aiec & csr presentation
TRANSCRIPT
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Framing Learning Analytics as an Opportunity: Toward a
Center for Data Governance and Innovation
Stefan T. Mol([email protected])
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
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.
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).