Intelligent Prediction and Personalized Intervention in Mathematics Learning Using Machine Learning Techniques

With the rapid development of information technology, personalized learning has become an important direction in modern education. Especially in the field of higher education, how to accurately predict and intervene based on students’ learning behavior to improve learning outcomes has become one of the core issues in educational research. Existing traditional teaching and intervention methods face challenges such as insufficient personalization and fixed intervention strategies. Therefore, this study proposes a personalized learning behavior prediction and intervention framework based on time series analysis and machine learning, aiming to provide students with more accurate learning support in a data-driven way. This study uses LSTM (Long Short-Term Memory Network) to predict students’ learning behavior. By modeling students’ historical learning data, it can accurately predict students’ learning trends and performance changes.