Academic Achievement Prediction Based on Attention Neural Network Algorithm

This paper proposes a neural network model based on attention mechanism, which can effectively capture key information and complex relationships between subjects in the learning process of students. First, we collect a variety of data, including students' personal information, learning behavior data, and academic performance. We then use attention mechanisms to automatically learn important features in the data, and introduce time series information into the model to capture dynamic changes in the student learning process. Finally, we verify the validity of our proposed model in the task of academic achievement prediction through experiments. The experimental results show that our model achieves remarkable predictive performance in different subjects and different student groups, indicating its potential value in practical educational applications.