Ascertaining the Online Learning Behaviors and Formative Assessments Affecting Students’ Academic Performance during the COVID-19 Pandemic: A Case Study of a Computer Science Course

Abstract

Prior education research has focused on using learning analytics to predict the academic performance of Massive Online Learning Courses (MOOCs) and e- learning courses in universities. There is limited research on online learning that has been transitioned from physical classes and that has continued to use active learning approaches in an online environment. This study aims to determine the variables affecting students’ academic performance for a computing course in a research-intense university during the COVID-19 pandemic. Variables that are indicative of self-regulated learning such as time management, frequency of accessing learning materials and the Learning Management System (LMS), participation in assessment activities and discussions, and the results of formative assessments were extracted from the LMS reports and log files to predict the students’ total marks and final exam results. The findings revealed that good time management and active participation are important for academic success. The results also supported the model for the early prediction of summative assessment performance using formative assessment results. Additionally, this study concludes that the gap in predictive power between formative assessment results and online learning behaviors is small. This research is considered unique because it demonstrates predictive models for students’ academic success for an institution that was forced to transition from physical to online learning. It highlights the importance of self-regulated learning behavior and formative assessments in the contemporary era.