Learning Analytics and Online Courses: A Bayesian Belief Network Approach to Predict Success

Abstract

In the field of educational research, learning analytics is one of the prevailing areas of exploration. The study explores a part of learning analytics using a Bayesian networks (BN) model to predict the success of the course in the online mode of education. Through the simulation results, it was found that the BN approach can be used to suggest improved online instruction delivery methods, helping the instructors and students reform their practices to maintain a synergy for a successful running of the course. As the study was executed on engineering students, it could further be generalized using students of other streams for comprehensive understanding. The study reveals that the student synergy with the method of teaching, paper difficulty, and take-home assignments are found to be the main determinants of the success of E-learning courses. The study reveals that student’s synergy with the method.