Article Details

Abstract

Under the dual background of the popularization of higher education and the application-oriented transformation of private colleges and universities, students' academic management in private colleges and universities faces multiple challenges such as weak students' foundation, lack of self-discipline, and strong demand for family-school collaboration. This paper focuses on the “teacher-class tutor-counselor-peer mentor” four-element education subject and the “school-parent” double-driven collaborative mechanism. Based on the relevant research results of academic early warning and collaborative education, this paper systematically reviews the research on the academic early warning system of private colleges and universities, and elucidates the operation logic and innovation value of the four-element double-driven collaborative mechanism. It deeply analyzes the theoretical basis, internal logic and operation process of the mechanism, and discusses the feasibility of integrating prediction models such as random forest and low-cost technologies such as B/S architecture into the mechanism to realize “accurate early warning, efficient collaboration and sustainable operation”. The research provides theoretical support and practical paradigm for the optimization and upgrading of the academic early warning system in private colleges and universities, and also provides reference for the construction of collaborative education mechanism in similar colleges and universities.

Keywords

private colleges and universities; academic early warning system; four-element dual-drive; coordination mechanism

References

1. [1] Bai Xuejun, Yuan Sheng, Du Xu, etc Preliminary exploration of warning factors for academic failure among college students [J]. Journal of Tianjin Normal University (Social Sciences Edition), 2022 (1): 70-76
2. [2] Liu Ruyi, Lv Xiaohan, Miao Qiguang, etc Research on Academic Early Warning Prediction Model Based on Attention Mechanism and Feature Interaction [J/OL]. Computer Science, 2025:1-17. (2025-08-15) https://link.cnki.net/urlid/50.1075.TP.20250815.0935.014.
3. [3] Wang Heng, Zhao Yue Home school communication strategies under the background of academic warning in universities [J]. School Party Building and Ideological Education, 2022 (12): 79-80
4. [4] Hu Jing Value selection of problem college student education transformation from the perspective of collaborative governance [J]. Journal of Yanbian Education College, 2020, 34 (3) 83-85, 89
5. [5] Dong Shizhi, Wu Chun, Meng Chao, etc The Implementation Path of Student Academic Early Warning Mechanism Reform under the Background of Engineering Education Professional Certification [J]. Higher Education Journal, 2025, 11 (28): 145-148
6. [6] Xie Xiaoyu, Fan Shen, Song Jinyu, etc Analysis and Prediction of Learning Behavior Data Based on MOOC Learning Platform [J]. Software Guide, 2025, 24 (2): 198-203
7. [7] Madani Research on Student Academic Early Warning Model Based on Machine Learning [D]. Shenyang: Shenyang University of Technology, 2020
8. [8] Xiang Lina, Yang Hao Research on the Collaborative Education of Academic Mentors and Counselors through "Dual Guidance and Drive" [J]. Journal of Wuzhou University, 2024, 34 (5): 80-88
9. [9] Ma Lingjie Research and Application of Student Academic Early Warning Based on Convolutional Neural Network and SVM [D]. Nanchang: Nanchang University, 2021
10. [10] Wang Ying, Wang Jiaqi, Xia Yu, etc Design of Internet Addiction and Academic Warning System Based on Machine Learning Methods [J]. Computer Applications and Software, 2025, 42 (7): 22-26, 50

About the authors

Qu Qing (1995-), female, Han ethnicity, from Baoji, Shaanxi, teaching assistant, master's student, research direction: application of artificial intelligence; Li Yuxin (1996-), female, Han ethnicity, from Xianyang, Shaanxi Province, teaching assistant, master's student, research direction: deep learning image processing.