Measurement of Self-regulated Learning: Strategies for mapping trace data to learning processes and downstream analysis implications
Author:
Affiliation:
1. Monash University, Clayton, Victoria, Australia
2. Peking University, Beijing, China
3. Radboud University, Nijmegen, Netherlands
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3636555.3636915
Reference41 articles.
1. Roger Azevedo, Jason Harley, Gregory Trevors, Melissa Duffy, Reza Feyzi-Behnagh, François Bouchet, and Ronald Landis. 2013. Using Trace Data to Examine the Complex Roles of Cognitive, Metacognitive, and Emotional Self Regulatory Processes During Learning with Multi-agent Systems. In International Handbook of Metacognition and Learning Technologies, Roger Azevedo and Vincent Aleven (Eds.). Springer, New York, NY, 427–449.
2. The benefits and caveats of using clickstream data to understand student self-regulatory behaviors: opening the black box of learning processes
3. Process mining techniques for analysing patterns and strategies in students’ self-regulated learning
4. Feedback and Self-Regulated Learning: A Theoretical Synthesis
5. The Relation Between Self-Regulated Learning and Academic Achievement Across Childhood and Adolescence: A Meta-Analysis
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