SHEILA Project - Workshop Slides Online Educa Berlin 2016

Supporting Higher Education to Incorporate Learning Analytics

OEB‘16SHEILAWorkshop30thNovember2016

JeffHaywood,DraganGašević,AdolfoRuizCalleja,MarenScheffel

Supporting Higher Education to Incorporate Learning Analytics

ProgrammeforOEB‘16SHEILAWorkshop,30thNovember20161000–1005 Welcome&introduc3ons–ProfJeffHaywood(UniversityofEdinburgh)

1005–1110 Threepresenta3ons&discussion“Overview–LearningAnaly3cs&SHEILAProject”ProfDraganGasevic(universityofEdinburgh)“SHEILAProject–interviewfindings”ProfAdolfoRuizCalleja(UniversityofTallinn)“SHEILAProject–conceptmapping”MarenScheffel(OpenUniversityoftheNetherlands)

1110–1130 Coffeebreak

1130–1300GroupworkonapplyingtheRapidOutcomeMappingApproach(ROMA)toassessingins3tu3onalreadinessfordeployingLA&plenarydiscussion

1300 Close

LearningAnaly3cs&SHEILAProject-Overview-

DraganGašević@dgasevic

November30,2016OEBSHEILAWorkshopBerlin,Germany

hap://sheilaproject.eu/

Educa3onalLandscapeToday

Growingneedforeduca3onAc3velearning

Educa3ondefunding

Feedbackloopsbetweenstudentsandinstructors

aremissing/weak!

LEARNINGANALYTICS

Learningenvironment

Educators

LearnersStudent

Informa3onSystems

Blogs

Videos/slides

Mobile

Search

Educators

Learners

Networks

StudentInforma3onSystems

Learningenvironment

Blogs

Mobile

Search

Networks

Educators

LearnersStudent

Informa3onSystems

Learningenvironment

Videos/slides

Learninganaly3cstopics

Veryfewins3tu3on-wideexamplesofadop3on

Adop3onchallenge

Policiesfor

learninganaly3csprac3ce

Adop3onchallenge

Equalengagementwithdifferentstakeholders

Adop3onchallenge

Trainingopportuni3estouselearninganaly3cs

Adop3onchallenge

Monitoringimpactandlearningfromtheuse

Policydevelopmentframework

Usedbydifferenthighereduca3onins3tu3onsRecognizesins3tu3onspecificdrivers

Promoteslearninganaly3csforforma3veassessment


Macfadyen,L.,Dawson,S.,Pardo,A.,Gašević,D.,(2014).Thelearninganaly3csimpera3veandthesociotechnicalchallenge:Policyforcomplexsystems.Research&Prac+ceinAssessment,9(Winter2014),17-28.


Projectac3vi3es

Literaturereview


Tsai,Y-S.,Gašević,D.(2016).Adop+onofLearningAnaly+csinHigherEduca+on:State,Challenges,andPolicies(Execu+vesummary).SHEILAProjectReport,hap://bit.ly/sheila_lr_es

Projectac3vi3es

LiteraturereviewInterviewswithdecisionmakers


Projectac3vi3es


Groupconceptmappingwithexperts


Projectac3vi3es


GroupconceptmappingwithexpertsSurveysandfocusgroupswithstudentsandteachingstaff


Expected3meline

Policydevelopmentframework(v1)–June2017Fourins3tu3onalpolicies(v1)–June2017


Expected3meline

Policydevelopmentframework(v1)–June2017Fourins3tu3onalpolicies(v1)–June2017

Policydevelopmentframework(v2)–March2018Fourins3tu3onalpolicies(v2)–March2018


Interestedinjoining?

Associatepartnersasearlyadoptersofthepolicydevelopmentframeworkarewelcome


Manythanks!


Interviews in SHEILA project

Adolfo Ruiz-Calleja, Kairit Tammets, Yi-Shan Tsai, Jeff Haywood, Maren Scheffel, Pedro Muñoz Merino and Dragan Gasevic

30th, November 2016

Index

32

■ Methodological aspects

■  Topics of the interviews

■ Results

■  Conclusions

Index

33



■ Results

■  Conclusions

Methodology: ROMA

34 Macfadyen, L., Dawson, S., Pardo, A., Gašević, D., (2014). The learning analytics imperative and the sociotechnical challenge: Policy for complex systems. Research & Practice in Assessment, 9(Winter 2014), 17-28.

Methodology

35

■  Purpose:

■  Sampling:

To collect opinions from decision makers in higher education institutions regarding the adoption of Learning Analytics.

•  Policy makers •  Vice-rectors •  Study departments •  Academic offices •  IT departments

Methodology

36

■  65 interviews

■  52 institutions

■  12 countries

Index

37



■ Results

■  Conclusions

Topics

38

■  LA projects and their goals

■  LA strategy

■  Progress and achieved goals

■  Challenges

■  Ethics and privacy

Index

39



■ Results

■  Conclusions

Results: LA projects and goals

40

■  Exploratory objectives

■  Very often LA projects are related to research

■ Diversity of interests

•  Sense of uncertainty •  What can LA do for my institution? •  What can our data tell us?

•  UK: correlation between behavioural patterns and learning outcomes

•  Spain: support teachers and improve their performance •  Baltics: improve students retention

Results: LA strategy

41

■  For some institutions (UK, Latvia, Finland) LA is part of their wider strategy

■ Others follow a bottom-up approach: some projects without connecting backbone structure

■ Managers become more interested on LA

Results: Achieved goals

42

■  Too early phase in many institutions

■  LA is considered useful

■ Awareness of LA, better understanding of challenges and improvement of data culture

■  In-house software is typically used •  Vendors offer Dashboards or systems to detect drop-out

Results: Challenges

43

■  Cultural barriers

■  Little technical experience

■ Human resources

•  Data-driven and not question-driven •  Traditional institutional culture •  Lack of awareness •  Lack of data culture

•  Many institutions do not feel ready for maintaining a data management infrastructure

•  Little experience on how to deal with online environments

•  Lack of time •  Priorities

Results: Ethics and privacy

44

■  Ethical use of data remains a big challenge

■  Privacy laws are very diverse

■  Ethical issues open technical problems

•  Although not all of them understand it as a problem (Estonia) •  Clear need of ethical guidelines and solutions

•  e.g. Germany and Austria are much more restrictive than UK or Estonia

•  It is very difficult to avoid processing the data of one specific student if he does not allow it

Index

45



■ Results

■  Conclusions

Conclusions

46

Conclusions

47

■  European universities are in an early stage of adoption of LA

■ Bottom-up approach from researchers, and awareness of policy makers

■  Several barriers have been detected, including ethics

■  There are important differences between countries

Interviews in SHEILA project

Adolfo Ruiz-Calleja, Kairit Tammets, Yi-Shan Tsai, Jeff Haywood, Maren Scheffel, Pedro Muñoz Merino and Dragan Gasevic

30th, November 2016

GroupConceptMapping

MarenScheffelOpenUniversiteitNederland

[email protected]@m_a_s_c

GroupConceptMappingPhase1:

Brainstorming

Phase2: Sor3ng

Phase3: Ra3ng

Anessen3alfeatureofahighereduca3onins3tu3on'slearninganaly3cspolicyshouldbe...

PointMap1 2

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ClusterReplayMap

ClusterMap

1.privacy&transparency

2.roles&responsibiliZes(ofallstakeholders)

3.objecZvesofLA(learnerandteachersupport)

4.risks&challenges

5.datamanagement

6.research&dataanalysis

Ra3ngMap–Importance




4.risks&challenges

5.datamanagement


ClusterLegendLayerValue15.08to5.2725.27to5.4635.46to5.6545.65to5.8455.84to6.03

Ra3ngMap–Ease




4.risks&challenges

5.datamanagement


ClusterLegendLayerValue13.79to4.1224.12to4.4534.45to4.7844.78to5.1155.11to5.44

Ra3ngLadderGraph

importance ease

privacy&transparency

privacy&transparency

risks&challenges

risks&challenges

roles&responsibili3es(ofallstakeholders)

roles&responsibili3es(ofallstakeholders)

objec3vesofLA(learnerandteachersupport)

objec3vesofLA(learnerandteachersupport)

datamanagement

datamanagement

research&dataanalysis

research&dataanalysis

3.79 3.79

6.03 6.03

r=0.66

GoZone–Privacy&Transparency

1 720 31

43862

10172445

64 65

8892

91556

6074

87

34

69

96

6.08

5.44

3.12

ease

3.83 6.03 6.59

importance

r=0.45

GoZone–Privacy&Transparency

1 720 31

43862

10172445

64 65

8892

91556

6074

87

34

69

96

6.08

5.44

3.12

ease

3.83 6.03 6.59

importance

r=0.45

2.transparency,i.e.clearlyinformingstudentsofhowtheirdataiscollected,usedandprotected88.acleardescrip3onofdataprotec3onmeasurestaken10.acleardescrip3onofdatausage17.beingclearaboutthepurposeforcollec3oncertaintypesofdata24.alignedwithdataprotec3onregula3ons(ins3tu3onal,na3onal,interna3onal)34. toassurethatthecollecteddataisusedonlyforthepurposeofimprovinglearningandinstruc3on96.anagreementbetweenlearners,teachersandpolicymakersonregula3ngaproperuseofdata

GoZone–Roles&Responsibili3es

5

38

62

11

1922

33

39 48

7091

25

28

37

40

55

61

6627

47 49

6.08

4.72

3.12

ease

3.83 5.48 6.59

importance

r=0.26

GoZone–Roles&Responsibili3es

5

38

62

11

1922

33

39 48

7091

25

28

37

40

55

61

6627

47 49

6.08

4.72

3.12

ease

3.83 5.48 6.59

importance

r=0.26

55.beingclearaboutthepurposeoflearninganaly3cs61.aclearar3cula3onofrolesandresponsibili3eswhenitcomestotheuseofins3tu3onaldata39.topromotebroadadop3onoflearninganaly3csbyspecifyingsuppor3veregula3onsandcaselaw

GoZone–Objec3vesofLA

3

80

84

99

12

7898

3550

93

4244

586376

79

94

6.08

3.79

3.12

ease

3.83 5.44 6.59

importance

r=-0.12

GoZone–Objec3vesofLA

3

80

84

99

12

7898

3550

93

4244

586376

79

94

6.08

3.79

3.12

ease

3.83 5.44 6.59

importance

r=-0.12

63.touselearninganaly3csforimprovingthequalityofteaching58.toallowforproac3vebehaviorofstudentstowardstheireduca3on,withsetngpersonalgoals44.tosupportlearnerslearningtolearnandimproveskills98.toencouragemeaningfulrela3onshipsbetweenstudents

GoZone–Risks&Challenges

18

41

8326

46

97

53

59

90

95

6.08

4.16

3.12

ease

3.83 5.52 6.59

importance

r=0.52

GoZone–Risks&Challenges

18

41

8326

46

97

53

59

90

95

6.08

4.16

3.12

ease

3.83 5.52 6.59

importance

r=0.52

90.todrawaaen3ontowhatthe"bigpicture"endgoalofthedatauseis59.todemarcateclearlybetweendifferentusesofanaly3csinins3tu3onalsetngs95.toensurethebenefitstostudentsoutweightherisks53.toensureadequaterepresenta3onduringhigh-stakesdecisionprocesses18.integratedwitharewardsystemfortheuseofevidence-basedimprovementstoteachingandlearning41.todiscouragestudentsandfacultyfromgamingthesystem

GoZone–DataManagement

4

14

89

23

67

71

85

6.08

4.5

3.12

ease

3.83 5.16 6.59

importance

r=-0.54

GoZone–DataManagement

4

14

89

23

67

71

85

6.08

4.5

3.12

ease

3.83 5.16 6.59

importance

r=-0.54

23.todefineclearrulesforcollabora3onwithotherresearchers

GoZone–Research&DataAnalysis

6

30

57

6872

75

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81

8

32

527382

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1621

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54

6.08

4.24

3.12

ease

3.83 5.08 6.59

importance

r=0.27

GoZone–Research&DataAnalysis

6

30

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6872

75

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81

8

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527382

1351

1621

29

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54

6.08

4.24

3.12

ease

3.83 5.08 6.59

importance

r=0.27

57.toassurethatinstruc3onalinterven3onsarebasedonwell-studiedandempiricallyvalidatedanaly3calmethodsandalgorithms75.toencouragethedevelopmentofdashboardswithmeaningfulandunderstandableoutcomes68.torecognisethelimita3onsoflearninganaly3cs72.toprovideclearexamplesofhowtointerpretdata,especiallywhentheremaybeinterac3ons81.to(re)evaluatecon3nuouslytheanaly3csbeingapplied54.toenhancetheconnec3onbetweentheacademicandthesocietyandindustry,showingwhatisbeingdoneattheins3tu3onsisrelatedtotheeduca3onandtraining21.tomakeexplicitmathsfrominnova3vedevelopment/useofanaly3csinindividualclassroomstoscalingup

Thank [email protected]@m_a_s_c

SHEILA Project - Workshop Slides Online Educa Berlin 2016

Education

Transcript of SHEILA Project - Workshop Slides Online Educa Berlin 2016