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.

1. LEARNING ANALYTICS IN HIGHER EDUCATION
PROMISING PRACTICES AND LESSONS LEARNED
Bodong Chen,
University of Minnesota
October 27, 2016, Manila, Philippines
@bod0ng

2. BODONG CHEN
Assistant Professor in Learning Technologies
University of Minnesota-Twin Cities
Research interests: online learning, learning analytics, CSCL,
knowledge building

3. Credit: Dreamtime.com

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. PRECISION AGRICULTURE
Credit: Airborne

6. AMAZON RECOMMENDATIONS

7. , U.S. Department of Homeland SecurityVisual Analytics Law Enforcement Toolkit (VALET)

8. Source

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. 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)

12. CAN ANY OF THESE PLAYERS AFFORD
NOT USING DATA?

13. WHAT I'M SEEING AS A PROFESSOR?

14. Buckingham Shum, S. (2012). . UNESCO Institute for
Information Technologies in Education.
UNESCO Policy Brief: Learning Analytics

15. AGENDA
A study of Australian universities
University of Minnesota
My Classrooms
Cross-cutting factors

16. 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

17. 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

18. OVERALL POSITIVE

19. 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)

20. PART 2: INITAITIVES AT MY UNIVERSITY

21. University-owned and directed consortium

22. GETTING PEOPLE INVOLVED!
Instructors
Students
Advisors
Administrators
Research Faculty
Credit: UMN LA Team

23. UMN LEARNING ANALYTICS
DATA
Student Information System
Learning Management Systems
Student Advising Systems
A COMMON DATA LAYER
ANALYTICS
Dashboards
Predictive engines

24. ONGOING UNIZIN PILOTS
Canvas LMS
Engage
Snapshot
...

25. 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

26. PART 3: AN EXPERIMENTATION IN MY CLASS

28. MY PEDAGOGICAL GOALS
Promote forum participation from students?
Help students become more aware and re ective
of their participation

29. SOCIOGRAM

30. WORDCLOUD

34. CROSS-CUTTING FACTORS
TO CONSIDER

35. 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)

36. 2. CONVERSATIONS
Among datasets
Among people
Between data and people
Across levels
Credits: , ,1 2 3

37. 3. CULTURAL SHIFT AND CAPACITY BUILDING
Data practices
Educator data literacy
Leadership structure
...

38. OPPORTUNITIES FOR OPEN UNIVERSITIES
Openness
Comprehensiveness of data
Unique local contexts
Cross-institution collaboration
. . .

39. THANK YOU!
chenbd@umn.edu
bodong.ch
@bod0ng
ACKNOWLEDGEMENT