Predicting Student Engagement in the Online Learning Environment

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.