Multiple Linear Regression Model to Predict Mathematics Learning Achievement Based on Learning Styles and School Environment

Abstract

This study aims to analyze the extent to which learning styles and the school environment predict students’ mathematics learning achievement using a multiple linear regression model. The research employs a quantitative correlational approach involving a sample of high school students selected through stratified random sampling. Data were collected using validated questionnaires to assess students' learning styles and perceptions of the school environment, along with academic records for mathematics achievement. The results of the regression analysis indicate that both learning styles and school environment significantly influence mathematics performance, with a combined coefficient of determination (R²) of 0.532. This suggests that 53.2% of the variance in mathematics achievement can be explained by these two variables. Moreover, the school environment was found to have a slightly greater impact compared to learning styles. These findings highlight the importance of integrating individualized learning strategies with a supportive educational context to optimize student achievement in mathematics. The study provides valuable insights for educators, curriculum developers, and policymakers aiming to improve mathematics education through evidence-based strategies.