Analyzing and Interpreting Students' Self-Regulated Learning Patterns Combining Time-Series Feature Extraction, Segmentation, and Clustering

Autor/inn/en	Zhang, Mingyan; Du, Xu; Hung, Jui-Long; Li, Hao; Liu, Mengfan; Tang, Hengtao
Titel	Analyzing and Interpreting Students' Self-Regulated Learning Patterns Combining Time-Series Feature Extraction, Segmentation, and Clustering
Quelle	In: Journal of Educational Computing Research, 60 (2022) 5, S.1130-1165 (36 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Zhang, Mingyan) ORCID (Hung, Jui-Long) ORCID (Tang, Hengtao)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0735-6331
DOI	10.1177/07356331211065097
Schlagwörter	Elementary Secondary Education; Students; Independent Study; Self Control; Self Management; Learning Processes; Cognitive Style; At Risk Students; Academic Achievement + Suchen Sie Ihr Suchwort? Student; Schüler; Schülerin; Studentin; Selbststudium; Selbstbeherrschung; Selbstmanagement; Learning process; Lernprozess; Cognitive styles; Kognitiver Stil; Schulleistung
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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01