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Learning analytics in higher education: Promising practices and lessons learned
This document summarizes Bodong Chen's presentation on learning analytics in higher education. The presentation covered three parts: 1) A study of Australian universities' use of learning analytics, which identified different clusters of institutions based on their drivers and perceptions. 2) Initiatives at the University of Minnesota, including the Unizin consortium and pilots using learning management system data. 3) An experiment using learning analytics in one of Chen's own classes to promote student participation and awareness. Key cross-cutting considerations discussed were the interventionist nature of analytics and importance of cultural shifts, conversations, and building educator data literacy.
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Learning analytics in higher education: Promising practices and lessons learned
- 1. LEARNING ANALYTICS INHIGHER EDUCATION PROMISING PRACTICES AND LESSONS LEARNED Bodong Chen, University of Minnesota October 27, 2016, Manila, Philippines @bod0ng
- 2. BODONG CHEN Assistant Professorin Learning Technologies University of Minnesota-Twin Cities Research interests: online learning, learning analytics, CSCL, knowledge building
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- 4. ... my mainconcern is the well being of the plant materials ... And because of the diversity of plants that we grow, we have to have a wide range of niches to put those plants into. Some need it to be a little cooler. Some want it a little warmer. Some want to be drier. Some want to be wetter. Our job here is to work with Mother Nature and to try to provide the conditions optimal for growth. Source
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- 7. , U.S. Departmentof Homeland SecurityVisual Analytics Law Enforcement Toolkit (VALET)
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- 9. LEARNING ANALYTICS IS “Themeasurement, collection, analysis, and reporting of data about learners and their contexts” WHAT Long, P., & Siemens, G. (2011). Penetrating the Fog: Analytics in Learning and Education. Educause Review, 46(5), 30–32.
- 10. LEARNING ANALYTICS IS “Themeasurement, collection, analysis, and reporting of data about learners and their contexts for understanding and optimising learning and the environments in which learning occurs” WHY (Long & Siemens, 2011)
- 12. CAN ANY OFTHESE PLAYERS AFFORD NOT USING DATA?
- 13. WHAT I'M SEEINGAS A PROFESSOR?
- 14. Buckingham Shum, S.(2012). . UNESCO Institute for Information Technologies in Education. UNESCO Policy Brief: Learning Analytics
- 15. AGENDA A study ofAustralian universities University of Minnesota My Classrooms Cross-cutting factors
- 16. PART 1: ASNAPSHOT OF AUSSIE UNIVERSITIES Colvin, C., Rogers, T., Wade, A., Dawson, S., Gasevic, D., Buckingham Shum, S., … Fisher, J. (2015). . Australian Department of Education. Student retention and learning analytics: A snapshot of Australian practices and a framework for advancement
- 17. AN INTERVIEW STUDY RESEARCHQUESTION How senior institutional leaders perceived learning analytics including the drivers, affordances and constraints? PARTICIPANTS Senior institutional leaders (Deputy Vice Chancellors) ANALYSES Qualitative Coding + Cluster Analysis
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- 19. CLUSTERS NOT DEFINEDBY 'YEARS' ... Cluster 1 (7) Cluster 2 (26) Purpose Measure Understand Driver type Ef ciency Learning and student success Retention Independent Inter-dependent Learning dimensionality Unidimensional Multidimensional Analytics Predictive Learning itself ... ... ... (Colvin et al., 2015)
- 20. PART 2: INITAITIVESAT MY UNIVERSITY
- 21. University-owned and directedconsortium
- 22. GETTING PEOPLE INVOLVED! Instructors Students Advisors Administrators ResearchFaculty Credit: UMN LA Team
- 23. UMN LEARNING ANALYTICS DATA StudentInformation System Learning Management Systems Student Advising Systems A COMMON DATA LAYER ANALYTICS Dashboards Predictive engines
- 24. ONGOING UNIZIN PILOTS CanvasLMS Engage Snapshot ...
- 25. BROADER DISCUSSIONS Ethics: ethicaluse of data Students: interacting with students Quality: data quality Leadership: the U's leadership structure Research: esp. related to Unizin's deidenti ed data Credit: UMN LA Team
- 26. PART 3: ANEXPERIMENTATION IN MY CLASS
- 28. MY PEDAGOGICAL GOALS Promoteforum participation from students? Help students become more aware and re ective of their participation
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- 35. 1. LEARNING ANALYTICSNOT NEUTRAL Data are not neutral Our analytics are our pedagogy (Knight et al., 2014) Interventionist by nature educational visions and values Replicate - Amplify - Transform (Hughes, Thomas, & Scharber, 2006)
- 36. 2. CONVERSATIONS Among datasets Amongpeople Between data and people Across levels Credits: , ,1 2 3
- 37. 3. CULTURAL SHIFTAND CAPACITY BUILDING Data practices Educator data literacy Leadership structure ...
- 38. OPPORTUNITIES FOR OPENUNIVERSITIES Openness Comprehensiveness of data Unique local contexts Cross-institution collaboration . . .
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