Literaturnachweis - Detailanzeige
| Autor/inn/en | Baneres, David; Rodriguez-Gonzalez, M. Elena; Guerrero-Roldan, Ana Elena |
|---|---|
| Titel | A Real-Time Predictive Model for Identifying Course Dropout in Online Higher Education |
| Quelle | In: IEEE Transactions on Learning Technologies, 16 (2023) 4, S.484-499 (16 Seiten)Infoseite zur Zeitschrift
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| Zusatzinformation | ORCID (Baneres, David) ORCID (Rodriguez-Gonzalez, M. Elena) ORCID (Guerrero-Roldan, Ana Elena) |
| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Zeitschriftenaufsatz |
| DOI | 10.1109/TLT.2023.3267275 |
| Schlagwörter | Prediction; Models; Identification; Potential Dropouts; Dropout Prevention; Online Courses; Higher Education; Probability; Intervention; At Risk Students; College Freshmen; Accuracy |
| Abstract | Course dropout is a concern in online higher education, mainly in first-year courses when different factors negatively influence the learners' engagement leading to an unsuccessful outcome or even dropping out from the university. The early identification of such potential at-risk learners is the key to intervening and trying to help them before they decide to drop out. This article focuses on this challenging problem by providing a predictive dropout model with distinctive characteristics from previous approaches. First, the identification is in real time by providing a daily dropout prediction. Second, a temporal window of variable size is defined to evaluate the likelihood of being a dropout learner at the activity level. Such contributions will serve as a basis for designing and applying intervention mechanisms to reverse the course dropout at-risk situation. The predictive model and the temporal window have been evaluated on data from an authentic online learning setting in two first-year undergraduate courses. We show the accuracy of correctly identifying at-risk learners within activities and the model performance to detect actual course dropout learners. (As Provided). |
| Anmerkungen | Institute of Electrical and Electronics Engineers, Inc. 445 Hoes Lane, Piscataway, NJ 08854. Tel: 732-981-0060; Web site: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4620076 |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2024/1/01 |
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