1. Learning analytics
are more than a technology
Dragan Gašević
@dgasevic
October 20, 2015
ELearn 2015, Kona, HI

2. Educational Landscape Today
“Non-traditional” students
Large classrooms
Long time to complete degrees
Long waiting lists

3. Learning at scale

4. Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., & Wenderoth, M. P. (2014). Active
learning increases student performance in science, engineering, and mathematics. Proceedings of the National
Academy of Sciences, 201319030.

5. Feedback loops between
students and instructors
are missing/weak!

6. LEARNING ANALYTICS

7. Learning
environment
Educators
Learners
Student
Information
Systems

8. Blogs
Videos/slides
Mobile
Search
Educators
Learners
Networks
Student
Information
Systems
Learning
environment

9. Blogs
Mobile
Search
Networks
Educators
Learners
Student
Information
Systems
Learning
environment
Videos/slides

10. Learning Analytics – What?
Measurement, collection,
analysis, and reporting of data about
learners and their contexts

11. Learning Analytics – Why?
Understanding and optimising
learning and the environments
in which learning occurs

12. CASE STUDIES

13. Student retention
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
100.00%
Year 1 Year 2 Year 3 Year 4
Course Signals
No Course Signals
Arnold, K. E., & Pistilli, M. D. (2012, April). Course Signals at Purdue: Using learning analytics to increase student
success. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (pp. 267-270).

14. Tanes, Z., Arnold, K. E., King, A. S., & Remnet, M. A. (2011). Using Signals for appropriate feedback: Perceptions and
practices. Computers & Education, 57(4), 2414-2422.
Can teaching be improved?

15. Wright, M. C., McKay, T., Hershock, C., Miller, K., & Tritz, J. (2014). Better Than Expected: Using Learning Analytics to
Promote Student Success in Gateway Science. Change: The Magazine of Higher Learning, 46(1), 28-34.

16. INSTITUTIONAL ADOPTION:
CURRENT STATE

17. Very few institution-wide
examples of adoption

18. Sophistication model
Siemens, G., Dawson, S., & Lynch, G. (2014). Improving the Quality and Productivity of the Higher Education Sector -
Policy and Strategy for Systems-Level Deployment of Learning Analytics. Canberra, Australia: Office of Learning and
Teaching, Australian Government. Retrieved from http://solaresearch.org/Policy_Strategy_Analytics.pdf

19. Sophistication model
Siemens, G., Dawson, S., & Lynch, G. (2014). Improving the Quality and Productivity of the Higher Education Sector -
Policy and Strategy for Systems-Level Deployment of Learning Analytics. Canberra, Australia: Office of Learning and
Teaching, Australian Government. Retrieved from http://solaresearch.org/Policy_Strategy_Analytics.pdf

20. Interest in analytics is high, but
many institutions had yet to make progress
beyond basic reporting
Bichsel, J. (2012). Analytics in higher education: Benefits, barriers, progress, and recommendations. EDUCAUSE
Center for Applied Research.

21. What’s necessary to
move forward?

22. DIRECTIONS

23. Data – Model – Transform
Barton, D., & Court, D. (Oct 2012). Making Advanced Analytics Work for You. Harvard Business Review, 79-83,
https://hbr.org/2012/10/making-advanced-analytics-work-for-you/ar/1

24. Data – Model – Transform
Creative data sourcing
Barton, D., & Court, D. (Oct 2012). Making Advanced Analytics Work for You. Harvard Business Review, 79-83,
https://hbr.org/2012/10/making-advanced-analytics-work-for-you/ar/1

25. Social networks are everywhere
Gašević, D., Zouaq, A., Jenzen, R. (2013). ‘Choose your Classmates, your GPA is at Stake!’ The Association of Cross-
Class Social Ties and Academic Performance. American Behavioral Scientist, 57(10), 1459–1478.

26. Data – Model – Transform
Creative data sourcing
Necessary IT support
Barton, D., & Court, D. (Oct 2012). Making Advanced Analytics Work for You. Harvard Business Review, 79-83,
https://hbr.org/2012/10/making-advanced-analytics-work-for-you/ar/1

27. Awareness of limitations and
challenging assumptions
Kovanović, V., Gašević, D., Dawson, S., Joksimović, S., Baker, R. (in press). Does Time-on-task Estimation Matter?
Implications on Validity of Learning Analytics Findings. Journal of Learning Analytics

28. Data – Model – Transform
Question-driven, not data-driven
Barton, D., & Court, D. (Oct 2012). Making Advanced Analytics Work for You. Harvard Business Review, 79-83,
https://hbr.org/2012/10/making-advanced-analytics-work-for-you/ar/1

30. Learning analytics is about
learning
Gašević, D., Dawson, S., Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends,
59(1), 64-71.

31. Once size fits all does not work
in learning analytics

32. Gašević, D., Dawson, S., Rogers, T., Gašević, D. (in press). Learning analytics should not promote one size fits all: The
effects of course-specific technology use in predicting academic success. The Internet and Higher Education.
Learning context
Instructional conditions shape
learning analytics results

33. Learner agency
Kovanović, V., Gašević, D., Joksimović, S., Hatala, M., Adesope, S. (2015). Analytics of Communities of Inquiry: Effects
of Learning Technology Use on Cognitive Presence in Asynchronous Online Discussions. The Internet and Higher
Education, 27, 74–89.
More time online does not
always mean better learning

34. Data – Model – Transform
Participatory design of analytics tools
Barton, D., & Court, D. (Oct 2012). Making Advanced Analytics Work for You. Harvard Business Review, 79-83,
https://hbr.org/2012/10/making-advanced-analytics-work-for-you/ar/1

35. Data – Model – Transform
Participatory design of analytics tools
Analytics tools for non-statistics experts
Barton, D., & Court, D. (Oct 2012). Making Advanced Analytics Work for You. Harvard Business Review, 79-83,
https://hbr.org/2012/10/making-advanced-analytics-work-for-you/ar/1

36. Visualizations can be harmful
Corrin, L., & de Barba, P. (2014). Exploring students’ interpretation of feedback delivered through learning
analytics dashboards. In Proceedings of the ascilite 2014 conference (pp. 629-633). ascilite.

37. Data – Model – Transform
Participatory design of analytics tools
Analytics tools for non-statistics experts
Develop capabilities to exploit (big) data
Barton, D., & Court, D. (Oct 2012). Making Advanced Analytics Work for You. Harvard Business Review, 79-83,
https://hbr.org/2012/10/making-advanced-analytics-work-for-you/ar/1

38. Marr, B. (Oct 2015). Forget Data Scientists - Make Everyone Data Savvy,
http://www.datasciencecentral.com/m/blogpost?id=6448529%3ABlogPost%3A337288

39. Are we ready to act on analytics?

40. What to do if we detect
deficit models in our practice?
Are we ready to act on analytics?
Joksimović, S., Gašević, D., Loughin, T. M., Kovanović, V., Hatala, M. (2015). Learning at distance: Effects of
interaction traces on academic achievement. Computers & Education, 87, 204–217

41. How do we deal with
performance-oriented culture?
Are we ready to act on analytics?
Jovanović, J., Pardo, A., Gašević, D., Dawson, S., Mirriahi, N. (2015). Dynamic analytics of learning in flipped
classrooms. Manuscript in preparation.

42. What’s our adoption reality?

43. CHALLENGES

44. Current state
Benchmarking learning analytics status, policy and
practices for Australian universities

45. Senior management perspective

46. Senior management perspective

47. Solution-driven approach
Bought an analytics product.
Analytics box ticked!

48. Lack of data-informed
decision making culture
Macfadyen, L., & Dawson, S. (2012). Numbers Are Not Enough. Why e-Learning Analytics Failed to Inform an
Institutional Strategic Plan. Educational Technology & Society, 15(3), 149-163.

49. Researchers not focused on
scalability

50. LA idealized systems model
Colvin, C., Rogers, T., Wade, A., Dawson, S., Gasevic, D., Buckingham Shum, S., Nelson, K., Alexander, S., Lockyer, L.,
Kennedy, G., Corrin, L., Fisher, J. (2015). Student retention and learning analytics: A snapshot of Australian practices
and a framework for advancement. Australian Goverement’s Office for Learning and Teaching.

51. FINAL REMARKS

52. Embracing complexity of
educational systems

53. Capacity development
Multidisciplinary teams in
institutions critical

54. Ethical and privacy consideration
Development of data privacy agency
Prinsloo, P., & Slade, S. (2015). Student privacy self-management: implications for learning analytics. In Proceedings of
the Fifth International Conference on Learning Analytics And Knowledge (pp. 83-92). ACM.

55. Sclater, N. (2014). Code of practice for learning analytics: A literature review of the ethical and legal issues.
http://repository.jisc.ac.uk/5661/1/Learning_Analytics_A-_Literature_Review.pdf

56. Development of
analytics culture
Manyika, J. et al. (2011). Big Data: The Next Frontier for Innovation, Competition, and Productivity. McKinsey
Global Institute, http://goo.gl/Lue3qs

58. Mahalo!