Learning analytics and MOOCs: What have we learned so far and where to go?

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This document discusses learning analytics and MOOCs. It summarizes that feedback loops between students and instructors are weak in MOOCs. Learning analytics can be used to predict attrition and performance. Research has identified learner subpopulations in MOOCs. Better data collection and more robust methods are still needed to understand the process of learning beyond just counting and coding behaviors. Privacy and ethical considerations must also be addressed.

Education

Learning analytics and MOOCs:
What have we learned so far and where to go?
Dragan Gašević
@dgasevic
January 9, 2015

Feedback loops between
students and instructors
are weak/missing!
Hattie, J., & Timperley, H. (2007). The power of feedback. Review of educational research, 77(1), 81-112.

Blogs
Videos/slides
Mobile
Search
Educators
Learners
Networks
MOOC Platform

Blogs
Mobile
Search
Networks
Educators
Learners
MOOC Platform
Videos/slides

Prediction of
attrition and performance
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).

Learner subpopulations in MOOCs
Kizilcec, R. F., Piech, C., & Schneider, E. (2013). Deconstructing disengagement: analyzing learner subpopulations
in massive open online courses. In Proceedings of the Third International Conference on Learning Analytics and
Knowledge (pp. 170-179). ACM.

Mainstream media about MOOCs
Kovanović, V., Joksimović, S., Gašević, D., Siemens, G., & Hatala, M. (2014). What public media reveals about MOOCs? Submitted
for Publication to British Journal of Educational Technology.
Dow Jones & Company’s Factiva: 3,958 articles (Jan 2012 – Jun 2014)

What do researchers plan?
Gašević, D., Joksimović, S., Kovanović, V., G. Siemens (2014). Where is research on massive open online courses headed? A data
analysis of the MOOC Research Initiative. The International Review of Research in Open and Distance Learning, 15(5), 134-176.

Is this is all
learning analytics can offer?

Reich, J. (2015). Rebooting MOOC research - Improve assessment, data sharing, and experimental
design. Science. 347(6217), 30-31, http://bit.ly/1s3b5kS

Counts don’t count much if
decontextualized
Wilson, T.D. (1999). Models in information behaviour research. Journal of Documentation, 55(3), 249 – 270.

Better data collection is needed
Gašević, D., Dawson, S., Siemens, G. (2015). Let's not forget: Learning analytics are about learning.
TechTrends, 59(1), 64-71, https://bit.ly/techtrends15

Analytics to account for
conditions, operations,
products, evaluation, and
standards (COPES)
Gašević, D., Dawson, S., Siemens, G. (2015). Let's not forget: Learning analytics are about learning.
TechTrends, 59(1), 64-71, https://bit.ly/techtrends15

Scaling up qualitative analysis
Text analysis and mining
Yang, D., Wen, M., Kumar, A., Xing, E. P., & Rose, C. P. (2014). Towards an integration of text and graph clustering
methods as a lens for studying social interaction in MOOCs. The International Review of Research in Open and
Distributed Learning, 15(5), 214-234.

Process nature of learning
- beyond coding and counting -

Life-long and ubiquitous
learner profiles

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.

Visualizations can be useful
if embedded into and fit learning tasks
if based on and support COPES

Learning activities don’t happen in
a single platform

More robust methods and
data sharing approaches
Reich, J. (2015). Rebooting MOOC research - Improve assessment, data sharing, and experimental
design. Science. 347(6217), 30-31, http://bit.ly/1s3b5kS

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Opportunities to facilitate learning on the Internet are widely recognized across subject matters, levels of education and situations ranging from extending one’s hobbies to life-long learning relating to workers’ changing roles in the workplace. However, information available in the Internet, even in formal academic courses, is rarely presented using empirically proven findings from the learning sciences. Often, learners are left “on their own” to figure out which tactics work best for them in seeking and understanding information, and studying to learn it. Given that most learners have weak skills in these areas and in self-regulating learning, this sets a stage for major failures in sensemaking and learning that can have dire societal consequences. On the other hand, there are open issues with the existing (a) tools that are typically designed for a hypothetical but factually non-existent “average” user; and (b) methods that are too often based on self-reports (e.g., questionnaires) that are insufficient to advance research on sensemaking and complex learning processes that involve dynamic feedback loops. This talk (i) discusses results of several studies, in which we have addressed the above challenges, and (ii) outlines promising research topics that spans across the three main research cornerstones – computational, socio-cognitive, and user-centered design.

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Learning analytics and MOOCs: What have we learned so far and where to go?

1. Learning analytics and MOOCs: What have we learned so far and where to go? Dragan Gašević @dgasevic January 9, 2015

2. Learning at scale MOOCs innovation

3. Feedback loops between students and instructors are weak/missing! Hattie, J., & Timperley, H. (2007). The power of feedback. Review of educational research, 77(1), 81-112.

4. LEARNING ANALYTICS

5. MOOC Platform Educators Learners

6. Blogs Videos/slides Mobile Search Educators Learners Networks MOOC Platform

7. Blogs Mobile Search Networks Educators Learners MOOC Platform Videos/slides

8. Prediction of attrition and performance 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).

9. Learner subpopulations in MOOCs Kizilcec, R. F., Piech, C., & Schneider, E. (2013). Deconstructing disengagement: analyzing learner subpopulations in massive open online courses. In Proceedings of the Third International Conference on Learning Analytics and Knowledge (pp. 170-179). ACM.

10. Mainstream media about MOOCs Kovanović, V., Joksimović, S., Gašević, D., Siemens, G., & Hatala, M. (2014). What public media reveals about MOOCs? Submitted for Publication to British Journal of Educational Technology. Dow Jones & Company’s Factiva: 3,958 articles (Jan 2012 – Jun 2014)

11. What do researchers plan? Gašević, D., Joksimović, S., Kovanović, V., G. Siemens (2014). Where is research on massive open online courses headed? A data analysis of the MOOC Research Initiative. The International Review of Research in Open and Distance Learning, 15(5), 134-176.

12. Is this is all learning analytics can offer?

13. DIRECTIONS

14. Reich, J. (2015). Rebooting MOOC research - Improve assessment, data sharing, and experimental design. Science. 347(6217), 30-31, http://bit.ly/1s3b5kS

15. Counts don’t count much if decontextualized Wilson, T.D. (1999). Models in information behaviour research. Journal of Documentation, 55(3), 249 – 270.

16. Better data collection is needed Gašević, D., Dawson, S., Siemens, G. (2015). Let's not forget: Learning analytics are about learning. TechTrends, 59(1), 64-71, https://bit.ly/techtrends15

17. Analytics to account for conditions, operations, products, evaluation, and standards (COPES) Gašević, D., Dawson, S., Siemens, G. (2015). Let's not forget: Learning analytics are about learning. TechTrends, 59(1), 64-71, https://bit.ly/techtrends15

18. Scaling up qualitative analysis Text analysis and mining Yang, D., Wen, M., Kumar, A., Xing, E. P., & Rose, C. P. (2014). Towards an integration of text and graph clustering methods as a lens for studying social interaction in MOOCs. The International Review of Research in Open and Distributed Learning, 15(5), 214-234.

19. Process nature of learning - beyond coding and counting -

20. Life-long and ubiquitous learner profiles

21. 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.

22. Visualizations can be useful if embedded into and fit learning tasks if based on and support COPES

23. LOCO-Analyst

24. Learning activities don’t happen in a single platform

25. Ethical and privacy consideration

26. More robust methods and data sharing approaches Reich, J. (2015). Rebooting MOOC research - Improve assessment, data sharing, and experimental design. Science. 347(6217), 30-31, http://bit.ly/1s3b5kS

27. Thank you!

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