Dropout Prediction and Decision Feedback Supported by Multi Temporal Sequences of Learning Behavior in MOOCs

Autor/inn/en	Xia, Xiaona; Qi, Wanxue
Titel	Dropout Prediction and Decision Feedback Supported by Multi Temporal Sequences of Learning Behavior in MOOCs
Quelle	In: International Journal of Educational Technology in Higher Education, 20 (2023), Artikel 32 (25 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Xia, Xiaona)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
DOI	10.1186/s41239-023-00400-x
Schlagwörter	MOOCs; Dropouts; Prediction; Decision Making; Feedback (Response); Models; Student Behavior; Learning Processes; Learning Analytics + Suchen Sie Ihr Suchwort? Drop-out; Drop-outs; Dropout; Early leavers; Schulversagen; Vorhersage; Decision-making; Entscheidungsfindung; Analogiemodell; Student behaviour; Schülerverhalten; Learning process; Lernprozess
Abstract	The temporal sequence of learning behavior is multidimensional and continuous in MOOCs. On the one hand, it supports personalized learning methods, achieves flexible time and space. On the other hand, it also makes MOOCs produce a large number of dropouts and incomplete learning behaviors. Dropout prediction and decision feedback have become an important issue of MOOCs. This study carries out sufficient method design and decision analysis on the dropout trend. Based on a large number of learning behavior instances, we construct a multi behavior type association framework, design dropout prediction model to analyze the temporal sequence of learning behavior, then discuss the corresponding intervention measures, in order to provide adaptive monitoring mechanism for long-term tracking and short-term learning method selection, and enable adaptive decision feedback. the full experiment shows that the designed model might improve the performance of the dropout prediction, which achieves the reliability and feasibility. The whole research can provide key technical solution and decision, which has important theoretical and practical value for dropout research of MOOCs. (As Provided).
Anmerkungen	BioMed Central, Ltd. Available from: Springer Nature. 233 Spring Street, New York, NY 10013. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-348-4505; e-mail: customerservice@springernature.com; Web site: https://www.springer.com/gp/biomedical-sciences
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01