Mathematical success: a review of impact factors and predictive analytics for student achievement

This study applies data analytics and machine learning techniques to identify key factors influencing mathematics achievement among secondary school students and to predict their academic performance. We analysed 549 student records from a secondary school between 2018 and 2023. Various factors were examined, including grades, gender, ethnicity, and Special Educational Needs status. After comprehensive data preprocessing and feature engineering, we developed multiple predictive models to forecast mathematics results in secondary education. These models were optimised using GridSearchCV, achieving predictive accuracy with R-squared values exceeding 0.89 across all models, with the Decision Tree model performing best (Root Square Error = 0.912840, Mean Squared Error = 0.341011). Our findings emphasise the importance of additional support for students with special educational needs. We also observed consistent progress from mock exams to final results. Furthermore, this research demonstrated the effectiveness of machine learning in identifying key determinants of mathematical success while providing actionable insights for targeted educational interventions.