Predicting Student Engagement in the Online Learning Environment

Students in online learning who have other responsibilities of life such as work and family face attrition. Constructing a model of engagement using the least amount of time is important as it allows us to uncover more subtle patterns. The authors built a student engagement prediction model using nine features that were significant out of 13 features to affect the levels of student engagement. The student engagement prediction model was built using non-linear regression technique from three factors behavioral, collaboration, and emotional across a micro-level time scale such as five 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. © 2021 IGI Global. All rights reserved.