Student performance prediction is a fundamental task in online learning systems, which aims to provide students with access to active learning. Generally, student performance prediction is achieved by tracing the evolution of each student's knowledge states via a series of learning activities. Every learning activity record has two types of feature data: student behavior and exercise features. However, most methods use features that are related to exercises, such as correctness and concepts,

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... le other student behavior features are usually ignored. The few studies that have focused on student behavior features through subjective manual selection argue that different student behavior features can be used in an equivalent manner to predict student performance. In this paper, we assume that the integration of student behavior features and exercise features is crucial to improve the precision of prediction, and each feature has a different impact on student performance. Therefore, this paper proposes a novel framework for student performance prediction by making full use of both student behavior features and exercise features and combining the attention mechanism with the knowledge tracing model. Specifically, we first exploit machine learning to capture feature representation automatically. Then, a fusion attention mechanism based on recurrent neural network architecture is used for student performance prediction. Extensive experiments on a real-world dataset show the effectiveness and practicability of our approach. The accuracy of our method is up to 98%, which is superior to previous methods.