Student selection

• First-level objectives– ”Good” individuals– A balanced group in terms of paths

• Second-level objectives– Gender issues, professional tracks

• Data available on 6 criteria– 3rd and 4th year grades– Interview scores regarding motivation, personality,

professional projects and knowledge of IE• Additional information

Data analysis - correlation

• Interview-based criteria are strongly positively correlated with one another

• There is little correlation between the 3rd year grade and the other criteria in 2009

  3rd 4th Motiv Pers. Proj. Know IE

3rd 1

4th 0,23 1

Motivation -0,09 0,40 1

Personality 0,00 0,44 0,75 1

Projects 0,06 0,39 0,70 0,76 1

Know IE -0,02 0,36 0,72 0,54 0,66 1

  3rd 4th Motiv Pers. Proj. Know IE3rd 1

4th 0,47 1

Motivation 0,10 0,21 1

Personality 0,16 0,22 0,83 1

Projects 0,08 0,13 0,84 0,80 1

Know IE 0,24 0,22 0,81 0,73 0,72 1

2009

2010

Data analysis – PCA

Attribute Axis_1 (55,61%) Axis_2 (74,57%)- Corr. % (Tot. %) Corr. % (Tot. %)

3rd0,0403 0 % (0 %) 0,9257

86 % (86 %)

4th0,5953

35 % (35 %) 0,4779

23 % (58 %)

Motivation 0,891

79 % (79 %) -0,1852 3 % (83 %)

Personality 0,8687

75 % (75 %) -0,0472 0 % (76 %)

Project0,8796

77 % (77 %) -0,0161 0 % (77 %)

Jobs0,8114

66 % (66 %) -0,1252 2 % (67 %)

Var. Expl.3,3366

56 % (56 %) 1,1377

19 % (75 %)

Factor loadings (2009)

• The two first axes explain ca. 75% / 80% of the variation

• First axis corresponds to interview criteria, the second to the grades

Attribute Axis_1 (58,02%) Axis_2 (80,90%)- Corr. % (Tot. %) Corr. % (Tot. %)

3rd0,276 8 % (8 %) 0,8213

67 % (75 %)

4th0,3391

11 % (11 %) 0,7779

61 % (72 %)

Motivation 0,9358

88 % (88 %) -0,1705 3 % (90 %)

Personality 0,9083

83 % (83 %) -0,1123 1 % (84 %)

Project0,8952

80 % (80 %) -0,2254 5 % (85 %)

Jobs0,8874

79 % (79 %) -0,0305 0 % (79 %)

Var. Expl.3,4809

58 % (58 %) 1,3732

23 % (81 %)

Factor loadings (2010)

BLR

UTA u(.)

RPM-screening- Loose statements on importance weights

Initial screening-Minimum criterion-specific performances- Missing data etc.

Input:-Criterion scores- Path preferences

Reject

Reject students with CI=0

Ranking

Ranking + Selection recommendations

Selected students with CI=1Further consider

students with 0 < CI < 1

Path allocation- Priority if CI=1 and path preference expressed- Path completed to have roughly equal distribution of gender and PT

Weight elicitation using BLR

Attribute Coef. Std-dev Wald Signif

constant -30,6645 18,0493 2,8864 0,0893

3rd 1,493 1,281 1,3583 0,2438

4th 2,28907 3,4421 0,4422 0,506

Motivation 1,318128 1,1347 1,3495 0,2454Personality 2,216501 1,7905 1,5325 0,2157

Project 1,419918 0,9956 2,0338 0,1538

Know IE 2,918889 1,4799 3,8904 0,0486

Attribute Coef. Std-dev Wald Signif

Constant -8,09716 2,4878 10,5935 0,0011Motivation 1,921766 0,785 5,993 0,0144

Know IE 1,847997 0,7108 6,7596 0,0093

3rd 4th Motiv. Pers. Proj. Know IE

Weight 0.13 0.20 0.11 0.19 0.12 0.25

Full model Stepwise reduced model

Normalized weights elicited from the full model

Based on these, the weight information in the 2010 RPM-screening is given as:

4th, personality, knowledge > 3rd, motivation, professional project 4th, personality, knowledge > 0.1

Value Recall1-

PrecisionY 0,9583 0,0417N 0,8333 0,1667

Cross-validation

2009 Post-decision analysis – RPM and ranking

Core Borderline Exterior Sum

Selected 36 14 0 50

Not selected 0 11 4 15

Sum 36 25 4 65

• RPM (loose weight statements)

• Selecting the 14 borderline students according to the ranking resulting from the utility function with crisp weights, the actual 2009 selection is replicated with an exception of 1 student

3rd 4th Motiv. Pers. Proj. Know IE Status

QUAGLIATA 22 24,34 3 3 3 3 OUT

SANTARSIERO 24 25,72 2 3 4 1 IN

2010 RPM-screening (41/48 selected)

Core indicesName Score 40 41 42SANCHEZ 4,87 1 1 1AMODEO 4,83 1 1 1CARLUCCI 4,80 1 1 1GALLO 4,71 1 1 1ABBATANGELO 4,66 1 1 1D'IMPERIO 4,54 1 1 1FALCE 4,38 1 1 1PIETRAGALLA 4,31 1 1 1IORIO 4,24 1 1 1MAURO 4,24 1 1 1ROMANO 4,21 1 1 1DI LASCIO 4,21 1 1 1GUARINI 4,18 1 1 1INFANTINO 4,17 1 1 1CASCIO 4,11 1 1 1MONTEMURRO 4,05 1 1 1QUARATINO 4,00 1 1 1RICCIARDI 4,00 0,73 0,92 0,93DEPASCALE 3,95 1 1 1ROMANAZZI 3,94 1 1 1PANARIELLO 3,93 1 1 1MARINO 3,92 1 1 1DE MARE 3,89 0,80 0,92 1LO TITO 3,89 1 1 1PODANO 3,77 1 1 1PASTORE 3,73 1 1 1MARTOCCIA 3,65 1 1 1BRANCATO 3,64 1 1 1MANCUSI 3,58 1 1 1LULLO 3,55 0,33 0,38 0,60CONTE 3,52 1 1 1DI VIRGILIO 3,51 1 1 1CANOSA 3,49 1 1 1CORONATO 3,45 0,93 1 1MECCA 3,45 1 1 1TROGLIA 3,44 0,33 0,38 0,67GAZZANEO 3,43 1 1 1MUZZILLO 3,34 1 1 1LANEVE 3,20 0,93 1 1MARRA 3,04 0,93 0,92 1RAPUCCI 2,90 0,93 1 1PACE 2,54 0,33 0,46 0,53CAMARDA 2,28 0 0 0,07MARCIELLO 2,24 0 0,08 0,07PALERMO 2,10 0 0 0,13MASSIMINO 1,98 0,33 0,46 0,47GRANIERI 1,90 0,40 0,46 0,47CIRIGLIANO 1,85 0 0 0,07

= core students

= selected borderline students

= rejected borderline students

= exterior students

Path allocationMatlab

• Assign paths to satisfy the students’ preferences in the ranking order (core students prioritized)

• Put the student ”on hold”, if– The preferred path is full– No preference information is available

ILP with Excel Solver

• Assign the ”on-hold” students such that the distribution of women and PTs is roughly equal across paths– Maximize the minimum number of women across paths, minimize the

maximum number of students on a PT within and across paths

Path allocation - resultsInitial path allocation satisfying student preferences

Final path allocation to balance out the PT and gender distribution