Effort, Mindset, and Motivation: Comparing Traditional and Machine Learning Regression Models in Predicting Math Performance

This study examines the influence of effort, growth mindset, and motivation on mathematics performance, comparing the effectiveness of traditional and machine learning regression models. Using secondary data from 4,552 students, five models—multiple regression, Random Forest, Gradient Boosting, XGBoost, and LightGBM—were evaluated for predictive accuracy and feature importance. The results demonstrate that machine learning models, particularly XGBoost, significantly outperform traditional regression, with XGBoost achieving the lowest Mean Squared Error (MSE = 7186.49) and the highest R-squared (0.1571). Effort emerged as the strongest predictor, followed by growth mindset, while motivation had a smaller but notable influence. These findings reinforce the applicability of Self-Regulation Theory and Growth Mindset Theory, highlighting the potential of machine learning to enhance educational research. Practical implications emphasize the importance of fostering effort and growth mindset through targeted interventions and leveraging predictive analytics for data-driven decision-making in education.