5. Machine learning for predicting the academic performance of lower secondary school students
Keywords:
Machine Learning; Academic performance; Lower secondary students; Prediction; Influencing factors.Abstract
This study aims to analyze the factors influencing academic performance among lower secondary school students and to develop a predictive application based on Machine Learning (ML). The dataset consisted of 957 observations and was tested for reliability (Cronbach’s Alpha ranging from 0.602 to 0.851) and construct validity through Exploratory Factor Analysis (KMO = 0.915; p < 0.001) , resulting in the extraction of seven principal factors. The linear regression model explained 53.4% of the variance in academic performance (R² = 0.534), with the cognition–attitude factor exerting the strongest effect (standardized β = 0.582; p < 0.001). A comparison of nine predictive models indicated that nonlinear models achieved superior performance; Random Forest and XGBoost reached R² = 0.951, while SVR achieved R² = 0.899 (RMSE = 0.456) and was selected for application deployment. Based on this model, a web-based demo was developed, allowing students to complete an online survey, after which the trained SVR model predicts academic outcomes and provides tailored learning recommendations. The findings demonstrate the feasibility of integrating ML into academic support systems at the lower secondary level.
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