JCERJournal of Contemporary Educational Research2208-84662208-8474Bio-Byword Scientific Publishing Pty. Ltd.10.26689/jcer.v9i5.10774ArticleAnalysis of Influencing Factors of Academic Warning in Higher Vocational Colleges Based on the Importance of Machine Learning Features and Paths to Improve Learning Abilityhttps://artdesignp.com/journal/JCER/9/5/10.26689/jcer.v9i5.10774HuangMeimei,ZhangLei,FanXifeng2025952025-06-05The traditional academic warning methods for students in higher vocational colleges are relatively backward, single, and have many influencing factors, which have a limited effect on improving their learning ability. A data set was established by collecting academic warning data of students in a certain university. The importance of the school, major, grade, and warning level for the students was analyzed using the Pearson correlation coefficient, random forest variable importance, and permutation importance. It was found that the characteristic of the major has a great impact on the academic warning level. Countermeasures such as dynamic adjustment of majors, reform of cognitive adaptation of courses, full-cycle academic support, and data-driven precise intervention were proposed to provide theoretical support and practical paths for universities to improve the efficiency of academic warning and enhance students’ learning ability.

Hu JT, 2010, Speech at the National Education Work Conference. People’s Daily, 9(09): 2.

Zhang BZ, Li H, Hou HX, et al., 2021, Design of Early Warning System of Academic Performance at Colleges and Universities Based on Database and Machine Learning Technology. Education of Chinese Medicine, 40(3): 63–67.

Jia LL, Sun TT, 2022, A Machine Learning Study on Gloeobacter violaceus Rhodopsin Spectral Properties. Journal of Zhejiang University (Science Edition), 49(3): 280–286.

He XQ, Fu HD, Zhang HT, et al., 2022, Machine Learning Aided Rapid Discovery of High-Performance Silver Alloy Electrical Contact Materials. Acta Metallurgica Sinica, 58(6): 816–826.

Wang GT, 2021, Academic Early Warning Research Based on Machine Learning, dissertation, Xi’an Shiyou University, 1–52.

Xue X, Zhou J, 2017, A Hybrid Fault Diagnosis Approach Based on Mixed-Domain State Features for Rotating Machinery. ISA Transactions, 66: 284–295.

Lee WM, 2019, Python Machine Learning, John Wiley & Sons, New Jersey.

Li ZG, 2014, Several Research on Random Forest Improvement, dissertation, Xiamen University, 1–46.

Radivojac P, Obradovic Z, Dunker AK, et al., 2004, Feature Selection Filters Based on the Permutation Test, European Conference on Machine Learning, Springer Berlin Heidelberg, Berlin, Heidelberg, 334–346.

Zhang LJ, Yao Qi, 2019, Research on the Characteristics and Educational Management Strategies of Higher Vocational Art Students—Taking Jiangsu Information Vocational and Technical College as an Example. Think Tank Era, (10): 164–165.

Zhao XM, Zhao KY, 2018, A Design Research About Big Data of Education Applied to Academic Warning: An Example of the Early-Warning System for Children Left Behind in Rural Areas. Research in Educational Development, 38(12): 64–71.

Lv L, Xia ZH, 2020, Cause Analysis and Countermeasure Research of College Academic Warning Based on Random Forest Algorithm. Journal of Nanchang Institute of Technology, 39(6): 81–86.