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A Conceptual Educational Data Mining Model for Supporting Self-Regulated Learning in Online Learning Environments

A Conceptual Educational Data Mining Model for Supporting Self-Regulated Learning in Online Learning Environments

Eric Araka, Robert Oboko, Elizaphan Maina, Rhoda K. Gitonga
Copyright: © 2021 |Pages: 15
ISBN13: 9781799847397|ISBN10: 179984739X|EISBN13: 9781799847403
DOI: 10.4018/978-1-7998-4739-7.ch016
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MLA

Araka, Eric, et al. "A Conceptual Educational Data Mining Model for Supporting Self-Regulated Learning in Online Learning Environments." Handbook of Research on Equity in Computer Science in P-16 Education, edited by Jared Keengwe and Yune Tran, IGI Global, 2021, pp. 278-292. https://doi.org/10.4018/978-1-7998-4739-7.ch016

APA

Araka, E., Oboko, R., Maina, E., & Gitonga, R. K. (2021). A Conceptual Educational Data Mining Model for Supporting Self-Regulated Learning in Online Learning Environments. In J. Keengwe & Y. Tran (Eds.), Handbook of Research on Equity in Computer Science in P-16 Education (pp. 278-292). IGI Global. https://doi.org/10.4018/978-1-7998-4739-7.ch016

Chicago

Araka, Eric, et al. "A Conceptual Educational Data Mining Model for Supporting Self-Regulated Learning in Online Learning Environments." In Handbook of Research on Equity in Computer Science in P-16 Education, edited by Jared Keengwe and Yune Tran, 278-292. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-4739-7.ch016

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Abstract

Self-regulated learning is attracting tremendous researches from various communities such as information communication technology. Recent studies have greatly contributed to the domain knowledge that the use self-regulatory skills enhance academic performance. Despite these developments in SRL, our understanding on the tools and instruments to measure SRL in online learning environments is limited as the use of traditional tools developed for face-to-face classroom settings are still used to measure SRL on e-learning systems. Modern learning management systems (LMS) allow storage of datasets on student activities. Subsequently, it is now possible to use Educational Data Mining to extract learner patterns which can be used to support SRL. This chapter discusses the current tools for measuring and promoting SRL on e-learning platforms and a conceptual model grounded on educational data mining for implementation as a solution to promoting SRL strategies.

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