Machine learning analysis of factors affecting college students' academic performance.

This study aims to explore various key factors influencing the academic performance of college students, including metacognitive awareness, learning motivation, participation in learning, environmental factors, time management, and mental health. By employing the chi-square test to identify features closely related to academic performance, this paper discussed the main influencing factors and utilized machine learning models (such as LOG, SVC, RFC, XGBoost) for prediction. Experimental results indicate that the XGBoost model performs the best in terms of recall and accuracy, providing a robust prediction for academic performance. Empirical analysis reveals that metacognitive awareness, learning motivation, and participation in learning are crucial factors influencing academic performance. Additionally, time management, environmental factors, and mental health are confirmed to have a significant impact on students' academic achievements. Furthermore, the positive influence of professional training on academic performance is validated, contributing to the integration of theoretical knowledge and practical application, enhancing students' overall comprehensive competence. The conclusions offer guidance for future educational management and guidance, emphasizing the importance of cultivating students' learning motivation, improving participation in learning, and addressing time management and mental health issues, as well as recognizing the positive role of professional training.