From Study Tactics to Learning Strategies: An Analytical Method for Extracting Interpretable Representations

Autor/inn/en	Fincham, Ed; Gasevic, Dragan; Jovanovic, Jelena; Pardo, Abelardo
Titel	From Study Tactics to Learning Strategies: An Analytical Method for Extracting Interpretable Representations
Quelle	In: IEEE Transactions on Learning Technologies, 12 (2019) 1, S.59-72 (14 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Fincham, Ed) ORCID (Jovanovic, Jelena) ORCID (Pardo, Abelardo)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1939-1382
DOI	10.1109/TLT.2018.2823317
Schlagwörter	Study Habits; Learning Strategies; Independent Study; Educational Research; Data Collection; Data Analysis; Blended Learning; Feedback (Response); College Students; Engineering Education; Computer System Design; Foreign Countries; Integrated Learning Systems; Metacognition; Outcomes of Education; Active Learning; Australia + Suchen Sie Ihr Suchwort? Study behavior; Study behaviour; Studienverhalten; Learning methode; Learning techniques; Lernmethode; Lernstrategie; Selbststudium; Bildungsforschung; Pädagogische Forschung; Data capture; Datensammlung; Auswertung; Collegestudent; Ingenieurausbildung; Ausland; Meta cognitive ability; Meta-cognition; Metakognitive Fähigkeit; Metakognition; Lernleistung; Schulerfolg; Aktives Lernen; Australien
Abstract	Research into self-regulated learning has traditionally relied upon self-reported data. While there is a rich body of literature that has extracted invaluable information from such sources, it suffers from a number of shortcomings. For instance, it has been shown that surveys often provide insight into students' perceptions about learning rather than how students actually employ study tactics and learning strategies. Accordingly, recent research has sought to assess students' learning strategies and, by extension, their self-regulated learning via trace data collected from digital learning environments. A number of studies have amply demonstrated the ability of educational data mining and learning analytics methods to identify patterns indicative of learning strategies within trace log data. However, many of these methods are limited in their ability to describe and interpret differences between extracted latent representations at varying levels of granularity (for instance, in terms of the underlying data of student actions and behavior). To address this limitation, the present study proposes a new methodology whereby interpretable representations of student's self-regulating behavior are derived at two theoretically inspired levels: that of learning strategies, and the study tactics that compose them. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01