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The Use of Learning Analytics to Explore
Sequences of Self-Regulated Learning in
MOOCs
Dr. Mohammad Khalil
LSAC 2019
Nancy, France
22.10.2019
Who am I?
Mohammad Khalil
@TUMohdKhalil
http://mohdkhalil.wordpress.com
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
https://www.classcentral.com/moocs-year-in-review-2018
6
No, they are not, But they are Evolving!
How learners study in MOOCs?
7
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!!
The self-regulated learning theory
9
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
Learning Analytics & MOOCs??
11
Engagement
Mapping MOOC learner engagement (profiles)
12
So, where is the Learning Analytics potential with
MOOCs and SRL?
14
“
… traces being behavioral manifestations of motivational,
cognitive, and metacognitive events measure SRL more
adequately than self-reports and think-aloud protocols - Winnie
15
How can we measure SRL using Learning Analytics?
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)
The case study
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
Intervention Design…
20
Forethought
Monitoring
Reflection
● 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).
The Learning Analytics approach & results
23
How students interacted after
the interventions?
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
SRL-Viewers
SRL-non Viewers
What was the order of shopped
activities when applying the
SRL intervention?
Main messages
29
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.
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
32
Thank you
Mohammad Khalil
@TUMohdKhalil
Email: mohammad.khalil@uib.no

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

  • 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. Who am I? Mohammad Khalil @TUMohdKhalil http://mohdkhalil.wordpress.com
  • 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
  • 6. 6 No, they are not, But they are Evolving!
  • 7. How learners study in MOOCs? 7
  • 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!!
  • 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. Learning Analytics & MOOCs?? 11 Engagement
  • 12. Mapping MOOC learner engagement (profiles) 12
  • 13.
  • 14. So, where is the Learning Analytics potential with MOOCs and SRL? 14
  • 15. “ … traces being behavioral manifestations of motivational, cognitive, and metacognitive events measure SRL more adequately than self-reports and think-aloud protocols - Winnie 15
  • 16. How can we measure SRL using Learning Analytics? 16
  • 17. 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)
  • 19. ● MOOC: Serious Gaming MOOC, Erasmus Rotterdam University ● Participants: 655 learners ● Activities: video lectures, reading, quizzes, peer assignments, and discussion forums ● Time frame: 6 weeks
  • 22. ● 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).
  • 23. The Learning Analytics approach & results 23
  • 24.
  • 25. How students interacted after the interventions?
  • 26. 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
  • 28. What was the order of shopped activities when applying the SRL intervention?
  • 30. 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.
  • 31. 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