Clustering of Students’ Behavior Using the GPT-2 Module Based on the Macroscopic Attention Model

A reasonable group classification of students is beneficial for the process management of university students on campus. To this end, a Macroscopic Attention (MA) model is developed to characterize individual students. Existing clustering methods find it difficult to take into account the temporal characteristics of the data and the relationship between different dimensions. Consequently a time series clustering method, GPT-K, is proposed to optimize the clustering effect by capturing the complex temporal dependencies and interactions between the MA quality features in the time series data through the GPT-2 module. Experiments indicate that the method has a great advantage over other clustering methods like AE-K and CNN-K in terms of Silhouette Coefficient, Davies-Bouldin Index and Calinski-Harabaz Index. We analysis the results of student group classification, it is found that the probability of students in one group to receive scholarships is significantly higher than that of students in another group. This means that there is a significant gap in academic performance between the two groups. Based on the research in this paper, early intervention and guidance can be provided to students in a timely manner.