Analytics Collaboration Session at Sakai 2011

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Academic Analytics is a hot topic in Higher Education. Institutions are seeking to use analytics to understand student success and academic performance, maximize retention. Increasingly, regulatory and accreditation bodies require this information to help measure effectiveness. This block session will report on a number of analytics initiatives within the Sakai Community, and higher education generally. Opportunities will be provided to interact with individual presenters, and to synthesise information available across the session.

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Analytics Collaboration Session at Sakai 2011

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  2. 2. Analytics:More Than Data-Driven Decisions Steven Lonn Research Specialist USE Lab, Digital Media Commons www.umich.edu/~uselab 2
  3. 3. Acknowledgements • USE Lab: • John Campbell – Stephanie D. Teasley • John Fritz – Andrew Krumm • Tim McKay – R. Joseph Waddington • David Wiley USE Lab Digital Media Commons 3http://umich.edu/~uselab University of Michigan
  4. 4. What is Analytics? + + USE Lab Digital Media Commons 4http://umich.edu/~uselab University of Michigan
  5. 5. Analytics in Our Lives USE Lab Digital Media Commons 5http://umich.edu/~uselab University of Michigan
  6. 6. Analytics in Our Lives USE Lab Digital Media Commons 6http://umich.edu/~uselab University of Michigan
  7. 7. Analytics in OAE! USE Lab Digital Media Commons 7http://umich.edu/~uselab University of Michigan
  8. 8. Analytics in OAE! USE Lab Digital Media Commons 7http://umich.edu/~uselab University of Michigan
  9. 9. Analytics in OAE! USE Lab Digital Media Commons 7http://umich.edu/~uselab University of Michigan
  10. 10. Analytics in Our Work USE Lab Digital Media Commons 8http://umich.edu/~uselab University of Michigan
  11. 11. Analytics in Our Work USE Lab Digital Media Commons 8http://umich.edu/~uselab University of Michigan
  12. 12. Analytics in Our Work at a? hi sd ll t it ha w DO ne oeso hatd W USE Lab Digital Media Commons 8http://umich.edu/~uselab University of Michigan
  13. 13. Data Collected at . . What kind of data is already available those “in the know?” USE Lab Digital Media Commons 9http://umich.edu/~uselab University of Michigan
  14. 14. Data Collected at . . Admissions USE Lab Digital Media Commons 10http://umich.edu/~uselab University of Michigan
  15. 15. Data Collected at . . Demographics USE Lab Digital Media Commons 11http://umich.edu/~uselab University of Michigan
  16. 16. Data Collected at . . Academic Record • Cumulative GPA • Specific course grades • Major / minor • Number of Michigan credits • Number of transfer credits • Credits / grades in subsets (e.g., math courses) USE Lab Digital Media Commons 12http://umich.edu/~uselab University of Michigan
  17. 17. Data Collected at . . Other Places Data is Gathered... USE Lab Digital Media Commons 13http://umich.edu/~uselab University of Michigan
  18. 18. Current Use of Data... USE Lab Digital Media Commons 14http://umich.edu/~uselab University of Michigan
  19. 19. What if...• Identify: – Who needs the most help – Most successful sequence of courses – Most / least successful portions of a course• Notify: – Instructors about their students – Students about their performance compared to peers – – Academic advisors about students “at risk” Staff about their resources (e.g., library use) ! USE Lab Digital Media Commons 15http://umich.edu/~uselab University of Michigan
  20. 20. Milestones• Stage 1: Extraction & reporting of transaction-level data• Stage 2: Analysis and monitoring of operational performance• Stage 3: What-if decision support (e.g., scenario building)• Stage 4: Predictive modeling & simulation• Stage 5: Automatic triggers of business processes (e.g., alerts) -- Goldstein & Katz, 2005 USE Lab Digital Media Commons 16http://umich.edu/~uselab University of Michigan
  21. 21. "#$%&#$()*$+,-##$(.$/00$)1213-,$#%31*)%#4 USE Lab Digital Media Commonshttp://umich.edu/~uselab University of Michigan
  22. 22. Signals • Purdue University • System developed in 2007 • Use of analytics for: – improving retention – identifying students “at risk” of academic failure USE Lab Digital Media Commons 18http://umich.edu/~uselab University of Michigan
  23. 23. Signals 567$58&%,9$5*:# ;383#%$<=>$?00@ !"#$%%&&&()*+,()*,-(%./%0102345%6#% 02307078902307078 USE Lab Digital Media Commons 19http://umich.edu/~uselab University of Michigan
  24. 24. “Check My Activity” Tool • University of Maryland, Baltimore County USE Lab Digital Media Commons 20http://umich.edu/~uselab University of Michigan
  25. 25. “Check My Activity” Tool • University of Maryland, Baltimore County USE Lab Digital Media Commons 20http://umich.edu/~uselab University of Michigan
  26. 26. “Check My Activity” Tool • University of Maryland, Baltimore County • Student-controlled • Designed to promote student agency & self-regulation • Low impact for the instructor USE Lab Digital Media Commons 20http://umich.edu/~uselab University of Michigan
  27. 27. Issues to Ponder • Who is the audience? – Students, Instructors, Advisors, Deans, Staff, Others? • Who has the control? – Issues of burden? • Which views? • Privacy concerns? – Is their an institutional obligation? • Is Learning Analytics just a fad? • Others? USE Lab Digital Media Commons 21http://umich.edu/~uselab University of Michigan
  28. 28. Our Project • M-STEM Academy – 50 Engineering students per cohort – Use Sakai data to better inform mentor team USE Lab Digital Media Commons 22http://umich.edu/~uselab University of Michigan
  29. 29. Our Project • M-STEM Academy – 50 Engineering students • When do students need mentoring / direction per cohort toUse Sakai data to better – resources? • How domentor team& students make use of this inform mentors data? • How does behavior change? USE Lab Digital Media Commons 22http://umich.edu/~uselab University of Michigan
  30. 30. USE Lab Digital Media Commons 23http://umich.edu/~uselab University of Michigan
  31. 31. USE Lab Digital Media Commons 24http://umich.edu/~uselab University of Michigan
  32. 32. comp USE Lab Digital Media Commons 25http://umich.edu/~uselab University of Michigan
  33. 33. Graphing! USE Lab Digital Media Commons 26http://umich.edu/~uselab University of Michigan
  34. 34. Advanced Graphing USE Lab Digital Media Commons 27http://umich.edu/~uselab University of Michigan
  35. 35. Project Next Steps• What Sakai events are “meaningful” for predicting student success?• Presenting data displays to advisors and students in-term. – Is a behavioral change noted? To what effect? What kinds of outcomes are noted?• Can this approach scale? – Beginning with engineering college USE Lab Digital Media Commons 28http://umich.edu/~uselab University of Michigan
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  47. 47. ATLAS Network Users Ecosystem Public NYU Portals Skins Single Video sign on sharing OpenSSO Kaltura ContentIdentity NetId and Digital password LDAP ATLAS Network eBooks library Grouper Echo360 SIS class Classroom rosters capture Pentaho Cassandra Scalable Analytics storage Data 12
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