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Learning analytics in higher education: Promising practices and lessons learned

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A talk I presented at the 30th AAOU conference in Manila

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Learning analytics in higher education: Promising practices and lessons learned

  1. 1. LEARNING ANALYTICS IN HIGHER EDUCATION PROMISING PRACTICES AND LESSONS LEARNED Bodong Chen, University of Minnesota October 27, 2016, Manila, Philippines @bod0ng
  2. 2. BODONG CHEN Assistant Professor in Learning Technologies University of Minnesota-Twin Cities Research interests: online learning, learning analytics, CSCL, knowledge building
  3. 3. Credit: Dreamtime.com
  4. 4. ... my main concern 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
  5. 5. PRECISION AGRICULTURE Credit: Airborne
  6. 6. AMAZON RECOMMENDATIONS
  7. 7. , U.S. Department of Homeland SecurityVisual Analytics Law Enforcement Toolkit (VALET)
  8. 8. Source
  9. 9. LEARNING ANALYTICS IS “The measurement, 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. 10. LEARNING ANALYTICS IS “The measurement, 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)
  11. 11. CAN ANY OF THESE PLAYERS AFFORD NOT USING DATA?
  12. 12. WHAT I'M SEEING AS A PROFESSOR?
  13. 13. Buckingham Shum, S. (2012). . UNESCO Institute for Information Technologies in Education. UNESCO Policy Brief: Learning Analytics
  14. 14. AGENDA A study of Australian universities University of Minnesota My Classrooms Cross-cutting factors
  15. 15. PART 1: A SNAPSHOT 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
  16. 16. AN INTERVIEW STUDY RESEARCH QUESTION 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
  17. 17. OVERALL POSITIVE
  18. 18. CLUSTERS NOT DEFINED BY '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)
  19. 19. PART 2: INITAITIVES AT MY UNIVERSITY
  20. 20. University-owned and directed consortium
  21. 21. GETTING PEOPLE INVOLVED! Instructors Students Advisors Administrators Research Faculty Credit: UMN LA Team
  22. 22. UMN LEARNING ANALYTICS DATA Student Information System Learning Management Systems Student Advising Systems A COMMON DATA LAYER ANALYTICS Dashboards Predictive engines
  23. 23. ONGOING UNIZIN PILOTS Canvas LMS Engage Snapshot ...
  24. 24. BROADER DISCUSSIONS Ethics: ethical use 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
  25. 25. PART 3: AN EXPERIMENTATION IN MY CLASS
  26. 26. MY PEDAGOGICAL GOALS Promote forum participation from students? Help students become more aware and re ective of their participation
  27. 27. SOCIOGRAM
  28. 28. WORDCLOUD
  29. 29. CROSS-CUTTING FACTORS TO CONSIDER
  30. 30. 1. LEARNING ANALYTICS NOT 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)
  31. 31. 2. CONVERSATIONS Among datasets Among people Between data and people Across levels Credits: , ,1 2 3
  32. 32. 3. CULTURAL SHIFT AND CAPACITY BUILDING Data practices Educator data literacy Leadership structure ...
  33. 33. OPPORTUNITIES FOR OPEN UNIVERSITIES Openness Comprehensiveness of data Unique local contexts Cross-institution collaboration . . .
  34. 34. THANK YOU! chenbd@umn.edu bodong.ch @bod0ng ACKNOWLEDGEMENT

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