Prediction of Students' Early Dropout Based on Their Interaction Logs in Online Learning Environment

Autor/inn/en	Mubarak, Ahmed A.; Cao, Han; Zhang, Weizhen
Titel	Prediction of Students' Early Dropout Based on Their Interaction Logs in Online Learning Environment
Quelle	In: Interactive Learning Environments, 30 (2022) 8, S.1414-1433 (20 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Mubarak, Ahmed A.) ORCID (Cao, Han) ORCID (Zhang, Weizhen)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1049-4820
DOI	10.1080/10494820.2020.1727529
Schlagwörter	Prediction; Dropouts; Interaction; Learning Analytics; Online Courses; Educational Environment; College Students; Dropout Prevention; Learner Engagement; Models; Reader Text Relationship; Academic Achievement + Suchen Sie Ihr Suchwort? Vorhersage; Drop-out; Drop-outs; Dropout; Early leavers; Schulversagen; Interaktion; Online course; Online-Kurs; Lernumgebung; Pädagogische Umwelt; Schulumwelt; Collegestudent; Analogiemodell; Schulleistung
Abstract	Online learning has become more popular in higher education since it adds convenience and flexibility to students' schedule. But, it has faced difficulties in the retention of the continuity of students and ensure continual growth in course. Dropout is a concerning factor in online course continuity. Therefore, it has sparked great interest among educators and researchers to offer many models and strategies to reduce online course students' dropout by analyzing students' behavior and their individual and academic information. However, online education platforms still face challenges of high dropout rate and the difficulty of accurate prediction to reduce it. The key aim of the present study is to construct a predictive model to early predict students who are at-risk of dropout. This model is useful to the course instructors to make effective and timely interventions. We have noticed that dropout prediction is basically a sequence labeling or time series prediction problem. For these reasons, we proposed two models; the Logistic Regression by adding a regularization term and the Input-Output Hidden Markov Model (IOHMM). Results showed that the proposed models achieved an accuracy of 84% compared to the baseline of Machine Learning Models for prediction of the students at-risk of dropping out. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01