A prediction model of student performance based on self-attention mechanism

Performance prediction is an important research facet of educational data mining. Most models extract student behavior features from campus card data for prediction. However, most of these methods have coarse time granularity, difficulty in extracting useful high-order behavior combination features, dependence on 6 historical achievements, etc. To solve these problems, this paper utilizes prediction of grade point average (GPA prediction) and whether a specific student has failing subjects (failing prediction) in a term as the goal of performance prediction and proposes a comprehensive performance prediction model of college students based on behavior features. First, a method for representing campus card data based on behavior flow is introduced to retain higher time accuracy. Second, a method for extracting student behavior features based on multi-head self-attention mechanism is proposed to automatically select more important high-order behavior combination features. Finally, a performance prediction model based on student behavior feature mode difference is proposed to improve the model’s prediction accuracy and increases the model’s robustness for students with significant changes in performance. The performance of the model is verified on actual data collected by the teaching monitoring big data platform of Xi’an Jiaotong University. The results show that the model’s prediction performance is better than the comparison algorithms on both the failing prediction and GPA prediction.