Analyzing and Interpreting Students’ Self-regulated Learning Patterns Combining Time-series Feature Extraction,Segmentation,and Clustering

Abstract

In online learning, students’ learning behavior might change as the course progresses. How students adjust learning behaviors aligned with course requirements reflects their self-regulated learning strategies. Analyzing students’ learning patterns can help instructors understand how the course design or activities shape students’ learning behaviors, including their learning beliefs and motivation, and facilitate teaching decision makings accordingly. This study aims to propose a scientific analytic method to understand students’ self-regulated learning (SRL) patterns. The whole process includes the following four steps: (1) encoding behavioral patterns; (2) detecting turning points and chunking behavioral patterns; (3) grouping similar patterns; and (4) interpreting results. A case study with 4604 K-12 students from 476 courses was conducted to validate the proposed method. Five successful patterns, three at-risk patterns, and three average patterns were identified. The case study indicated that successful students showed at least one of the following characteristics: (1) Balanced, (2) Proactive and Balanced, and (3) Balanced with one highly engaged behavior. The at-risk students showed the following characteristics: (1) Oscillatory and (2) Low Engaged. Patterns which led to successful or at-risk conditions are compared and connected with corresponding SRL strategies. Practical and research implications are discussed in the article as well.

Keywords

Long Short-Term Memory autoencoder learning pattern time-series turning points learning performance

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