Prediction of Learners' Dropout in E-Learning Based on the Unusual Behaviors

Autor/inn/en	Zhou, Yizhuo; Zhao, Jin; Zhang, Jianjun
Titel	Prediction of Learners' Dropout in E-Learning Based on the Unusual Behaviors
Quelle	In: Interactive Learning Environments, 31 (2023) 3, S.1796-1820 (25 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Zhou, Yizhuo) ORCID (Zhao, Jin) ORCID (Zhang, Jianjun)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1049-4820
DOI	10.1080/10494820.2020.1857788
Schlagwörter	Electronic Learning; Prediction; Dropouts; Student Behavior; Learning Management Systems; Models; Dropout Research; Foreign Countries; China + Suchen Sie Ihr Suchwort? Vorhersage; Drop-out; Drop-outs; Dropout; Early leavers; Schulversagen; Student behaviour; Schülerverhalten; Analogiemodell; Ausland
Abstract	On e-learning platforms, most e-learners didn't complete the course successfully. It means that reducing dropout is a critical problem for the sustainability of e-learning. This paper aims to establish a predictive model to describe e-learners' dropout behavior, which can help the commercial e-learning platforms to make appropriate interventions and incentives. First of all, we defined the features of unusual learning behaviors in commercial e-learning platform, and used the Cox proportional hazard model of survival analysis to select variables that can reasonably predict dropout possibilities. Results show that there are six variables which have significant influence on dropout behavior: dropout history, number of watched videos, number of progress bar operation, number of test questions operation, number of weeks that the login frequency is higher than average, and payment status. We also proposed cumulative gain, predicted retention number and predicted dropout learner number in next period, to evaluate the application ability of the predictive model. Finally, we performed an empirical analysis and verified the predictive effectiveness. The further application of the predictive model also shows that it can help the managers of e-learning platforms to adjust their strategy to improve the retention rate of potential lost learners. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01