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

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Who am I?
Mohammad Khalil
@TUMohdKhalil
http://mohdkhalil.wordpress.com

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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.
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https://www.classcentral.com/moocs-year-in-review-2018

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No, they are not, But they are Evolving!

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How learners study in MOOCs?
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MOOC & Learners
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● 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!!

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The self-regulated learning theory
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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

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Learning Analytics & MOOCs??
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Engagement

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Mapping MOOC learner engagement (profiles)
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So, where is the Learning Analytics potential with
MOOCs and SRL?
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“
… traces being behavioral manifestations of motivational,
cognitive, and metacognitive events measure SRL more
adequately than self-reports and think-aloud protocols - Winnie
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How can we measure SRL using Learning Analytics?
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LA & SRL
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● 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)

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The case study
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● MOOC: Serious Gaming MOOC, Erasmus Rotterdam University
● Participants: 655 learners
● Activities: video lectures, reading, quizzes, peer assignments, and discussion forums
● Time frame: 6 weeks

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Intervention Design…
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Forethought
Monitoring
Reflection

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● 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).

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The Learning Analytics approach & results
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How students interacted after
the interventions?

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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

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SRL-Viewers
SRL-non Viewers

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What was the order of shopped
activities when applying the
SRL intervention?

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Main messages
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Conclusion
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● 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.

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Conclusion
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● 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

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Thank you
Mohammad Khalil
@TUMohdKhalil
Email: mohammad.khalil@uib.no