Inferring Learners' Affinities from Course Interaction Data

Autor/in	Osipenko, Maria
Titel	Inferring Learners' Affinities from Course Interaction Data
Quelle	In: Education and Information Technologies, 27 (2022) 4, S.5717-5736 (20 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1360-2357
DOI	10.1007/s10639-021-10833-4
Schlagwörter	Behavior Patterns; Models; Undergraduate Students; Preferences; Student Behavior; Learning Processes; Interaction + Suchen Sie Ihr Suchwort? Analogiemodell; Student behaviour; Schülerverhalten; Learning process; Lernprozess; Interaktion
Abstract	A data-driven model where individual learning behavior is a linear combination of certain stylized learning patterns scaled by learners' affinities is proposed. The absorption of stylized behavior through the affinities constitutes "building blocks" in the model. Non-negative matrix factorization is employed to extract common learning patterns and their affinities from online learning data ensuring meaningful non-negativity of the result. The empirical learning patterns resulting from the actual course interaction data of 111 undergraduate university students are connected to a learning style system. Bootstrap-based inference allows to check the significance of the pattern coefficients. Dividing the learners in two groups "failed" and "passed" and considering their mean affinities leads to a bootstrap-based test on whether the course structure is well balanced regarding the learning preferences. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01