Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

The use of Learning Analytics to explore sequences of self-regulated learning in moocs


Published on

my slides to the learning and student analytics symposium in Nancy France #LSAC2019

Published in: Education
  • Be the first to comment

The use of Learning Analytics to explore sequences of self-regulated learning in moocs

  1. 1. The Use of Learning Analytics to Explore Sequences of Self-Regulated Learning in MOOCs Dr. Mohammad Khalil LSAC 2019 Nancy, France 22.10.2019
  2. 2. Who am I? Mohammad Khalil @TUMohdKhalil
  3. 3. This extended abstract is based on a full study carried out and is now published in Computers & Education Journal For full reference: Wong, J., Khalil, M., Baars, M., de Koning, B., & Paas, F. (2019). Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course. Computers & Education, 140, 103595. 3
  4. 4. 4
  5. 5.
  6. 6. 6 No, they are not, But they are Evolving!
  7. 7. How learners study in MOOCs? 7
  8. 8. MOOC & Learners 8 ● MOOCs are autonomous ● MOOCs are designed linearly ● Students are free to pick their activities ● Students can skip any activity ● Therefore, learners should self-monitor their progress ● And, self-regulate their learning!!
  9. 9. The self-regulated learning theory 9
  10. 10. Self-Regulated Learning (SRL) SRL processes are seen as “the processes whereby students activate and sustain cognitions, affects, and behaviors that are systematically oriented toward the attainment of personal goals.” - Zimmerman & Schunk, 2011
  11. 11. Learning Analytics & MOOCs?? 11 Engagement
  12. 12. Mapping MOOC learner engagement (profiles) 12
  13. 13. So, where is the Learning Analytics potential with MOOCs and SRL? 14
  14. 14. “ … traces being behavioral manifestations of motivational, cognitive, and metacognitive events measure SRL more adequately than self-reports and think-aloud protocols - Winnie 15
  15. 15. How can we measure SRL using Learning Analytics? 16
  16. 16. LA & SRL 17 ● Frequency of the observed actions ○ e.g. the number of times a learner watched a video ● The transition state ○ e.g. the activity that a learner begins after ending the previous activity ● The sequence of transitions that regularly occurs ○ e.g. learners proceed to a discussion after viewing a video followed by completing a quiz Through: (Winnie, 2010)
  17. 17. The case study 18
  18. 18. ● MOOC: Serious Gaming MOOC, Erasmus Rotterdam University ● Participants: 655 learners ● Activities: video lectures, reading, quizzes, peer assignments, and discussion forums ● Time frame: 6 weeks
  19. 19. Intervention Design… 20
  20. 20. Forethought Monitoring Reflection
  21. 21. ● Participants: 655 learners, 222 with SRL prompts ● Two main cohorts: SRL prompt-viewers and non-viewers ● 103 learners were identified as active learners (i.e., learners who completed at least one activity). ● The 103 learners were further grouped as i) learners who watched at least one of the weekly SRL-prompt videos (SRL-prompt viewers, n = 39) and ii) learners who did not watch any of the SRL-prompt videos (SRL-prompt non-viewers, n = 64).
  22. 22. The Learning Analytics approach & results 23
  23. 23. How students interacted after the interventions?
  24. 24. How students interacted with all the activities? SRL-prompt viewers had: • More activities watched • Higher frequency • Participated in discussion prompts • Did more quizzes and assignments
  25. 25. SRL-Viewers SRL-non Viewers
  26. 26. What was the order of shopped activities when applying the SRL intervention?
  27. 27. Main messages 29
  28. 28. Conclusion 30 ● SRL is a broad and complex construct and supporting SRL in MOOCs is challenging as MOOC users are heterogenous. ● Learning Analytics afford opportunities to learners to exercise SRL. for researchers – to collect, measure and report data about learner, and their learning environments, and for teachers to help students to develop their SRL. ● Using Learning Analytics, we can evaluate SRL strategies and learners’ responsiveness.
  29. 29. Conclusion 31 ● There are differences in sequential patterns of learner activities between those who viewed the SRL-prompts and those who did not. ● SRL-prompt viewers tend to follow the structure of the course provided by the instructor and whereas this was less so in the group of SRL-prompt non-viewers. ● SRL-prompt viewers interacted with more course activities
  30. 30. 32 Thank you Mohammad Khalil @TUMohdKhalil Email: