Students’ Academic Performance: Prediction using Machine Learning Approaches

Abstract

The discipline of higher education is seeing rapid growth and is closely intertwined with the advancements in technology. Utilisation of machine learning (ML) to predict students' academic achievement has demonstrated promising results and has been advantageous for educational institutions. The challenges associated with making predictions reside in the ability to accurately identify potential attributes within multi-class projections, while also considering the varying quantities of distinct attribute categories. Therefore, this study has examined multiple classes and variations of attributes from various categories, including demographic, academic, personal, and parental profiles. The implementation of five distinct machine learning models for prediction exploited a dataset sourced from the Kaggle repository. In order to mitigate attribute complexity across several categories, two approaches for attribute selection or reduction were employed. Furthermore, eight distinct metrics were employed for the examination of the models. The findings indicate that the classification model's performance in terms of accuracy was only average when considering multi-class predictions and variations of categorical attributes. This was observed after using attribute reduction approaches for 50% and 100% of the attributes.