Deciphering Student Academic Success: Bayesian Analytical Insights

Abstract

This study delves into the factors influencing student’s academic achievement utilizing Bayesian mixed effect models. It presents five distinct models, each integrating various fixed variables such as gender, playing hours, stress level, and travelling hours, alongside random variables such as school level and type of school. These models are evaluated using the LeaveOne-Out Information Criterion (LOOIC) to gauge their adequacy in fitting the data and predicting outcomes. The findings unveil that the inclusion of additional factors, such as school characteristics and students' activities, modifies the relationship between gender and academic success, with gender exerting a diminishing influence as more variables are incorporated. Additionally, stress level and travelling hours emerge as noteworthy predictors of average marks. Among the models assessed, the one incorporating gender, playing hours, and stress level as fixed effects, alongside school level and type as random effects, demonstrates superior fit and predictive capability. This underscores the significance of considering both individual traits and contextual elements in comprehending academic performance.