Self-report and traces of blended SRL
In a blended computer-systems course, self-report and LMS-trace clustering both revealed coherent SRL profiles that aligned with online activity and course performance.
Source
Self-reported and digital-trace measures of computer science students' self-regulated learning in blended course designs
Study at a glance
- Design
- Cross-sectional — Self-report vs digital-trace SRL in a first-year computer-systems course
- N
- N=145 · 108 men, 37 women; Australian metropolitan university
- Population
- First-year computer-science undergraduates
- Outcome
- Agreement of self-reported and digital-trace measures of self-regulated learning
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Self-report and traces of blended SRL
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145 computer science undergraduates participated.
The participants were 145 computer science undergraduates, of whom 108 were male (74.5%) and 37 were female (25.5%).
What course context was studied?
They were enrolled in a first-year compulsory course – “computer systems”
What they did
145 first-year computer science undergraduates in a 13-week blended “computer systems” course completed self-regulated learning surveys; researchers also used LMS digital traces and clustered students using both data sources.
What they found
Clusters were somewhat consistent across methods. Students reporting higher efficacy, higher intrinsic goals, lower anxiety, and better strategies were also more active online and scored higher; poorer self-reported SRL aligned with less online engagement and lower performance.
The limits
What it doesn't show
Correlational clustering cannot prove that changing SRL strategies causes higher grades, and findings are from one compulsory CS course.
Key terms
- Self-regulated learning (SRL)
- Learners’ planning, monitoring, motivation, and strategy use to guide their own learning.
- Digital-trace measures
- LMS log indicators of online engagement and activity.
- Self-reported measures
- Questionnaire ratings of efficacy, goals, anxiety, and strategies.
- Blended course
- Course combining face-to-face and compulsory online learning components.
- Learning analytics
- Analysis of digital learning data to understand engagement and outcomes.
- Cluster analysis
- Grouping students with similar SRL or engagement profiles.
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What dual measurement approach did the study use?
Common questions
Who was studied?
145 CS undergraduates in a first-year computer systems course.
What two measurement types were compared?
Self-report SRL questionnaires and LMS digital traces.
Did the two methods tell a similar story?
Authors report a certain level of consistency between self-report and trace-based clusters.
How did stronger SRL profiles relate to outcomes?
Higher online activity and higher course performance.
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