A Comparative Study of Machine Learning Approaches on Learning Management System Data

Abstract

This paper addresses the analysis of machine learning (ML) effectiveness in learning analytics context. Four different machine learning approaches are evaluated. The results offer information about the usefulness of these approaches and help to decide which of the approaches is the most promising one in learning analytics application. Results substantiate that the neural networks ML model trained on our learning management system (LMS) data exhibits the best performance for predicting the students' academic performance. In our future research, predictive model results will be explained within a pedagogical context in order to be used as part of student support mechanism.