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Predicting Student Engagement in the Online Learning Environment

Predicting Student Engagement in the Online Learning Environment

Abdalganiy Wakjira, Samit Bhattacharya
Copyright: © 2021 |Volume: 16 |Issue: 6 |Pages: 21
ISSN: 1548-1093|EISSN: 1548-1107|EISBN13: 9781799867425|DOI: 10.4018/IJWLTT.287095
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MLA

Wakjira, Abdalganiy, and Samit Bhattacharya. "Predicting Student Engagement in the Online Learning Environment." IJWLTT vol.16, no.6 2021: pp.1-21. http://doi.org/10.4018/IJWLTT.287095

APA

Wakjira, A. & Bhattacharya, S. (2021). Predicting Student Engagement in the Online Learning Environment. International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), 16(6), 1-21. http://doi.org/10.4018/IJWLTT.287095

Chicago

Wakjira, Abdalganiy, and Samit Bhattacharya. "Predicting Student Engagement in the Online Learning Environment," International Journal of Web-Based Learning and Teaching Technologies (IJWLTT) 16, no.6: 1-21. http://doi.org/10.4018/IJWLTT.287095

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Abstract

Students in the online learning who have other responsibilities of life such as work and family face attrition. Constructing a model of engagement with smallest granule of time has not been implemented widely, but implementing it is important as it allows to uncover more subtle patterns. We built a student engagement prediction model using 9 features that were significant out of 13 features to affect the levels of student engagement and emerged in the final model. The student engagement prediction model was built using non-linear regression technique from three factors: behavioral, collaboration and emotional factors across micro level time scale such as 5 minutes to identify at risk students as quickly as possible before they disengage. The accuracy of the model was found to be 83.3%. The results of the study will give teachers the chance to provide early interventions and guidelines for designing online learning activities.