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Framing Learning Analytics as an Opportunity: Toward a

Center for Data Governance and Innovation

Stefan T. Mol(s.t.mol@uva.nl)

Potential data sources

• Is it ethical to use these data to improve the learning process?

Potential data sources

• Is it ethical to NOT use these data to improve the learning process?

Learning analytics

… is het meten, verzamelen, analyseren en rapporteren van en over data van leerlingen en hun context, met als doel het begrijpen en optimaliseren van het leren en de omgeving waarin dit plaatsvindt (SOLAR 2012).

Learning analytics

… is het meten, verzamelen, analyseren en rapporteren van en over data van leerlingen en hun context, met als doel het begrijpen en optimaliseren van het leren en de omgeving waarin dit plaatsvindt (SOLAR 2012).

Learning analytics

… is het meten, verzamelen, analyseren en rapporteren van en over data van leerlingen en hun context, met als doel het begrijpen en optimaliseren van het leren en de omgeving waarin dit plaatsvindt (SOLAR 2012).

Learning Analytics Innovatie

Vereiste kennis• Onderwijskundigen• Psychologen• Data scientists

Voorwaardelijke kennis• Ethici• Juristen

Inherente multidisciplinariteit

Stakeholder analysis

(Szörényi, 2014)

The LA Focus Groep in Context

The UvAInform Project - History

• Initiated as a proposal from the ICTS Department• Expertise group Education ICT (EGO-ICT) Reserved 150K on its 2013

budget• EGO-ICT dislike of (bottom-up) tender procedure with limited

strategic vision• Focus Group Learning Analytics Established Late 2012• UvAInform proposal approved in June 2013• Central infrastructure (LRS/Dashboards)• (De?)centralized pilot(s)

Potential data sources

• Who owns what data?• Highly political issue• Organizational resistance • Gatekeepers resistance

• Complex infrastructure

Learning Record Store

• Community sourced, secure, scalable repository/infrastructure • Store and retrieve statement data reliably and ensures a good scalable

storage layer for various types of data and data streams• Scales above 100 billion records.• These data can be made available in a secure and consistent way for

further analysis. • Upon this LRS infrastructure dashboards can be built or developed for

the delivery of (analysed) data to students, educators, and researchershttp://tincanapi.com

Cluster 1: Mirroring of traditional and non-traditional study performance to students

UvA Inform (COACH2 - FNWI)• Visualize the position of individuals in the context of the group using

BB data• Using positioning of individual student to determine support from

teaching staff.Cluster Exam Feedback (qDNA - FMG) • More fine grained mirroring of exam results to provide students and

teachers with insight in the development on four competencies (interpretation, analysis, evaluation, inference) and knowledge goals

Cluster 1: Mirroring of traditional and non-traditional study performance to studentsIdentifying Types of Effective Comparative Feedback and Relevant Mediators (FMG)

Cluster 1: Mirroring of traditional and non-traditional study performance to studentsGoal setting in education (FEB)• Building on functionality of schedules (www.roosters.uva.nl) to include goal

setting and goal tracking. • Students will be instructed/taught to formulate goals in a concrete manner

such that they are Specific, Measurable, Attainable, Relevant and Time-bound (SMART).• Dashboard will facilitate individual students to choose from, and set their

own goals (and deadlines) against specific course events deadlines based on mirrored data. • Dashboard shows students how they are scoring/succeeding in attaining

the goals compared to their fellows

Arbeidsmarktgeorienteerde leertrajecten

Koppelen van student data aan arbeidsmarkt data

Inkomende studenten helpen om een

langetermijn orientatie te krijgen tijdens hun studie

Spiegelen van alumni dataaan huidige studenten op basis van geambieerde /

bereikte functies

Cluster 3: Using other people’s data to provide recommendation system to studentsValidating Learning Analytics in Higher Ed. (FGW)• Determine the predictive validity of demographic data, learning

styles, motivation, and behavior to optimize prediction of study success• Developing interventions (recommendations based) based on

evidence

Proof Of Concept: Data warehouse and data governance

● ICTServices: SAP HANA <<Very fast computer>>

● Validated Predictive models     Open Academic Analytics Initiative.● Have support from researcher(s)● Need data governance and infrastructure that supports a student

consent service and self declared data.

Center for Data Governance & Innovation

Possible roles of a Center for Data Governance and Innovation• Supporting learning analytics (and other big data initiatives)• Evaluating/approving specific learning analytics projects, ensuring

ethical and legal compliance• Centralization of knowledge • Streamlining policies (e.g. user agreements) so as to facilitate

Learning analytics (and other big data Initiatives)• Facilitating communications amongst key stakeholder• Managing public relations• Later: Establishing decision trees

Slade & Prinsloo (2013)…

• Petersen (2012, P. 46) states, “The most important step that any campus can take is to create a comprehensive data-governance structure to address all the types of data used in various situations.”

• Researchers and ethics boards should “work in tandem to forge the next generation of research ethics, one that still embraces core principles while creating new opportunities for important research endeavors” (Buchanan, 2011, p. 103).