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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2. Analytics:
More Than Data-Driven Decisions
Steven Lonn
Research Specialist
USE Lab, Digital Media Commons
www.umich.edu/~uselab
2

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
3
http://umich.edu/~uselab University of Michigan

4. What is Analytics?
+ +
USE Lab Digital Media Commons
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http://umich.edu/~uselab University of Michigan

5. Analytics in Our Lives
USE Lab Digital Media Commons
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http://umich.edu/~uselab University of Michigan

6. Analytics in Our Lives
USE Lab Digital Media Commons
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http://umich.edu/~uselab University of Michigan

7. Analytics in OAE!
USE Lab Digital Media Commons
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http://umich.edu/~uselab University of Michigan

8. Analytics in OAE!
USE Lab Digital Media Commons
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http://umich.edu/~uselab University of Michigan

9. Analytics in OAE!
USE Lab Digital Media Commons
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http://umich.edu/~uselab University of Michigan

10. Analytics in Our Work
USE Lab Digital Media Commons
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http://umich.edu/~uselab University of Michigan

11. Analytics in Our Work
USE Lab Digital Media Commons
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http://umich.edu/~uselab University of Michigan

12. Analytics in Our Work
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USE Lab Digital Media Commons
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http://umich.edu/~uselab University of Michigan

13. Data Collected at . .
What kind of data is
already available those
“in the know?”
USE Lab Digital Media Commons
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http://umich.edu/~uselab University of Michigan

14. Data Collected at . .
Admissions
USE Lab Digital Media Commons
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http://umich.edu/~uselab University of Michigan

15. Data Collected at . .
Demographics
USE Lab Digital Media Commons
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http://umich.edu/~uselab University of Michigan

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
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http://umich.edu/~uselab University of Michigan

17. Data Collected at . .
Other Places Data is Gathered...
USE Lab Digital Media Commons
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http://umich.edu/~uselab University of Michigan

18. Current Use of Data...
USE Lab Digital Media Commons
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http://umich.edu/~uselab University of Michigan

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
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http://umich.edu/~uselab University of Michigan

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
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http://umich.edu/~uselab University of Michigan

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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
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http://umich.edu/~uselab University of Michigan

23. Signals
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http://umich.edu/~uselab University of Michigan

24. “Check My Activity” Tool
• University of Maryland, Baltimore County
USE Lab Digital Media Commons
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http://umich.edu/~uselab University of Michigan

25. “Check My Activity” Tool
• University of Maryland, Baltimore County
USE Lab Digital Media Commons
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http://umich.edu/~uselab University of Michigan

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
20
http://umich.edu/~uselab University of Michigan

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
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http://umich.edu/~uselab University of Michigan

28. Our Project
• M-STEM Academy
– 50 Engineering students
per cohort
– Use Sakai data to better
inform mentor team
USE Lab Digital Media Commons
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http://umich.edu/~uselab University of Michigan

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
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http://umich.edu/~uselab University of Michigan

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http://umich.edu/~uselab University of Michigan

31. USE Lab Digital Media Commons
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http://umich.edu/~uselab University of Michigan

32. comp
USE Lab Digital Media Commons
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http://umich.edu/~uselab University of Michigan

33. Graphing!
USE Lab Digital Media Commons
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http://umich.edu/~uselab University of Michigan

34. Advanced Graphing
USE Lab Digital Media Commons
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http://umich.edu/~uselab University of Michigan

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
28
http://umich.edu/~uselab University of Michigan

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