Designing a Transferable Predictive Model for Online Learning Using a Bayesian Updating Approach

Autor/inn/en	Xing, Wanli; Du, Dongping; Bakhshi, Ali; Chiu, Kuo-Chun; Du, Hanxiang
Titel	Designing a Transferable Predictive Model for Online Learning Using a Bayesian Updating Approach
Quelle	In: IEEE Transactions on Learning Technologies, 14 (2021) 4, S.474-485 (12 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Xing, Wanli) ORCID (Du, Dongping) ORCID (Du, Hanxiang)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1939-1382
DOI	10.1109/TLT.2021.3107349
Schlagwörter	Electronic Learning; Bayesian Statistics; Prediction; Models; Learning Analytics; Data Collection; Robustness (Statistics); Courses + Suchen Sie Ihr Suchwort? Vorhersage; Analogiemodell; Data capture; Datensammlung; Widerstandsfähigkeit; Kursangebot
Abstract	Predictive modeling in online education is a popular topic in learning analytics research and practice. This study proposes a novel predictive modeling method to improve model transferability over time within the same course and across different courses. The research gaps addressed are limited evidence showing whether a predictive model built on historical data retrospectively can be directly applied to a future offering of the same course or to another different course; lacking interpretable data mining models to improve model transferability over time and across courses. Three datasets from two distinct online courses with one course offered two times over two years were applied using direct transferring of the predictive model and the proposed Bayesian updating technique for model transfer. The results showed that the direct transferring of predictive model to the subsequent offering of the course and to a totally different course did not work effectively. By contrast, the proposed Bayesian updating provided a robust and interpretable approach with improved model transferability results for both situations. This Bayesian updating model can be continuously updated with new collected data rather than building prediction model from scratch every time, which can serve as a new methodological framework to carry experience and knowledge from past and other courses forward to new courses. (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