Research on Academic Risk Identification and Early Warning System of College Students Based on Adversarial Transformer Model

Abstract

With the continuous expansion of the scale of higher education, the problem of students' academic risks has become increasingly prominent. How to effectively identify and warn students of academic risks has become an important topic in education. This study aims to explore the academic risk identification and early warning system of college students based on the adversarial Transformer model to improve the accuracy and timeliness of academic risk management. Firstly, the research analyzes the limitations of current academic risk identification methods, such as insufficient data feature extraction and weak generalization ability of the model, and then proposes a Transformer model integrating adversarial training. By introducing an adversarial sample generation mechanism, the model enhances the robustness and generalization ability of the model in the face of complex academic data. The experimental results show that the accuracy of the proposed model in academic risk identification reaches 92.5%, which is 8.3 percentage points higher than that of traditional machine learning methods. Regarding early warning timeliness, the model can accurately predict students with potential academic risks two weeks in advance, providing a valuable time window for the implementation of intervention measures. In addition, this study also constructs a visual early warning system, which realizes real-time monitoring and dynamic early warning of academic risk data and provides scientific decision support for university education administrators. The conclusion shows that the academic risk identification and early warning system based on the adversarial Transformer model has obvious advantages in improving the efficiency of academic risk management and promoting students' all-round development, and provides a new technical path and solution for college students' academic risk management.