Disengagement Detection in Online Learning: Validation Studies and Perspectives

Autor/inn/en	Cocea, M.; Weibelzahl, S.
Titel	Disengagement Detection in Online Learning: Validation Studies and Perspectives
Quelle	In: IEEE Transactions on Learning Technologies, 4 (2011) 2, S.114-124 (11 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1939-1382
DOI	10.1109/TLT.2010.14
Schlagwörter	Prediction; Electronic Learning; Online Courses; Delivery Systems; Program Validation; Learner Engagement; Learning Motivation; Motivation Techniques; Learning Processes; Predictor Variables; Performance Factors; Data Processing; Computer Managed Instruction; Courseware; Educational Environment; Achievement Need; Data Analysis; Educational Technology; Educational Practices; Best Practices; Psychological Characteristics + Suchen Sie Ihr Suchwort? Vorhersage; Online course; Online-Kurs; Auslieferung; Motivation for studies; Lernmotivation; Motivationsförderung; Learning process; Lernprozess; Prädiktor; Leistungsindikator; Datenverarbeitung; Computer-assisted instruction; Computerunterstützter Unterricht; Lernsoftware; Lernumgebung; Pädagogische Umwelt; Schulumwelt; Auswertung; Unterrichtsmedien; Bildungspraxis
Abstract	Learning environments aim to deliver efficacious instruction, but rarely take into consideration the motivational factors involved in the learning process. However, motivational aspects like engagement play an important role in effective learning-engaged learners gain more. E-Learning systems could be improved by tracking students' disengagement that, in turn, would allow personalized interventions at appropriate times in order to reengage students. This idea has been exploited several times for Intelligent Tutoring Systems, but not yet in other types of learning environments that are less structured. To address this gap, our research looks at online learning-content-delivery systems using educational data mining techniques. Previously, several attributes relevant for disengagement prediction were identified by means of log-file analysis on HTML-Tutor, a web-based learning environment. In this paper, we investigate the extendibility of our approach to other systems by studying the relevance of these attributes for predicting disengagement in a different e-learning system. To this end, two validation studies were conducted indicating that the previously identified attributes are pertinent for disengagement prediction, and two new meta-attributes derived from log-data observations improve prediction and may potentially be used for automatic log-file annotation. (Contains 10 tables and 3 figures.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10