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Tracking Changes in Students' Online Self-Regulated Learning Behaviors and Achievement Goals Using Trace Clustering
Michelle Taub1, Allison M Banzon1, Tom Zhang2
1Department of Learning Sciences and Educational Research, University of Central Florida, Orlando, FL, United States.
Frontiers in Psychology
|April 4, 2022
Summary
Students
Area of Science:
- Educational Psychology
- Online Learning
- STEM Education
Background:
- Online and blended learning require self-regulated learning (SRL), particularly in challenging STEM subjects like physics.
- SRL involves planning, goal-setting, monitoring, and reflection, crucial for academic success.
- Winne & Hadwin's COPES model describes SRL as a dynamic process influenced by factors like goal orientation and content difficulty.
Purpose of the Study:
- To investigate changes in students' SRL behavior and achievement goal orientation over time in an online introductory physics course.
- To analyze how SRL strategies evolve in response to changing course demands.
- To correlate SRL patterns with students' motivational profiles.
Main Methods:
- Employed trace clustering and process mining on clickstream data from 250 college students in an online physics course.
- Utilized the achievement goals questionnaire-revised (AGQ-R) to measure achievement goal orientation.
- Analyzed SRL behavior by generating and interpreting causal nets from clustered event traces, assessed across three course modules.
Main Results:
- Identified distinct SRL patterns (causal nets) adopted by students.
- Observed temporal shifts in SRL behavior and achievement goal orientation throughout the semester.
- Found associations between specific SRL causal nets and students' motivational profiles (AGQ-R responses).
Conclusions:
- SRL is a dynamic process that changes over time in online physics courses.
- Understanding temporal SRL dynamics and motivational factors is key to supporting student success.
- Findings offer guidance for designing online learning materials to foster SRL in diverse student populations.
Keywords:
achievement goal orientationonline learning modulesprocess miningself-regulated learningtrace clusteringMore Related Videos
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