A Probability-Based Model for Course Completion Prediction in Online Asynchronous Learning

Abstract

The sheer scale of Massive Open Online Courses (MOOCs), presents a significant challenge in delivering personalized learning experiences and effective student support. With vast participation numbers, it becomes difficult for instructors to track individual progress, pinpoint specific areas where students struggle, and understand the underlying reasons for failure to complete the course. This lack of individualized attention can lead to disengagement and higher dropout rates. A probability-based analysis offers a solution by generating student-specific predictions about their likelihood of completion. This empowers educators to proactively identify those at risk, tailor interventions, optimize resource allocation, and potentially improve the overall learning experience within the MOOC environment. This study focuses on developing a robust predictive framework to accurately estimate the probability of students completing an introductory programming course offered on the Open Learning Platform of the University of Moratuwa, Sri Lanka. The approach began with a classification model to determine the likelihood of course completion. Building upon this, a regression model was developed to generate a specific probability percentage representing the chance of a student successfully completing the course within a designated time frame. Initial findings suggested that predictions from the classification model achieved the highest accuracy when students have completed approximately 42.8% of the course materials. It is anticipated that further refinements to the methodology will improve the reliability of the predictions. A crucial aspect of this research involves determining the optimal percentage of course progress needed to yield reliable probability predictions. This is investigated through systematic analysis and experimentations including incremental model testing. The dataset encompasses diverse demographic and educational variables, enabling the identification of influential factors affecting course completion. This study provides insights on developing personalized learning strategies, intervention tactics and academic planning within online asynchronous education.