Turning Learning into Numbers - A Learning Analytics Framework

Turning Learning into Numbers A Learning Analytics Framework Graphic by Alex Guerten, 2008Hendrik Drachsler & Wolfgang GrellerCentre for Learning Sciences and Technologies (CELSTEC) 1

Goals of the Presentation LA Framework Examples Data initiatives Participation 2

A view on Learning AnalyticsThe LearningAnalyticsFrameworkGreller, W., & Drachsler, H., (submitted). Turning Learning into Numbers.Toward a Generic Framework for Learning Analytics. Journal of EducationalTechnology & Society. 3

Zooming into the FrameworkPracticalexamples forapplicationand use 5

Stakeholders data datasubjects clients 6

ObjectivesReﬂection(Glahn, 2009) 7

ObjectivesReﬂection Prediction(Glahn, 2009) 7

Educational DataVerbert, K., Manouselis, N., Drachsler, H., and Duval,E. (submitted).Dataset-drivenResearch to Support Learning and Knowledge Analytics. Journal of Educational Technology& Society. 8

Educational DataDrachsler, H., et al. (2010). Issues and Considerations regarding Sharable Data Sets forRecommender Systems in Technology Enhanced Learning. 1st Workshop RecommnderVerbert, in Manouselis, N., Drachsler, H., and Duval,E. (submitted).Dataset-drivenSystemsK., Technology Enhanced Learning (RecSysTEL@EC-TEL 2010) September, 28,Research to Support Learning and Knowledge Analytics. Journal of Educational Technology2010, Barcelona, Spain.& Society. 8

Educational Data 1.Privacy 2.Prepare datasets 3.Share datasets 4.Body of knowledgeDrachsler, H., et al. (2010). Issues and Considerations regarding Sharable Data Sets forRecommender Systems in Technology Enhanced Learning. 1st Workshop RecommnderVerbert, in Manouselis, N., Drachsler, H., and Duval,E. (submitted).Dataset-drivenSystemsK., Technology Enhanced Learning (RecSysTEL@EC-TEL 2010) September, 28,Research to Support Learning and Knowledge Analytics. Journal of Educational Technology2010, Barcelona, Spain.& Society. 8

Technologies Learning AnalyticsHanani et al., (2001). Information Filtering: Overview of Issues, Research and Systems", User Modeling and User-Adapted Interaction, 11, 2001 9

TechnologiesPrediction 9

Technologies ReﬂectionPeter Kraker, Claudia Wagner, Fleur Jeanquartier, Stefanie N. Lindstaedt (2011):On the Way to a Science Intelligence: Visualizing TEL Tweets for Trend Detection 9Sixth European Conference on Technology Enhanced Learning (EC-TEL 2011)

Constraints 10

Constraints1.Legal protection 10

Constraints1.Legal protection2.Privacy 10

Constraints1.Legal protection2.Privacy3.Ethics 10

Constraints1.Legal protection2.Privacy3.Ethics4.Ownership 10

Privacy 11

Privacy1.Privacy as conﬁdentiality The right to be let alone (Warren and Brandeis, 1890) 11

Privacy1.Privacy as conﬁdentiality The right to be let alone (Warren and Brandeis, 1890)2.Privacy as control The right of the individual to decide what information about herself should be communicated to others and under which circumstances. 11

Privacy1.Privacy as conﬁdentiality The right to be let alone (Warren and Brandeis, 1890)2.Privacy as control The right of the individual to decide what information about herself should be communicated to others and under which circumstances.3.Privacy as practice The right to intervene in the ﬂows of existing data and the re-negotiation of boundaries with respect to collected data. 11

Privacy solutions 12

Privacy solutions1.Privacy as conﬁdentiality Information services that minimizing, secure or anonymize the collected information 12

Privacy solutions1.Privacy as conﬁdentiality Information services that minimizing, secure or anonymize the collected information2.Privacy as control Identity Management Systems (IDMS), with access control rules 12

Privacy solutions1.Privacy as conﬁdentiality Information services that minimizing, secure or anonymize the collected information2.Privacy as control Identity Management Systems (IDMS), with access control rules3.Privacy as practice Timestamp on data, data degradation technologies 12

Competences 13

Reading score of 15 year olds of countries Competences compared to the percentage of migrants 13

Reading score of 15 year olds of countries Competences compared to the percentage of migrants 1.E-literacy 2.Interpretation skills 3.Self-directedness 4.Ethical understanding 13

Data initiativesRelevantdata initiativesfor LearningAnalytics 14

Data Quality / Standard 15

Data Quality / StandardCAM -ContextualizedAttention Metadatahttps://sites.google.com/site/camschema/ 15

Data Citing 16

Sharing policies 17

Policy guidelinesA brief guide on data licenses developed by SURF and the Centre forIntellectual Property Law (CIER), 2009 available atwww.surffoundation.nl 18

Data platforms 19

ParticipationHow you canbecomeinvolved... 20

Learning Analytics Challenges• Reduce the drop-out rate by 10% through applying Learning Analytics prediction and reflection techniques.• Customize data mining techniques to learning and educational reality.• Evaluation criteria for Learning Analytics applications• Generic infrastructure for sharing, analyzing and reusing educational data.• Applying existing privacy and legal protection solutions for Learning Analytics. 21

Open issues1. Evaluation of LA approaches2. Comparable experiments3. Publicly available datasets4. Body of knowledge5. New Learning Analytic applications6. Privacy and data protection7. Best practice and ethical guidelines 22

Learning Analytics Questionnairehttp://bit.ly/Learning_Analytics 23

Special Interest Group - dataTEL 24

Special Interest Group - dataTEL 1.Networking 2.Privacy, legal, ethics 3.LA datasets 4.LA products http://bit.ly/datatel 24

Many Thanks::Questions?This presentation isavailable at:slideshare.com/Drachsler Blackmore’s custom-built LSD Drive http://www.ﬂickr.com/photos/rootoftwo/Email: hendrik.drachsler@ou.nlEmail: wolfgang.greller@ou.nl 25

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Turning Learning into Numbers - A Learning Analytics Framework

by Hendrik Drachsler on Aug 30, 2011

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Turning Learning into Numbers - A Learning Analytics Framework — Presentation Transcript