Comparative analysis of feature selection and extraction methods for student performance prediction across different machine learning models

Abstract

Education is at the core of developmental progress, necessitating the exploration and implementation of diverse contemporary methods to ensure the success of students across multiple levels. However, impediments to this success exist, categorized into three primary groups which are, individual factors, family factors and social factors. These factors can manifest as absenteeism and boredom, posing a threat to the future of both students and society at large. Addressing these challenges proves to be a complex task for educators and pedagogues, given the unique problems each student faces. This paper aims to employ a broad spectrum of feature selection and extraction methods, some unconventional in the education sector but proven reliable in other domains. By integrating machine learning (ML) and deep learning (DL) models, we seek to predict student performance based on these identified factors. Subsequently, a comparative analysis will be conducted to determine the most effective model, considering the relevance of various factors.