9.61 29.39 31.60
ITA12 1.37 1.75 12.93 16.19 6.42 8.92 28.04 29.88
% nonita= percentage of students who do not speak Italian at home. No data was treated as “Language spoken at home = Italian”
0
1
1
2
2
3
3
4
NORD CENTRO SUD C-N C-S ITA12
% m
arri
ed
venuti preiscritti non venuti
0
2
4
6
8
10
12
NORD CENTRO SUD C-N C-S ITA12
% n
on
ita
0
5
10
15
20
25
30
NORD CENTRO SUD C-N C-S ITA12
% w
ork
ing
stu
de
nts
10
15
20
25
30
35
40
NORD CENTRO SUD C-N C-S ITA12
% n
o o
the
rla
ng
218
TABLE A-8.1: Mean diploma grade (VMD) for eligible students who did/did not come to sit the test, broken down by quartiles within each Disciplinary group
Disciplinary group
VMD - CAME TO SIT THE TEST VMD - DID NOT COME TO SIT THE TESTQUARTILE
mean medianQUARTILE
mean median4 3 2 1 4 3 2 1
agr.al 99.27 (**) 86.89 (**) 77.75 (**) 67.24 82.35 81 95.56 82.23 76.15 68.36 80.21 78arch 97.16 (**) 86.31 (**) 76.06 (**) 64.86 (**) 80.15 80 98.55 87.35 77.85 66.03 81.93 82art 93.14 79.79 (**) 72 62.53 76.61 75 90.76 77.72 70.61 62.72 75.05 73bio 98.52 (**) 85.26 (**) 77.72 (**) 66.21 (**) 81.80 81 94.58 80.91 71.99 62.94 77.29 76
chim 100.72 (**) 90.33 80.79 68.25 84.51 83 99.24 90.31 80.00 68.09 84.11 83comun 90.46 (**) 79.34 (**) 70.18 (**) 62.7 75.16 74 94.71 81.01 71.73 63.64 77.32 75
cult 97.13 (**) 82.34 74.44 (**) 64.29 78.40 77 93.91 81.42 72.63 63.03 77.36 77econ 98.79 (**) 87.60 (**) 78.20 (**) 67.11 82.59 82 97.98 86.38 76.78 66.92 81.85 80farm 99.35 90.23 (**) 81.31 (**) 70.17 84.83 85 99.21 88.89 80.32 69.08 83.95 84filo 98.71 90.55 (**) 80.04 69.57 (**) 84.00 83 98.53 87.38 79.28 66.86 82.48 83
form 92.72 81.97 (**) 73.63 65.52 (**) 78.26 77 91.74 80.63 73.30 63.68 76.77 77geo 92.83 81.38 75.60 (**) 66.88 (**) 78.39 78 93.89 81.08 71.69 63.17 77.16 75.5giu 99.77 (**) 88.71 (**) 79.14 (**) 67.72 83.42 83 98.79 87.52 78.24 67.45 82.82 82ing 101.05 92.7 80.96 (**) 68.25 85.25 85 100.42 91.96 81.83 69.15 85.56 86lett 100.58 (**) 90.90 (**) 81.16 (**) 68.30 (**) 84.68 85 98.71 85.75 76.44 64.37 81.23 80.5ling 97.90 (**) 85.81 (**) 75.82 (**) 65.4 80.70 80 94.89 82.91 74.77 65.10 78.95 78
mat.fis.stat 101.48 90.78 79.46 (**) 67.96 (**) 83.63 83 100.92 90.24 77.34 66.51 83.12 83med 110 99.42 (**) 90.14 (**) 72.27 90.41 95 110.00 98.70 87.11 71.01 88.38 92odon 97.68 (!) 97.68 (**) 85.55 (**) 71.55 (**) 88.11 89.5 99.57 90.90 81.88 67.37 84.29 86polit 94.21 81.28 74.09 65.15 78.40 77 94.32 81.83 74.13 65.09 78.20 77psic 98.48 (**) 85.93 (**) 78.63 (**) 68.24 (**) 82.36 80 96.29 83.13 74.80 65.74 79.22 78soc 95.39 (**) 82.28 74.67 65.79 79.34 78 92.72 81.36 75.34 65.26 77.89 78sto 94.56 83.69 74.8 65.25 (**) 78.08 78 94.85 83.57 74.90 64.71 79.18 79terr 99.60 (**) 87.00 (**) 75.39 65.33 81.07 80 97.05 84.57 75.60 65.10 79.82 80vet 98.6 90.00 (**) 83.90 (**) 74.08 (**) 85.77 86 97.46 87.29 79.17 70.28 83.04 82
ITA12 99.59 (**) 87.91 (**) 78.27 (**) 67.11 (**) 82.84 (**) 82 98.88 86.88 77.06 66.15 81.83 81
(**): The asterisks beside the grades of eligible students indicate that the means are significantly different (95% confidence interval) for students who came to sit the test versus those who did not. No asterisks indicate that the means are not significantly different. Grey-shading indicates cases where the mean grade for students who did not come to sit the test is significantly higher versus students who did come to sit the test.(!): indicates that the top 50% students in that Disciplinary group have the same meanSource: See TABLE 3
60
62
64
66
68
70
72
74
76
agr.
al
arch ar
t
bio
chim
com
un
cult
eco
n
farm filo
form ge
o
giu
ing
lett
ling
mat
.fis
.sta
t
med
od
on
po
lit
psi
c
soc
sto
terr
vet
ITA
12
VM
D
VENUTI NON VENUTI
60
65
70
75
80
85
90
95
agr.
al
arch ar
t
bio
chim
com
un
cult
eco
n
farm filo
form ge
o
giu
ing
lett
ling
mat
.fis
.sta
t
med
od
on
po
lit
psi
c
soc
sto
terr
vet
ITA
12
VM
D
VENUTI NON VENUTI
TABLE A-8.2: Mean diploma grade (VMD) for eligible students who did/did not come to sit the test, broken down by quartiles (Q1 and Q2)
within each Disciplinary group
Q2
Q1
Source: See TABLE A-8.1
219
70
75
80
85
90
95
100
105
110
agr.
al
arch ar
t
bio
chim
com
un
cult
eco
n
farm filo
form ge
o
giu
ing
lett
ling
mat
.fis
.sta
t
med
od
on
po
lit
psi
c
soc
sto
terr
vet
ITA
12
VM
DVENUTI NON VENUTI
70
75
80
85
90
95
100
105
110
agr.
al
arch ar
t
bio
chim
com
un
cult
eco
n
farm filo
form ge
o
giu
ing
lett
ling
mat
.fis
.sta
t
med
od
on
po
lit
psi
c
soc
sto
terr
vet
ITA
12
VM
D
VENUTI NON VENUTI
TABLE A-8.3: Mean diploma grade (VMD) for eligible students who did/did not come to sit the test, broken down by quartiles (Q3 and Q4)
within each Disciplinary group
Q4
Q3
Source: See TABLE A-8.1
TABLE A-8.4: Mean grade in university exams sat so far (VME), for eligible students who did/did not come to sit the test, broken down by quartiles within each Disciplinary group
Disciplinary group
VME - CAME TO SIT THE TEST VME - DID NOT COME TO SIT THE TESTQUARTILE
mean medianQUARTILE
mean median4 3 2 1 4 3 2 1
agr.al 28.56 (**) 27.31 (**) 26.00 (**) 24.15 26.51 26.67 28.08 26.59 25.52 23.75 25.98 25.94arch 28.69 27.61 26.75 (**) 25.03 (**) 27.03 27.22 28.76 27.57 26.59 24.92 26.94 27.07art 29.29 (**) 28.40 (**) 27.47 (**) 25.33 27.61 28.00 28.73 27.61 26.61 24.80 26.93 27.06bio 28.39 (**) 26.87 (**) 25.68 (**) 23.81 (**) 26.19 26.25 28.10 26.34 25.08 23.39 25.72 25.67
chim 28.64 (**) 27.02 (**) 25.54 (**) 23.47 (**) 26.16 26.39 27.98 26.00 24.58 22.82 25.30 25.27comun 28.13 26.93 (**) 25.53 (**) 23.21 25.94 26.24 28.33 26.68 25.28 22.87 25.79 26.00
cult 29.36 (**) 28.51 (**) 27.39 (**) 25.69 (**) 27.73 27.99 29.24 28.17 27.06 25.22 27.42 27.71econ 28.02 (**) 26.29 (**) 24.80 (**) 22.91 (**) 25.49 25.60 27.57 25.52 24.15 22.47 24.93 24.83farm 28.46 27.03 (**) 25.87 (**) 24.24 (**) 26.39 26.45 28.45 26.87 25.53 23.44 26.08 26.24filo 29.80 (**) 29.30 (**) 28.55 (**) 27.23 (**) 28.72 29.01 29.69 29.05 28.27 26.79 28.45 28.75
form 28.90 (**) 27.81 (**) 26.74 (**) 25.38 (**) 27.21 27.21 28.64 27.42 26.38 24.84 26.81 26.94geo 28.24 26.92 (**) 25.98 (**) 24.46 (**) 26.35 26.33 27.93 26.40 25.36 23.84 25.86 25.83giu 28.85 (**) 27.32 (**) 25.93 (**) 23.95 (**) 26.51 26.76 28.69 26.96 25.54 23.57 26.19 26.21ing 28.21 26.11 24.68 22.93 25.48 25.37 28.15 26.14 24.74 22.96 25.50 25.38lett 29.52 (**) 28.61 (**) 27.71 (**) 26.28 (**) 28.03 28.19 29.35 28.34 27.27 25.63 27.65 27.86ling 28.96 (**) 27.54 26.58 (**) 25.02 (**) 27.03 27.05 28.79 27.47 26.27 24.53 26.77 26.89
mat.fis.stat 29.24 (**) 27.38 (**) 25.69 (**) 23.42 (**) 26.43 26.47 28.76 26.57 25.01 22.79 25.78 25.79med 29.33 28.35 27.33 (**) 25.57 27.63 27.80 29.31 28.39 27.40 25.57 27.67 27.93odon 29.38 28.46 (**) 27.16 25.62 27.66 28.07 29.23 28.07 27.01 24.99 27.32 27.61polit 28.56 (**) 27.15 (**) 25.83 (**) 23.42 26.23 26.50 28.10 26.66 25.42 23.71 25.97 26.08psic 28.54 (**) 27.46 (**) 26.24 (**) 24.22 (**) 26.63 26.94 28.29 26.61 25.29 23.21 25.84 25.95soc 28.53 27.1 25.97 24.19 26.44 26.53 28.41 27.04 25.78 24.27 26.38 26.51sto 29.74 (**) 29.17 (**) 28.31 (**) 26.61 (**) 28.43 28.71 29.49 28.67 27.82 25.69 27.92 28.29terr 28.43 (**) 27.10 (**) 25.97 (**) 24.33 (**) 26.46 26.53 28.26 26.86 25.85 23.97 26.24 26.37vet 28.81 (**) 27.77 (**) 26.66 (**) 24.95 (**) 27.03 27.27 28.11 26.58 25.48 23.88 26.01 26.03
ITA12 28.90(**) 27.43(**) 26.08(**) 23.97(**) 26.59(**) 26.79 28.84 27.29 25.85 23.75 26.42 26.58(**): The asterisks beside the grades of eligible students indicate that the means are significantly different (95% confidence interval) for students who came to sit the test versus those who did not. No asterisks indicate that the means are not significantly different. Grey-shading indicates cases where the mean grade for students who did not come to sit the test is significantly higher versus students who did come to sit the test.Source: See TABLE 3
220
20
21
22
23
24
25
26
27
28
29
30
agr.
al
arch ar
t
bio
chim
com
un
cult
eco
n
farm filo
form ge
o
giu
ing
lett
ling
mat
.fis
.sta
t
med
od
on
po
lit
psi
c
soc
sto
terr
vet
ITA
12
VM
EVENUTI NON VENUTI
20
21
22
23
24
25
26
27
28
29
30
agr.
al
arch ar
t
bio
chim
com
un
cult
eco
n
farm filo
form ge
o
giu
ing
lett
ling
mat
.fis
.sta
t
med
od
on
po
lit
psi
c
soc
sto
terr
vet
ITA
12
VM
E
VENUTI NON VENUTI
TABLE A-8.5: Mean grade in university exams sat so far (VME), for eligible students who did/did not come to sit the test, broken down by quartiles (Q1 and Q2)
within each Disciplinary group
Q2
Q1
Source: See TABLE A-8.4
24
25
26
27
28
29
30
agr.
al
arch ar
t
bio
chim
com
un
cult
eco
n
farm filo
form ge
o
giu
ing
lett
ling
mat
.fis
.sta
t
med
od
on
po
lit
psi
c
soc
sto
terr
vet
ITA
12
VM
E
VENUTI NON VENUTI
24
25
26
27
28
29
30
agr.
al
arch ar
t
bio
chim
com
un
cult
eco
n
farm filo
form ge
o
giu
ing
lett
ling
mat
.fis
.sta
t
med
od
on
po
lit
psi
c
soc
sto
terr
vet
ITA
12
VM
E
VENUTI NON VENUTI
TABLE A-8.6: Mean grade in university exams sat so far (VME), for eligible students who did/did not come to sit the test, broken down by quartiles (Q3 and Q4) within
each Disciplinary group
Q4
Q3
Source: See TABLE A-8.4
221
TABLE A-8.7: Mean diploma grade (VMD) for eligible students who did/did not come to sit the test, broken down by quartiles within each Macro-group
Macro-group
VMD - CAME TO SIT THE TESTVMD – ELIGIBLE, DID NOT COME TO SIT THE
TEST
QUARTILESmean median
QUARTILESmean median
1 2 3 4 1 2 3 4
SAN 72.18 (**) 85.81 (**) 97.65 (**) 110 87.37 90 69.42 83.13 96.95 110 86.56 88
SC 67.25 (**) 78.27 (**) 88.19 (**) 100.19 (**) 82.77 82 66.03 77.08 86.82 99.12 81.72 81
SOC 66.5 77.07 86.09 (**) 98.57 (**) 81.83 80 66.55 76.82 85.59 97.7 81.2 80
H 65.29 75.94 (**) 85.24 (**) 97.68 (**) 80.7 80 64.83 74.87 82.83 95.44 78.83 78
ITA12 67.11 (**) 78.27 (**) 87.91 (**) 99.59 (**) 82.84 (**) 82 66.15 77.06 86.88 98.88 81.83 81
(**): The mean grades are significantly different (95% confidence interval) for students who came to sit the test versus those who did not.
Source: See TABLE 3. The corresponding graphs are shown in TABLE 8.8
TABLE A-8.8: Mean grade in university exams sat so far (VME), for eligible students who did/did not come to sit the test, broken down by quartiles within each Macro-group
Macro-group
VME - CAME TO SIT THE TESTVME – ELIGIBLE, DID NOT COME TO SIT THE
TEST
QUARTILEmean median
QUARTILEmean median
1 2 3 4 1 2 3 4
SAN 24.73 (**) 26.60 (**) 27.79 (**) 29.04 (**) 27.04 27.24 24.58 26.77 28 29.14 27.12 27.43
SC 23.66 (**) 25.69 (**) 27.08 (**) 28.65 (**) 26.26 26.38 23.5 25.51 26.83 28.44 26.06 26.2
SOC 23.57 (**) 25.66 (**) 27.06 (**) 28.58 (**) 26.21 26.42 23.29 25.32 26.71 28.44 25.93 26
H 25.58 (**) 27.27 (**) 28.39 (**) 29.43 (**) 27.67 27.88 25 26.85 28.01 29.19 27.27 27.44
ITA12 23.97 (**) 26.08 (**) 27.43 (**) 28.90 (**) 26.59 (**) 26.79 23.75 25.85 27.29 28.84 26.42 26.58
(**): The mean grades are significantly different (95% confidence interval) for students who came to sit the test versus those who did not.
Grey-shading indicates cases where the mean grade for students who did not come to sit the test is significantly higher versus students who did come to sit the test.
Source: See TABLE 3. The corresponding graphs are shown in TABLE 8.8
222
TABLE A-8.9: Mean diploma grade (VMD) for eligible students who did/did not come to sit the test, broken down by quartiles within each University
University
CAME TO SIT THE TEST ELIGIBLE, DID NOT COME TO SIT THE TEST
QUARTILESmean median
QUARTILESmean median
1 2 3 4 1 2 3 4
PO 68.7 78.31 87.69 (**) 98.74 82.84 82 67.68 78 86.21 98.5 82.07 80.5
MI 67.79 (**) 78.83 (**) 86.64 (**) 98.58 (**) 82.46 82 66.63 76.77 85.33 97.35 80.93 80
PD 71.78 (**) 83.15 (**) 92.55 (**) 101.84 (**) 86.5 87 68.74 80.21 89.51 99.32 84.26 84
UD 69.61 (**) 79.88 (**) 88.08 99.47 (**) 83.61 83 67.2 78.16 88.15 101.44 83.38 82
BO 69.63 80.1 87.79 (**) 95.25 (**) 82.86 83 68.66 80.08 89.47 99.4 84.13 84
FI 70.35 (**) 81.45 (**) 91.01 (**) 101.26 (**) 85.57 85 67.46 77.61 85.8 98.38 81.83 81
RM1 62.99 71.15 79.49 94.10 (**) 75.68 73 62.99 71.02 79.24 93.32 75.61 73
RM2 69.13 (**) 80.98 (**) 91.13 99.56 85.05 85 67.44 79.75 90.15 99.64 83.79 84
NA 75.22 (**) 87.70 (**) 98.41 (**) 98.41 (!) 89.36 92 70.49 81.98 92.3 99.82 85.88 86
LE 73.85 (**) 87.13 (**) 98.64 (**) 110.00 (**) 90.08 92 70.1 81.43 90.96 99.95 85.02 85
ME 73.12 (**) 83.1 96.9 110 87.82 87 70.05 82.66 96.61 110 86.86 87
CA 71.85 (**) 81.69 94.06 (**) 102.03 (**) 86.99 87 68.68 81.14 96.2 110 86 85
ITA12 67.11 (**) 78.27 (**) 87.91 (**) 99.59 (**) 82.84 (**) 82 66.15 77.06 86.88 98.88 81.83 81
(!): indicates that the top 50% students in that University have the same mean
(**): The mean grades are significantly different (95% confidence interval) for students who came to sit the test versus those who did not.
Source: See TABLE 3. The corresponding graphs are shown in TABLE 8.9
TABLE A-8.10: Mean grade in university exams sat so far (VME) for eligible students who did/did not come to sit the test, broken down by quartiles within each University
University
VME - CAME TO SIT THE TEST VME – ELIGIBLE, DID NOT COME TO SIT THE TEST
QUARTILES (Q)mean median
QUARTILES (Q)mean median
1 2 3 4 1 2 3 4
PO 23.31 25.39 26.93 (**) 28.69 (**) 26.08 26.16 23.14 25.27 26.62 28.43 25.85 25.90
MI 24.18 26.10 (**) 27.49 (**) 28.93 (**) 26.67 26.79 24.02 25.90 27.27 28.79 26.50 26.60
PD 24.30 (**) 26.07 (**) 27.32 (**) 28.78 (**) 26.61 26.72 23.79 25.69 26.88 28.41 26.19 26.29
UD 23.68 (**) 25.47 (**) 26.81 (**) 28.32 (**) 26.08 26.14 23.19 24.89 26.32 27.82 25.52 25.69
BO 24.82 (**) 26.87 (**) 28.08 (**) 29.36 (**) 27.29 27.53 24.15 26.49 27.96 29.21 26.95 27.31
FI 24.37 (**) 26.11 (**) 27.35 (**) 28.83 (**) 26.67 26.71 23.92 25.80 27.02 28.56 26.32 26.42
RM1 23.44 (**) 25.83 27.29 (**) 28.81 26.34 26.65 23.60 25.83 27.22 28.75 26.34 26.55
RM2 24.01 (**) 25.98 (**) 27.64 (**) 29.31 (**) 26.73 26.75 23.63 25.64 27.22 28.88 26.34 26.49
NA 24.35 (**) 26.44 (**) 27.69 (**) 28.90 (**) 26.85 27.13 23.39 25.52 27.01 28.62 26.13 26.30
LE 24.42 26.60 (**) 27.82 (**) 29.09 (**) 26.96 27.20 24.15 25.93 27.23 28.70 26.50 26.58
ME 24.74 (**) 26.47 (**) 27.5 29.09 26.95 27.06 23.78 25.97 27.33 29.05 26.54 26.65
CA 24.71 26.64 (**) 27.86 (**) 29.15 27.11 27.24 24.79 27.00 28.20 29.28 27.32 27.67
ITA12 23.97 (**) 26.08 (**) 27.43 (**) 28.90 (**) 26.59 (**) 26.79 23.75 25.85 27.29 28.84 26.42 26.58
(**): The mean grades are significantly different (95% confidence interval) for students who came to sit the test versus those who did not.
Grey-shading indicates cases where the mean grade for students who did not come to sit the test is significantly higher versus students who did come to sit the test.
Source: See TABLE 3. The corresponding graphs are shown in TABLE 8.9
223
PO
MI (+)(*)PD (+)(*)
UD (+)(*)
BO (+)(*)
FI (+)(*)RM1
RM2NA
LE
ME
CA
ITA12
-20
-10
0
10
20
30
40
-30 -20 -10 0 10 20 30
P
R12
NORD (+)(*)
CENTRO
SUD
CENTRO-NORD (+)(*)
CENTRO-SUD
ITA12
-6
-4
-2
0
2
4
6
8
10
-15 -10 -5 0 5 10 15
P
R12
SAN (+)(*)
SC
SOC (+)(*)H
ITA12
-6
-4
-2
0
2
4
6
8
10
-6 -4 -2 0 2 4 6 8 10
P
R12
agr.al
arch(*)
art
bio (*)
chim
comuncult
econ farm
filo (+)(*)
form
geogiu (+)(*)
ing (*)
lett (*)
ling
mat.fis.stat (+)(*)
med (+)(*)odon (+)(*)
polit (+)(*)
psic (+)(*)
soc
sto (+)(*)
terr
vet (+)(*)
ITA12
-15
-10
-5
0
5
10
15
20
25
-30 -20 -10 0 10 20
P
R12
Disciplinary group
Macro-group
University
TABLE A-8.11: Participation Index and quality of active teachers (VQR 2004-2010 grade, measured by R12), per Disciplinary group, Macro-group, University and Geographic Area
Geographic Area
R12 represents the ETP-weighted average of the ratios between the mean VQR 2004-2010 grade obtained by each teacher involved in teaching each course within the Universities of the Geographic Area and the mean grade obtained by all teachers in the 12 participating universities in the CUN group to which the teacher belongs.
Source: See TABLES 2.1 to 2.5 for the P values. See TABLE A-6.3.1 for R12 values.
Correlation = 0.10
Correlation = 0.18
NORD (+)(*)
CENTRO
SUD
CENTRO-NORD (+)(*)
CENTRO-SUD
ITA12
-6
-4
-2
0
2
4
6
8
10
-8 -6 -4 -2 0 2 4 6 8
P
VM
PO
MI (+)(*)PD (+)(*)
UD (+)(*)
BO (+)(*)
FI (+)(*)RM1
RM2NA
LE
ME
CA ITA12
-20
-10
0
10
20
30
40
-20 -15 -10 -5 0 5 10 15
P
VM
Disciplinary group
Macro-group
University Geographic Area
agr.al
arch(*)
art
bio (*)
chim
comuncult
econfarm
filo (+)(*)
form
geo giu (+)(*)
ing (*)
lett (*)
ling
mat.fis.stat (+)(*)
med (+)(*)odon (+)(*)
polit (+)(*)
psic (+)(*)
socsto (+)(*)
terr
vet (+)(*)
ITA12
-15
-10
-5
0
5
10
15
20
25
-30 -20 -10 0 10 20
P
VM
SAN (+)(*)
SC
SOC (+)(*)H
ITA12
-6
-4
-2
0
2
4
6
8
10
-20 -15 -10 -5 0 5 10
P
VM
TABLE A-8.12: Participation Index and quality of active teachers (VQR 2004-2010 grade, measured by the mean grade VM), per Disciplinary group, Macro-group, University and Geographic Area
VM represents the ETP-weighted average of the mean VQR 2004-2010 grades obtained by teachers who are active in the courses given in the Universities of the Geographic Area.
Source: See TABLES 2.1 to 2.5 for the P values. See TABLE A-6.3.1 for R12 values.
Correlation = 0.38
Correlation = 0.50
224
Disciplinary group
Macro-group
TABLE A-8.13: Participation Index and Merit Index (M) calculated for all eligible students, per Disciplinary group, Macro-group, University and Geographic Area
Geographic Area
agr.al
arch (*)
art
bio (*)
chim
comuncult
econfarm
filo (+)(*)
form
geo giu (+)(*)
ing (*)
lett (*)
ling
mat.fis.stat (+)(*)
med (+)(*)odon (+)(*)
polit (+)(*)
psic (+)(*)
socsto (+)(*)
terr
vet (+)(*)
ITA12
-15
-10
-5
0
5
10
15
20
25
-15 -10 -5 0 5 10
P
M
SAN (+)(*)
SC
SOC (+)(*)H
ITA12
-6
-4
-2
0
2
4
6
8
10
-8 -6 -4 -2 0 2 4
P
M
NORD (+)(*)
CENTROSUD
CENTRO-NORD (+)(*)
CENTRO-SUD
ITA12
-6
-4
-2
0
2
4
6
8
10
-4 -2 0 2 4 6
P
M
University
PO
MI (+)(*)PD (+)(*)
UD (+)(*)
BO (+)(*)
FI (+)(*)
RM1
RM2
NA
LE
ME
CAITA12
-20
-10
0
10
20
30
40
-10 -5 0 5 10
P
M
Source: See TABLE 2.1 for the P values. See TABLE A-6.3.5 for M values. Source: See TABLE 2.3 for the P values. See TABLE A-6.3.9 for M values.
Source: See TABLE 2.2 for the P values. See TABLE A-6.3.7 for M values. Source: See TABLE 2.4 for the P values. See TABLE A-6.3.11 for M values.
TABLE A-8.14: Mean grade in university exams sat so far (VME) for eligible and pre-registered students, per Disciplinary group, Macro-group, University and Geographic Area,
broken down by GenderDisciplinary group F+M F M Macro-group F+M F M
agr.al 26.51 26.77 26.32 SAN 27.15 27.18 27.08arch 27.01 27.01 26.99 SC 26.18 26.29 26.07art 27.24 27.23 27.26 SOC 26.11 26.23 25.89bio 26.11 26.14 26.07 H 27.60 27.52 27.87
chim 25.73 25.61 25.86 ITA12 26.55 26.68 26.36comun 25.80 25.95 25.50
cult 27.67 27.58 28.00 University F+M F Mecon 25.34 25.47 25.21 PO 26.05 26.27 25.47farm 26.30 26.44 25.93 MI 26.66 26.71 26.58filo 28.72 28.89 28.44 PD 26.56 26.64 26.42
form 27.15 27.14 27.49 UD 26.08 25.97 26.29geo 26.04 26.09 25.94 BO 27.21 27.31 27.05giu 26.42 26.43 26.39 FI 26.62 26.74 26.43ing 25.41 25.27 25.47 RM1 26.41 26.60 26.11lett 27.88 27.85 27.96 RM2 26.52 26.86 26.15ling 27.10 27.02 27.52 NA 26.68 26.71 26.64
mat.fis.stat 26.26 25.96 26.41 LE 26.88 27.20 26.34med 27.72 27.82 27.59 ME 26.79 26.78 26.82odon 27.37 27.49 27.27 CA 27.13 27.09 27.20polit 26.16 26.25 26.06 ITA12 26.55 26.68 26.36psic 26.33 26.43 25.77soc 26.47 26.54 25.82 Geographic Area F+M F Msto 28.24 28.36 28.17 NORTH 26.46 26.52 26.36terr 26.36 26.56 26.11 CENTRE 26.53 26.72 26.26vet 27.00 27.15 26.63 SOUTH 26.78 26.83 26.69
ITA12 26.55 26.68 26.36 CENTRE-NORTH 26.60 26.67 26.47CENTRE-SOUTH 26.52 26.68 26.28
Source: See TABLE 3 ITA12 26.55 26.68 26.36
225
TABLE 9.3: Overview of the correlation between TECO results and formulation of expectations in the ‘SUA’ forms for academic year 2012-2013 (*)
#TECO
%TECO
A B C A B C
SUA
A 31 131 25
SUA
A 10.00 42.26 8.06
B 13 69 27 B 4.19 22.26 8.71
C 1 10 3 C 0.32 3.23 0.97
Total percentage consistent evaluations: 33.23
Total percentage evaluations that differ by one level: 58.39
Total percentage evaluations that differ by two levels: 8.39
(*): Cases in which one of the values is “null” or the SUA evaluation is not univocal (e.g. “B/C”) are not taken in consideration
Source: See TABLE 9.2
TECO Score
Acquired competences are deemed adequate for TECO
TotalYES NO
# % # %3 1 0.02 1 0.09 24 2 0.04 0 0.00 25 2 0.04 1 0.09 36 1 0.02 1 0.09 27 7 0.15 1 0.09 88 6 0.13 2 0.18 89 23 0.49 3 0.26 26
10 32 0.68 6 0.53 3811 26 0.56 4 0.35 3012 48 1.03 10 0.88 5813 72 1.54 12 1.06 8414 112 2.39 17 1.50 12915 141 3.01 38 3.34 17916 196 4.19 41 3.61 23717 248 5.30 47 4.13 29518 292 6.24 71 6.24 36319 322 6.88 69 6.07 39120 373 7.97 92 8.09 46521 396 8.46 118 10.38 51422 407 8.70 117 10.29 52423 387 8.27 111 9.76 49824 355 7.59 94 8.27 44925 335 7.16 77 6.77 41226 272 5.81 56 4.93 32827 205 4.38 51 4.49 25628 152 3.25 35 3.08 18729 97 2.07 26 2.29 12330 65 1.39 14 1.23 7931 44 0.94 12 1.06 5632 32 0.68 6 0.53 3833 17 0.36 2 0.18 1934 10 0.21 1 0.09 1135 1 0.02 0 0.00 136 1 0.02 1 0.09 237 0 0.00 0 0.00 038 0 0.00 0 0.00 0
Column Total
4680 100.00 1137 100.00 5817
(*): 36 students did not answer the question
0
20
40
60
80
100
120
140
3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37
# st
ud
en
ts
TECO score
NO
0
50
100
150
200
250
300
350
400
450
3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37
# st
ud
en
ts
TECO score
YES
0
2
4
6
8
10
12
3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38
% s
tud
en
ts
TECO score
SI NO
TABLE 9.4: Frequency distribution of TECO scores broken down by students’ self-assessment (*) of adequacy for TECO of the competences acquired at University
YES
226
TABLE 9.5: Students’ self-assessment of adequacy of the competences acquired at University and their TECO results, per Disciplinary group
Disciplinary group
Acquired competences are deemed adequate
Acquired competences are deemed inadequate
Totals TECO NO/YES
YES % YES TECO NO % NO TECO # YES+NO TECO
agr.al 112 82.35 981.46 24 17.65 1011.83 136 1009.73 103.09
arch 209 76.84 1012.78 63 23.16 983.25 272 984.01 97.08
art 39 60.94 972.54 25 39.06 953.32 64 1005.94 98.02
bio 203 80.24 1001.31 50 19.76 1026.54 253 965.03 102.52
chim 90 85.71 1005.61 15 14.29 926.13 105 1006.43 92.10
comun 113 86.26 976.42 18 13.74 986.56 131 995.45 101.04
cult 103 73.05 981.21 38 26.95 969.05 141 977.81 98.76
econ 406 87.31 990.28 59 12.69 997.10 465 977.99 100.69
farm 322 81.93 967.90 (**) 71 18.07 1011.52 393 991.15 104.51
filo 101 93.52 1017.97 7 6.48 1021.57 108 975.45 100.35
form 94 74.60 908.78 32 25.40 894.13 126 1018.20 98.39
geo 40 75.47 915.58 13 24.53 990.54 53 903.28 108.19
giu 654 75.43 1008.52 213 24.57 1012.58 867 933.96 100.40
ing 413 89.20 999.27 50 10.80 1017.14 463 1001.20 101.79
lett 151 80.75 1022.71 36 19.25 983.67 187 1012.51 96.18
ling 153 66.81 979.84 76 33.19 988.91 229 985.38 100.93
mat.fis.stat 363 93.80 1043.17 24 6.20 1010.50 387 1041.43 96.87
med 282 72.87 1072.92 105 27.13 1079.16 387 1072.25 100.58
odon 29 65.91 1026.83 15 34.09 993.00 44 1015.30 96.71
polit 166 84.69 1002.72 30 15.31 1035.90 196 1006.18 103.31
psic 149 78.42 1023.13 41 21.58 1053.29 190 1029.75 102.95
soc 66 83.54 941.17 13 16.46 1043.69 79 958.04 110.89
sto 40 70.18 1010.15 17 29.82 1013.06 57 1011.02 100.29
terr 306 78.26 929.22 85 21.74 959.06 391 935.70 103.21
vet 76 81.72 997.11 17 18.28 1035.41 93 1004.11 103.84
ITA12 4680 80.45 998.39 1137 19.55 1005.18 5817 999.53 100.68
YES and NO percentages are calculated on the row totals
Grey shading indicates the highest TECO value among YES and NO
(**) TECO means are significantly different (95% confidence interval), if the number of observations is higher or equal to 30
On the total number of 5853 students who participated to the TECO pilot test, 5817 answered the question on whether they deem adequate for TECO the competences they have acquired at University
TECO NO/YES is the ratio between TECO for students who deem their acquired competences as inadequate versus adequate x 100
Source: See TABLE 3.
TABLE 9.6: Students’ self-assessment of adequacy of the competences acquired at University and their TECO results, per Macro-group (Part 1)
Macro-group
Acquired competences are deemed adequate
Acquired competences are deemed inadequate
TotalsTECO NO/YES
# YES % YES TECO # NO % NO TECO # YES+NO TECO
SAN 709 77.321015.21
(**)208 22.68 1046.28 917 1021.40 103.06
SC 1701 84.50 996.85 312 15.50 989.90 2013 995.74 99.30
SOC 1580 80.37 998.18 (**) 386 19.63 1015.26 1966 1001.54 101.71
H 690 74.92 985.34 231 25.08 971.97 921 981.77 98.64
ITA12 4680 80.45 998.39 1137 19.55 1005.18 5817 999.53 100.68
YES and NO percentages are calculated on the row totals
Grey shading indicates the highest TECO value among YES and NO
(**) TECO means are significantly different (95% confidence interval)
On the total number of 5853 students who participated to the TECO pilot test, 5817 answered the question on whether they deem adequate for TECO the competences they have acquired at University
TECO NO/YES is the ratio between TECO for students who deem their acquired competences as inadequate versus adequate x 100
Source: See TABLE 3. See Part 2 for graphs
227
SAN (+)(*)
SC
SOC (+)(*)
H
ITA12
-2,5
-2
-1,5
-1
-0,5
0
0,5
1
1,5
2
2,5
3
-6 -4 -2 0 2 4 6 8
TECO NO/YES
% NO
SAN (+)(*)
SCSOC (+)(*)
H
ITA12
-15
-10
-5
0
5
10
15
20
-8 -6 -4 -2 0 2 4 6
TECO
% YES
950
970
990
1010
1030
1050
1070
SAN SC SOC H ITA12
TEC
O
SI NO
Students’ self-assessment of adequacy of the competences acquired at University and their TECO results, per Macro-group (Part 2)
YES80%
NO20%
Competences deemed adequate
Source: See Part 1
0%
20%
40%
60%
80%
100%
SAN SC SOC H ITA12
SI NO
Percentages in students’ declarations TECO results
YESYES
TABLE 9.7: Students’ self-assessment of adequacy of the competences acquired at University and their TECO results, per University (Part 1)
University
Acquired competences are deemed adequate
Acquired competences are deemed inadequate
Totals TECO NO/YES
# YES % YES TECO # NO % NO TECO # YES+NO TECO
PO 263 84.29 992.70 49 15.71 1002.82 312 994.92 101.04
MI 627 79.77 1033.08 159 20.23 1043.91 786 1034.53 101.06
PD 441 80.33 1023.83 108 19.67 1032.3 549 1025.49 100.78
UD 229 80.07 1019.88 57 19.93 1041.49 286 1024.28 102.06
BO 290 79.23 1052.27 76 20.77 1027.09 366 1047.73 97.62
FI 554 80.17 1023.20 137 19.83 1030.83 691 1024.71 100.78
RM1 1317 79.53 976.11 339 20.47 976.19 1656 976.04 100.01
RM2 154 85.08 982.59 27 14.92 963.41 181 981.22 98.05
NA 477 82.38 957.97 (**) 102 17.62 1001.48 579 965.43 104.49
LE 130 85.53 941.95 22 14.47 956.91 152 939.92 101.58
ME 112 86.15 927.61 18 13.85 926.72 130 925.78 99.90
CA86 66.67
1004.62 (**) 43 33.33 949.07 129 986.10 94.44
ITA12 4680 80.45 998.39 1137 19.55 1005.18 5817 999.53 100.66
YES and NO percentages are calculated on the row totals
Grey shading indicates the highest TECO value among YES and NO
(**) TECO means are significantly different (95% confidence interval), if the number of observations is higher or equal to 30
On the total number of 5853 students who participated to the TECO pilot test, 5817 answered the question on whether they deem adequate for TECO the competences they have acquired at University
TECO NO/YES is the ratio between TECO for students who deem their acquired competences as inadequate versus adequate x 100
Source: See TABLE 3. See Part 2 for graphs
228
PO
MI (+)(*)
PD (+)(*)
UD (+)(*)
BO (+)(*)
FI (+)(*)
RM1RM2
NA
LE
ME
CA
ITA12
-80
-60
-40
-20
0
20
40
60
-15 -10 -5 0 5 10
TECO
% YESPO MI (+)(*)PD (+)(*)
UD (+)(*)
BO (+)(*)
FI (+)(*)
RM1
RM2
NA
LE
ME
CA
ITA12
-8
-6
-4
-2
0
2
4
6
-10 -5 0 5 10 15
TECO NO/YES
% NO
TABLE 9.7: Students’ self-assessment of adequacy of the competences acquired at University and their TECO results, per University (Part 2)
850
900
950
1000
1050
1100
TEC
O
SI NO
YES80%
NO20%
Competences deemed adequate
Source: See Part 1
Percentages in students’ declarations TECO results
0%
20%
40%
60%
80%
100%
PO MI
PD
UD
BO FI
RM
1
RM
2
NA LE ME
CA
ITA
12
SI NO YESYES
TABLE 9.8: Students’ self-assessment of adequacy of the competences acquired at University and their TECO results, per University , per Geographic Area (Part 1)
Geographic Area
Acquired competences are deemed adequate
Acquired competences are deemed inadequate
Totals TECO NO/YES
# YES % YES TECO # NO % NO TECO # YES+NO TECO
NORTH 1560 79.92 1021.72 392 20.08 1032.31 1952 1024.24 101.04
CENTRE 2315 79.99 997.35 579 20.01 995.20 2894 996.92 99.78
SOUTH 805 81.31 956.15 185 18.69 976.72 990 959.99 102.15
CENTRE-NORTH 2404 80.40 1025.75 586 19.60 1032.86 2990 1027.14 100.69
CENTRE-SOUTH 2276 80.51 969.49 551 19.49 975.74 2827 970.70 100.64
ITA12 4680 80.45 998.39 1137 19.55 1005.18 5817 999.71 100.68
YES and NO percentages are calculated on the row totals
Grey shading indicates the highest TECO value among YES and NO
On the total number of 5853 students who participated to the TECO pilot test, 5817 answered the question on whether they deem adequate for TECO the competences they have acquired at University
TECO NO/YES is the ratio between TECO for students who deem their acquired competences as inadequate versus adequate x 100
Source: See TABLE 3. See Part 2 for graphs
229
NORTH (+)(*)
CENTRE
SUD
CENTRE-NORTH (+)(*)
CENTRE-SOUTH
ITA12
-50
-40
-30
-20
-10
0
10
20
30
40
-1 -0,5 0 0,5 1
TECO
% YES NORTH (+)(*)
CENTRE
SOUTH
CENTRE-NORTH (+)(*)
CENTRE-SOUTH
ITA12
-1,5
-1
-0,5
0
0,5
1
1,5
2
-1 -0,5 0 0,5 1
TECO NO/YES
% NO
900
920
940
960
980
1000
1020
1040
1060
TEC
O
SI NO
TABLE 9.8: Students’ self-assessment of adequacy of the competences acquired at University and their TECO results, per Geographic Area (Part 2)
YES80%
NO20%
Competences deemed adequate
Source: See Part 1
0%
20%
40%
60%
80%
100%
SI NO
Percentages in students’ declarations TECO results
YESYES
Macro-group
Quartile TECO T
NO (competencesd
eemed inadequate)
YES (competencesd
eemed adequate)
no answer
T % T % T %
SAN
1 809.26 238 45 18.91 190 79.83 3 1.26
2 977.84 227 44 19.38 181 79.74 2 0.88
3 1083.74 233 60 25.75 171 73.39 2 0.86
4 1224.31 226 59 26.11 167 73.89 0 0.00
total 1021.40 924 208 22.51 709 76.73 7 0.76
SC
1 793.42 513 71 13.84 441 85.96 1 0.19
2 947.14 499 93 18.64 404 80.96 2 0.40
3 1050.67 508 90 17.72 415 81.69 3 0.59
4 1195.61 501 58 11.58 441 88.02 2 0.40
total 995.74 2021 312 15.44 1701 84.17 8 0.40
SOC
1 796.29 501 81 16.17 416 83.03 4 0.80
2 963.59 513 100 19.49 411 80.12 2 0.39
3 1064.45 497 116 23.34 377 75.86 4 0.80
4 1196.03 468 89 19.02 376 80.34 3 0.64
total 1001.54 1979 386 19.50 1580 79.84 13 0.66
H
1 785.78 237 62 26.16 171 72.15 4 1.69
2 940.74 238 58 24.37 180 75.63 0.00
3 1036.93 226 60 26.55 164 72.57 2 0.88
4 1173.66 228 51 22.37 175 76.75 2 0.88
total 981.77 929 231 24.87 690 74.27 8 0.86
The quartiles are calculated within the relevant Macro-group.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1 2 3 4 totale
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1 2 3 4 totale
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1 2 3 4 totale
senza risposta
SI (competenzepercepiteadeguate)
NO(competenzepercepite nonadeguate)
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1 2 3 4 totale
TABLE 9.10: Proportion of negative and positive answers to the question on adequacy of the competences acquired at University in the quartiles of distribution of TECO results, per Macro-group
SAN
SC
SOC
H
quartile quartile
230
Macro-group Quartile
Student’s assessment of adequacy for TECO of the competences acquired at
University
NO YES
T TECO T TECO
SAN
1 53 844.62(**) 178 798.42
2 54 1006.00(**) 182 970.70
3 50 1110.90(**) 173 1076.11
4 51 1235.16 176 1220.63
total 208 1046.28(**) 709 1015.21
SC
1 82 808.77 441 793.74
2 74 946.81 412 948.98
3 80 1035.73(**) 427 1053.92
4 76 1179.04 421 1198.58
total 312 989.90 1701 996.85
SOC
1 101 834.57(**) 398 787.92
2 96 987.74(**) 401 955.59
3 93 1060.20 405 1063.09
4 96 1189.32 376 1196.26
total 386 1015.26(**) 1580 998.18
H
1 62 772.73 182 796.34
2 58 947.53 169 942.36
3 55 1033.60 178 1040.72
4 56 1157.34(**) 161 1182.89
total 231 971.97 690 985.34
Quartiles are calculated on the set of students who gave the same answer, within the Macro-group
(**): TECO means significantly different (95% confidence interval) between students who perceive skills acquired during university studies as relevant for TECO and those who do not.
700
800
900
1000
1100
1200
1300
1 2 3 4 totale
quartile
Student’s assessment of the adequacy of competences acquired
at University
NO SI
700
800
900
1000
1100
1200
1300
1 2 3 4 totalequartile
700
800
900
1000
1100
1200
1300
1 2 3 4 totalequartile
700
800
900
1000
1100
1200
1300
1 2 3 4 totale
quartile
SAN
SC
SOC
H
TABLE 9.11: Distributions by TECO quartiles based on students’ self-assessment of adequacy of the competences acquired at University, per Macro-group
TEC
OTE
CO
YES
0%
20%
40%
60%
80%
100%
1 2 3 4 totale
0%
20%
40%
60%
80%
100%
1 2 3 4 totale
0%
20%
40%
60%
80%
100%
1 2 3 4 totale
TABLE 9.12: Proportion of negative and positive answers to the question on adequacy of competences in the quartiles of
distribution of TECO results, per Geographic Area
Geographic Area
Quartile TECO T
NO (competences
deemed inadequate)
YES (competences
deemed adequate)
no answer
T % T % T %
NORTH
1 825.10 500 76 15.20 420 84.00 4 0.80
2 982.99 485 97 20.00 383 78.97 5 1.03
3 1081.26 482 115 23.86 359 74.48 8 1.66
4 1212.83 486 85 17.49 398 81.89 3 0.62
total 1024.01 1953 373 19.10 1560 79.88 20 1.02
CENTRE
1 793.46 737 142 19.27 594 80.60 1 0.14
2 955.39 737 160 21.71 577 78.29 0 0.00
3 1052.22 708 150 21.19 556 78.53 2 0.28
4 1194.75 717 127 17.71 588 82.01 2 0.28
total 997.07 2899 579 19.97 2315 79.86 5 0.17
SOUTH
1 754.00 258 36 13.95 217 84.11 5 1.94
2 913.72 246 48 19.51 195 79.27 3 1.22
3 1013.97 248 55 22.18 192 77.42 1 0.40
4 1161.02 249 46 18.47 201 80.72 2 0.80
total 958.90 1001 185 18.48 805 80.42 11 1.10
CENTRE-NORTH
1 828.10 758 127 16.75 627 82.72 4 0.53
2 984.46 760 159 20.92 596 78.42 5 0.66
3 1084.37 780 170 21.79 600 76.92 10 1.28
4 1221.07 714 130 18.21 581 81.37 3 0.42
total 1027.07 3012 586 19.46 2404 79.81 22 0.73
CENTRE-SOUTH
1 767.45 734 123 16.76 604 82.29 7 0.95
2 931.78 757 153 20.21 601 79.39 3 0.40
3 1031.18 659 155 23.52 503 76.33 1 0.15
4 1170.07 691 120 17.37 568 82.20 3 0.43
total 970.34 2841 551 19.39 2276 80.11 14 0.49
The quartiles are calculated within the relevant Geographic Area.
NORTH
CENTRE
SOUTH
0%
20%
40%
60%
80%
100%
1 2 3 4 totale
senza risposta
SI (competenze percepiteadeguate)
NO (competenze percepite nonadeguate)
0%
20%
40%
60%
80%
100%
1 2 3 4 totale
CENTRE-NORTH
CENTRE-SOUTH
quartile quartile
No answer
YES
NO
231
Geographic Area
Quartile
Student’s assessment of adequacy for TECO of the competences acquired at
University
NO YES
T TECO T TECO
NORTH
1 100 854.13(**) 407 822.81
2 92 1006.53(**) 383 979.51
3 96 1088.70(**) 397 1082.12
4 85 1216.99 373 1217.82
total 373 1034.78 1560 1021.72
CENTRE
1 145 796.74 579 790.81
2 145 956.30 592 952.38
3 146 1042.58(**) 573 1054.67
4 143 1187.51 571 1195.88
total 579 995.20 2315 997.35
SOUTH
1 47 789.49(**) 208 749.63
2 47 939.70(**) 204 910.53
3 45 1026.53(**) 192 1012.87
4 46 1157.13 201 1161.96
total 185 976.72 805 956.15
Quartiles are calculated on the set of students who gave the same answer, within the Geographic Area
(**): TECO means significantly different (95% confidence interval) between students who perceive skills acquired during university studies as relevant for TECO and those who do not.
700
800
900
1000
1100
1200
1300
1 2 3 4 totale
risu
ltat
o s
u T
ECO
quartile
Student’s perception on whether skills acquired during studies are relevant for the test
NO SI
700
800
900
1000
1100
1200
1300
1 2 3 4 totale
risu
ltat
o s
u T
ECO
quartile
700
800
900
1000
1100
1200
1300
1 2 3 4 totale
risu
ltat
o s
u T
ECO
quartile
NORTH
CENTRE
SOUTH
TABLE 9.13: Distributions by TECO quartiles based on students’ self-assessment of adequacy of the competences acquired at University, per Geographic Area (Part 1)
YES
Geographic Area
Quartile
Student’s assessment of adequacy for TECO of the competences acquired at
University
NO YES
T TECO T TECO
CENTRE-NORTH
1 149 842.91(**) 606 825.99
2 148 997.97(**) 602 980.71
3 153 1083.41 615 1083.60
4 136 1222.06 581 1219.52
total 586 1032.86 2404 1025.75
CENTRE-SOUTH
1 140 784.72(**) 569 760.16
2 136 939.87(**) 574 922.48
3 138 1022.86 565 1026.19
4 137 1159.09 568 1170.27
total 551 975.74 2276 969.49
Quartiles are calculated on the set of students who gave the same answer, within the Geographic Area
(**): TECO means significantly different (95% confidence interval) between students who perceive skills acquired during university studies as relevant for TECO and those who do not.
700
800
900
1000
1100
1200
1300
1 2 3 4 totale
risu
ltat
o s
u T
ECO
quartile
Student’s perception on whether skills acquired during studies are relevant for the test
NO SI
700
800
900
1000
1100
1200
1300
1 2 3 4 totale
risu
ltat
o s
u T
ECO
quartile
CENTRE-NORTH
CENTRE-SOUTH
TABLE 9.13: Distribution by TECO quartiles by student’s perception of whether skills acquired during university studies are relevant for the TECO test, per
Geographic Area (Part 2)
YES
232
NORTH CENTRE SOUTH CENTRE-NORTH CENTRE-SOUTH ITA12
Competences deemed
adequate
Course with admission
test
Competences deemed
adequate
Course with admission
test
Competences deemed adequate
Course with admission
test
Competences deemed
adequate
Course with admission
test
Competences deemed
adequate
Course with admission
test
Competences deemed
adequate
Course with admission
test
Disciplinary group
NO/YES
T TECO T TECO T TECO T TECO T TECO T TECO T TECO T TECO T TECO T TECO T TECO T TECO
agr.alNO 14 990.71 71 975.35 6 1075.00 36 1019.56 4 991.00 16 885.94 20 1016.00 107 990.22 4 991.00 16 885.94 24 1011.83 123 976.66
YES 70 992.26 16 1040.50 30 1008.47 12 850.92 100 997.12 16 1040.50 12 850.92 112 981.46 16 1040.50
archNO 6 1075.83 51 980.00 6 918.33 18 1040.83 45 960.22 63 983.25YES 32 1067.44 38 1068.76 161 1006.22 212 999.91 16 969.50 22 955.55 84 1047.07 102 1045.97 125 989.74 170 981.92 209 1012.78 272 1005.94
artNO 6 856.33 16 950.75 19 983.95 28 1025.57 10 944.60 26 998.81 15 959.13 18 997.72 25 953.32 44 998.36
YES 10 1007.40 29 960.52 20 891.70 16 1032.69 23 930.70 20 891.70 39 972.54 20 891.70
bioNO 25 1023.64 15 994.33 20 1046.50 5 961.20 1 1155.00 36 1022.89 15 994.33 14 1035.93 1 1155.00 50 1026.54 16 1004.38
YES 114 1029.82 127 1032.51 60 961.82 80 982.99 29 970.93 33 963.88 133 1030.26 157 1031.76 70 946.30 83 958.90 203 1001.31 240 1006.57
chimNO 4 1026.50 11 961.64 10 852.70 49 959.20 1 1259.00 1 881.00 5 1018.00 13 983.23 10 880.20 48 951.63 15 926.13 61 958.36
YES 45 1028.00 39 1048.95 39 986.51 6 961.83 6 1024.83 46 1032.17 39 1048.95 44 977.84 6 1024.83 90 1005.61 45 1045.73
comunNO 4 921.75 36 957.08 11 1001.73 59 970.08 3 1017.33 11 954.27 6 930.00 52 956.52 12 1014.83 54 971.26 18 986.56 106 964.03
YES 55 991.64 23 1033.57 48 962.83 10 957.90 2 1067.00 69 984.51 23 1033.57 44 963.73 2 1067.00 113 976.42 25 1036.24
cultNO 11 1039.36 47 1003.53 20 930.70 65 973.23 7 968.14 24 929.63 13 1052.23 59 1024.03 25 925.80 77 939.21 38 969.05 136 976.01
YES 35 992.77 45 992.13 23 942.26 6 1023.00 45 1016.73 58 953.66 6 1023.00 103 981.21 6 1023.00
econNO 12 1008.75 71 991.28 39 1006.21 137 977.63 8 935.25 33 946.55 33 1014.12 133 1004.10 26 975.50 108 944.51 59 997.10 241 977.39
YES 136 1002.53 77 1013.87 217 989.50 119 1008.64 53 962.08 28 972.71 229 1017.21 129 1029.94 177 955.45 95 973.37 406 990.28 224 1005.95
farmNO 42 1063.48 7 1007.00 24 931.00 8 1081.75 5 961.60 50 1038.14 15 1046.87 21 948.14 71 1011.52 15 1046.87
YES 157 1017.09 193 1026.67 70 974.23 86 952.16 95 881.95 100 885.93 185 1018.53 221 1020.27 137 899.53 158 905.99 322 967.90 379 972.63
filoNO 1 1014.00 42 1030.86 4 1069.50 55 1023.00 2 929.50 11 945.91 3 1039.33 68 1025.26 4 1008.25 40 1006.20 7 1021.57 108 1018.20
YES 41 1031.27 51 1019.35 9 949.56 65 1024.62 36 1005.97 101 1017.97
formNO 10 930.70 48 932.79 14 886.71 31 901.45 8 861.38 19 869.95 22 907.36 79 920.49 10 865.00 19 869.95 32 894.13 98 910.69
YES 38 933.34 39 899.44 22 888.50 17 875.29 8 853.13 58 928.91 1 1120.00 36 876.33 29 870.76 94 908.78 30 879.07
geoNO 8 1030.00 30 936.27 5 927.40 21 942.62 9 1047.78 33 942.55 4 861.75 18 932.17 13 990.54 51 938.88
YES 22 902.18 16 947.38 2 808.50 2 808.50 24 903.08 16 934.31 2 808.50 40 915.58 2 808.50The dark grey shading indicates Disciplinary groups for which there is a national admission test or a local admission test for 100% of eligible students within the Geographic Area and ITA12.The light grey shading shows Disciplinary groups for which there is a local admission test for more than 50% of the eligible students within the Geographic Area and ITA12.The yellow shading indicates Disciplinary groups for which there is a local admission test for more than 50% of the eligible students within the Geographic Area and ITA12, and for which the TECO result is better for eligible students studying in a course without admission test, even though there is a greater number of eligible students who perceive their acquired competences as adequate for TECO.On the total number of 5853 students who participated to the TECO pilot test, 5817 answered the question on adequacy for TECO of the comptences acquired at University
TABLE 9.14: Students’ self-assessment of adequacy of the competences acquired at University and their TECO results, attendance in courses with an admission test, and these students’ TECO results, per Disciplinary group and per Geographic Area (Part 1)
NORTH CENTRE SOUTH CENTRE-NORTH CENTRE-SOUTH ITA12
Competences deemed
adequate
Course with admission
test
Competences deemed
adequate
Course with admission
test
Competences deemed adequate
Course with admission
test
Competences deemed
adequate
Course with admission
test
Competences deemed
adequate
Course with admission
test
Competences deemed
adequate
Course with admission
testDisciplinary
groupNO/YE
ST TECO T TECO T TECO T TECO T TECO T TECO T TECO T TECO T TECO T TECO T TECO T TECO
giuNO 91 1038.66 376 1036.65 78 1014.60 297 1007.82 44 955.07 201 962.21 133 1051.05 550 1038.83 80 948.64 324 960.34 213 1012.58 874 1009.73YES 281 1035.04 217 1005.80 156 964.53 413 1034.27 241 964.39 654 1008.52
ingNO 6 1022.83 51 992.18 41 1020.39 151 1036.30 3 961.33 70 985.14 10 1035.00 125 1029.33 40 1012.68 147 1002.56 50 1017.14 272 1014.86YES 45 988.09 276 1008.75 166 986.57 92 976.28 25 949.68 115 1028.83 298 987.86 191 981.74 413 999.27 191 981.74
lettNO 20 1001.05 78 1030.04 14 966.29 91 1002.91 2 931.50 21 989.00 23 1001.87 103 1036.98 13 951.46 87 983.54 36 983.67 190 1012.51YES 56 1043.29 77 1009.57 18 1014.89 78 1049.59 73 993.99 151 1022.71
lingNO 29 1039.52 83 1016.93 30 974.63 27 937.96 17 927.76 40 982.60 33 1027.36 90 1012.37 43 959.40 60 965.35 76 988.91 150 993.56YES 61 979.79 8 858.50 67 970.01 70 984.36 25 1006.32 3 938.67 67 980.15 11 892.73 86 979.60 70 982.41 153 979.84 81 970.23
mat.fis.statNO 7 1002.14 72 1057.67 17 1013.94 294 1038.01 17 1008.65 11 1045.55 146 1069.96 13 980.85 237 1022.19 24 1010.50 383 1040.40YES 66 1066.61 1 1259.00 276 1039.09 21 1023.19 4 1085.00 136 1073.32 1 1259.00 227 1025.11 4 1085.00 363 1043.17 5 1119.80
medNO 16 1112.38 46 1071.63 43 1074.86 41 1097.51 64 1067.41 105 1079.16YES 85 1106.65 103 1105.49 120 1066.32 166 1067.79 77 1045.97 124 1050.61 141 1096.67 184 1095.82 141 1049.16 209 1051.50 282 1072.92 393 1072.25
odonNO 5 1025.80 4 949.50 6 994.67 5 1025.80 10 976.60 15 993.00YES 8 1125.88 13 1087.38 6 976.33 10 965.60 15 994.20 21 994.33 9 1117.89 14 1085.00 20 985.85 30 982.77 29 1026.83 44 1015.30
politNO 7 1140.14 76 1028.92 19 1015.89 83 1022.01 4 948.50 42 933.74 14 1066.71 109 1028.31 16 1008.94 92 979.96 30 1035.90 201 1006.18YES 67 1021.28 63 1019.54 36 938.75 92 1022.47 74 978.18 166 1002.72
psicNO 17 1060.59 19 1070.42 5 963.40 25 1060.68 16 1041.75 41 1053.29YES 37 1038.97 54 1045.78 91 1019.13 111 1028.21 21 1012.52 26 1003.08 78 1031.14 104 1038.44 71 1014.32 87 1019.37 149 1023.13 191 1029.75
socNO 1 1117.00 11 975.00 9 1075.11 29 977.62 3 925.00 18 858.33 9 1070.78 27 1014.07 4 982.75 31 875.68 13 1043.69 58 940.10YES 17 966.00 7 973.43 28 974.79 8 1077.38 21 876.24 6 954.33 29 1002.48 11 1029.91 37 893.11 10 983.00 66 941.17 21 1007.57
stoNO 9 1097.44 21 1074.90 8 918.13 29 970.52 7 987.14 14 1026.79 33 1023.48 3 949.00 24 993.88 17 1013.06 57 1011.02YES 12 1058.00 21 990.48 7 987.14 19 1021.05 21 1000.29 40 1010.15
terrNO 12 963.17 25 975.92 66 958.14 120 968.74 7 960.71 25 838.20 28 977.36 99 993.28 57 950.07 71 891.08 85 959.06 170 950.60YES 32 998.16 19 1005.32 245 927.42 191 912.08 29 868.28 11 995.45 112 985.30 41 960.61 194 896.84 180 915.96 306 929.22 221 924.24
vetNO 10 1103.40 5 1024.40 2 723.00 15 1077.07 2 723.00 17 1035.41YES 38 1019.11 48 1036.67 23 1055.48 28 1049.93 15 851.87 17 836.71 61 1032.82 76 1041.55 15 851.87 17 836.71 76 997.11 93 1004.11
Total Geographic
Areas
NO 373 1034.78 1187 1012.92 579 995.20 1610 1002.89 185 976.72 557 950.28 586 1032.86 1882 1019.29 551 975.74 1472 970.10 1137 1005.18 3354 997.70
YES 1560 1021.72 766 1041.20 2315 997.35 1289 989.80 805 956.15 444 969.72 2404 1025.75 1130 1040.03 2276 969.49 1369 970.59 4680 998.39 2499 1001.99
The dark grey shading indicates Disciplinary groups for which there is a national admission test or a local admission test for 100% of eligible students within the Geographic Area and ITA12.The light grey shading shows Disciplinary groups for which there is a local admission test for more than 50% of the eligible students within the Geographic Area and ITA12.The yellow shading indicates Disciplinary groups for which there is a local admission test for more than 50% of the eligible students within the Geographic Area and ITA12, and for which the TECO result is better for eligible students studying in a course without admission test, even though there is a greater number of eligible students who perceive their acquired skills as relevant for TECO.On the total number of 5853 students who participated to the TECO pilot test, 5817 answered the question on adequacy for TECO of the comptences acquired at University
TABLE 9.14: Students’ self-assessment of adequacy of the competences acquired at University and their TECO results, attendance in courses with an admission test, and these students’ TECO results, per Disciplinary group and per Geographic Area (Part 2)
233
TABLE 9.15: Attendance reported as regular by tested students and these students’ TECO results, per Disciplinary group (Part 1)
Disciplinary group
Regular attendance = yes Regular attendance = no Totals TECO NO/YES# YES % YES TECO # NO % NO TECO # YES + NO TECO
agr.al 129 94.85 996.34 7 5.15 811.29 136 984.01 81.43
arch 272 100.00 1005.94 0 0.00 272 1005.94 0.00
art 59 92.19 967.03 5 7.81 941.40 64 965.03 97.35
bio 248 98.02 1004.75 5 1.98 1082.80 253 1006.43 107.77
chim 102 97.14 995.93 3 2.86 937.33 105 995.45 94.12
comun 118 90.08 971.46 13 9.92 1035.46 131 977.81 106.59
cult 129 91.49 974.25 12 8.51 1017.58 141 977.99 104.45
econ 446 95.91 989.92 19 4.09 1020.00 465 991.15 103.04
farm 390 99.24 975.46 3 0.76 1017.00 393 975.45 104.26
filo 100 92.59 1027.50 8 7.41 902.00 108 1018.20 87.79
form 118 93.65 902.77 8 6.35 938.75 126 903.28 103.99
geo 47 88.68 933.57 6 11.32 937.00 53 933.96 100.37
giu 756 87.20 1009.46 111 12.80 1009.87 867 1009.73 100.04
ing 451 97.41 1001.35 12 2.59 995.33 463 1001.20 99.40
lett 179 95.72 1015.11 8 4.28 1017.00 187 1012.51 100.19
ling 217 94.76 985.39 12 5.24 937.00 229 985.38 95.09
mat.fis.stat 379 97.93 1041.01 8 2.07 1047.38 387 1041.43 100.61
med 379 97.93 1073.33 8 2.07 1135.25 387 1072.25 105.77
odon 44 100.00 1015.30 0 0.00 44 1015.30 0.00
polit 179 91.33 1012.61 17 8.67 957.12 196 1006.18 94.52
psic 178 93.68 1026.72 12 6.32 1072.83 190 1029.75 104.49
soc 73 92.41 969.30 6 7.59 821.00 79 958.04 84.70
sto 53 92.98 1008.49 4 7.02 1044.50 57 1011.02 103.57
terr 382 97.70 934.50 9 2.30 986.67 391 935.70 105.58
vet 93 100.00 1004.11 0 0.00 93 1004.11 0.00
ITA12 5521 94.91 999.87 296 5.09 996.87 5817 999.53 99.70
YES and NO percentages are calculated on the row totals
Grey shading indicates the highest TECO value among YES and NOOf the total 5853 tested students, 36 observations are missing. Comparison between TECO means is not possible because the sample size is lower than 30. In the only possible case (giu) the means are not significantly different (95% confidence level).
TECO NO/YES is the ratio between TECO for students who report irregular attendance versus TECO for those who report regular attendance x 100
Source: See TABLE 3. See Part 2 for graphs
agr.al
arch (*)
art
bio (*)
chim
comuncult
econ
farm
filo (+)(*)
form
geo
giu (+)(*)
ing (*)
lett (*)
ling
mat.fis.stat (+)(*)
med (+)(*)
odon (+)(*)polit (+)(*)
psic (+)(*)
soc
sto (+)(*)
terr
vet (+)(*)ITA12
-100
-80
-60
-40
-20
0
20
40
60
80
-10 -5 0 5 10
TECO
%YES
agr.al
art
bio (*)
chim
comuncult
econfarm
filo (+)(*)
form
geo
giu (+)(*)ing (*)lett (*)
ling
mat.fis.stat (+)(*)
med (+)(*)
polit (+)(*)
psic (+)(*)
soc
sto (+)(*)
terr
ITA12
-20
-15
-10
-5
0
5
10
15
-10 -5 0 5 10
TECO NO/YES
% NO
TABLE 9.15: Attendance reported as regular by tested students and these students’ TECO results, per Disciplinary group (*) (Part 2)
(*): Within the Disciplinary groups odon, arch and vet all students reported regular attendance
750
850
950
1050
1150
TEC
O
SI NO
SI95%
NO5%
Attendance reported as regular
Source: See Part 1
YES
YES
234
TABLE 9.16: Attendance reported as regular by tested students and these students’ TECO results, per Macro-group (Part 1)
Macro-group
Regular attendance = yes Regular attendance = no TotalsTECO
NO/YES # YES % YES TECO # NO % NO TECO # YES +
NOTECO
SAN 906 98.80 1021.28 11 1.20 1103.00 917 1021.40 108
SC 1969 97.81 996.13 44 2.19 979.73 2013 995.74 98.35
SOC 1784 90.74 1001.26 182 9.26 1004.2 1966 1001.54 100.29
H 862 93.59 982.99 59 6.41 967.25 921 981.77 98.40
ITA12 5521 94.91 999.87 296 5.09 996.87 5817 999.53 99.70
YES and NO percentages are calculated on the row totals
Grey shading indicates the highest TECO value among YES and NO
Of the total 5853 tested students, 36 observations are missing. TECO means are not significantly different (95% confidence interval), if the number of observations is higher or equal to 30
TECO NO/YES is the ratio between TECO for students who report irregular attendance versus TECO for those who report regular attendance x 100
Source: See TABLE 3. See Part 2 for graphs
SAN (+)(*)
SC
SOC (+)(*)
H
ITA12
-2
0
2
4
6
8
10
-6 -4 -2 0 2 4 6
TECO NO/YESI
% NO
SAN (+)(*)
SC
SOC (+)(*)
H
ITA12
-20
-15
-10
-5
0
5
10
15
20
25
-6 -4 -2 0 2 4 6
TECO
% YES
850
900
950
1000
1050
1100
1150
SAN SC SOC H ITA12
TEC
O
SI NO
TABLE 9.16: Attendance reported as regular by tested students and these students’ TECO results, per Macro-group (Part 2)
SI95%
NO5%
Attendance reported as regular
Source: See Part 1
YES
YES
235
TABLE 9.17: Attendance reported as regular by tested students and these students’ TECO results, per University (Part 1)
University Regular attendance = yes Regular attendance = no Totals TECO
NO/YES# YES % YES TECO # NO % NO TECO # YES + NO TECO
PO 304 97.44 993.90 8 2.56 1009.13 312 994.92 101.53
MI 728 92.62 1039.24 (**) 58 7.38 985.43 786 1034.53 94.82
PD 537 97.81 1025.99 12 2.19 1003.42 549 1025.49 97.80
UD 275 96.15 1023.84 11 3.85 1032.73 286 1024.28 100.87
BO 349 95.36 1043.52 17 4.64 1119.35 366 1047.73 107.27
FI 654 94.65 1024.11 37 5.35 1035.32 691 1024.71 101.10
RM1 1564 94.44 974.99 92 5.56 995.43 1656 976.04 102.10
RM2 168 92.82 982.34 13 7.18 946.00 181 981.22 96.30
NA 549 94.82 965.44 30 5.18 969.20 579 965.43 100.39
LE 143 94.08 948.11 9 5.92 880.67 152 939.92 92.89
ME 128 98.46 927.98 2 1.54 896.00 130 925.78 96.55
CA 122 94.57 989.93 7 5.43 919.43 129 986.10 92.88
ITA12 5521 94.91 999.87 296 5.09 996.87 5817 999.53 99.70
YES and NO percentages are calculated on the row totals
Grey shading indicates the highest TECO value among YES and NO
(**) TECO means are significantly different (95% confidence interval), if the number of observations is higher or equal to 30
Of the total 5853 tested students, 36 observations are missing.
TECO NO/YES is the ratio between TECO for students who report irregular attendance versus TECO for those who report regular attendance x 100
Source: See TABLE 3. See Part 2 for graphs
PO
MI (+)(*)
PD (+)(*)
UD (+)(*)
BO (+)(*)
FI (+)(*)
RM1
RM2
NA
LE
ME
CA
ITA12
-8
-6
-4
-2
0
2
4
6
8
10
-4 -2 0 2 4
TECO NO/YES
% NOPO
MI (+)(*)
PD (+)(*)UD (+)(*)
BO (+)(*)
FI (+)(*)
RM1
RM2
NA
LE
ME
CA
ITA12
-80
-60
-40
-20
0
20
40
60
80
-3 -2 -1 0 1 2 3 4
TECO
% YES
TABLE 9.17: Attendance reported as regular by tested students and these students’ TECO results, per University (Part 2)
850
900
950
1000
1050
1100
1150
PO MI PD UD BO FI RM1 RM2 NA LE ME CA ITA12
TEC
O
SI NO
SI95%
NO5%
Attendance reported as regular
Source: See Part 1
YES
YES
236
TABLE 9.18: Attendance reported as regular by tested students and these students’ TECO results, per Geographic Area (Part 1)
Geographic Area Regular attendance = yes Regular attendance = no Totals TECO
NO/YES# YES % YES TECO # NO % NO TECO # YES+NO TECO
NORTH 1844 95.40 1025.61 89 4.60 995.83 1933 1024.01 97.10
CENTRE 2735 94.51 995.93 159 5.49 1013.92 2894 997.07 101.81
SOUTH 942 95.15 960.89 48 4.85 942.29 990 958.9 98.06
CENTRE-NORTH 2847 95.22 1027.46 143 4.78 1020.73 2990 1027.07 99.345
CENTRE-SOUTH 2674 94.59 970.48 153 5.41 974.56 2827 970.34 100.42
ITA12 5521 94.91 999.87 296 5.09 996.87 5817 999.53 99.70
YES and NO percentages are calculated on the row totals
Grey shading indicates the highest TECO value among YES and NO
Of the total 5853 tested students, 36 observations are missing.
TECO NO/YES is the ratio between TECO for students who report irregular attendance versus TECO for those who report regular attendance x 100
Source: See TABLE 3. See Part 2 for graphs
NORTH(+)(*)
CENTRE
SOUTH
CENTRE-NORTH (+)(*)
CENTRE-SOUTH
ITA12
-50
-40
-30
-20
-10
0
10
20
30
40
-0,6 -0,4 -0,2 0,0 0,2 0,4 0,6
TECO
% YES
NORTH(+)(*)
CENTRE
SOUTH
CENTRE-NORTH (+)(*)
CENTRE-SOUTH
ITA12
-4
-3
-2
-1
0
1
2
3
4
5
6
-0,6 -0,4 -0,2 0,0 0,2 0,4 0,6
TECO NO/YES
% NO
900
940
980
1020
1060
NORD CENTRO SUD CENTRO-NORD CENTRO-SUD ITA12
TEC
O
SI NO
TABLE 9.18: Attendance reported as regular by tested students and these students’ TECO results, per Geographic Area (Part 2)
SI95%
NO5%
Attendance reported as regular
Source: See Part 1
YES
YES
237
8. Index of Annexes available upon request
The following Annexes are available upon request addressed to ANVUR.
ANNEX 1 ANVUR (2012) Set-up of a Working Group to adopt and carry out a pilot test of
generic competences on Italian graduating students, and of a Committee of Guarantors for international selection of a test (Resolution No. 65 of 13 August 2012)
ANNEX 2 Pruneti F. (2012), Proposal of a CINECA solution for administering the CLA+ test to Italian graduating students in 12 pilot universities in 2013 – slide shown at the meeting in Casalecchio di Reno, 17 December 2012.
ANNEX 3 Exchange of mails on the proposal to modify costs in the agreement with CINECA ANNEX 4 ANVUR-Invitalia contract, 2013. ANNEX 5 ANVUR_CRUI agreement, 19 February 2013. ANNEX 6 ANVUR (2012b) Summary Report of the meeting of the Working Group to adopt
and carry out a pilot test of generic competences on Italian graduating students (WG test GSK), Rome, 19 September 2012
ANNEX 7 CBUI (2012), CBUI certification. Model for the national co-ordination of a quality-based orientation of courses of study, 25 January 2012
ANNEX 8 ANVUR (2012c) Summary Report of the meeting of the Working Group to adopt and carry out a pilot test of generic competences on Italian graduating students, Rome, 7 November 2012
ANNEX 9 ANVUR (2013) Form per single class (and only if needed, per single course of studies) for listing possible explanations about the regularity index.
ANNEX 10 CAE-ANVUR contract, 15 February 2013 ANNEX 11 CAE (2012), Confidentiality Agreement with ANVUR ANNEX 12 Scoring Guide, TECO “Parks” Performance Task, 20 June 2013 ANNEX 13 Vendruscolo, F. (2013), Table on the scoring logic ANNEX 14 Vendruscolo, F. (2013), Table on scoring the “Parks” Performance Task ANNEX 15 Vendruscolo, F. (2013), Table on scoring the “Parks” Performance Task - Filter ANNEX 16 CLA+ Assessment Guide ANNEX 17 Presentation of the TECO test ANNEX 18 CAE (2014), CLA+ ANVUR Item Analysis Report, 30 September 2013, with an
update on 20 January 2014 ANNEX 19 TECO pre-registration form ANNEX 20 CAE, Cognitive Laboratory Guide ANNEX 21 TECO test administration dates ANNEX 22 Sample certificate of participation to TECO ANNEX 23 Sample certificate of results obtained on TECO ANNEX 24 Letter of thanks sent to students who sat the TECO test ANNEX 25 Summary of certificates requested per class ANNEX 26 Student Prospectus, University of Camerino, 5 April 2013
238
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Vendruscolo, F. (2013), Relazione sullo svolgimento della sperimentazione TECO nell’Università di Udine, mimeograph.
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