Research on objective evaluation methods of labor education teaching for college students in higher education

被引:0
|
作者
Wang X. [1 ]
Zhou W. [2 ]
机构
[1] Fujian Normal University, Fuzhou, Fujian
[2] Fujian Chuanzheng Communications College, Fujian, Fuzhou
关键词
Binary tree; Classification algorithm; Evaluation system; Labor education; Support vector machine;
D O I
10.2478/amns-2024-0905
中图分类号
学科分类号
摘要
Labor education in colleges and universities in the new era is the fundamental task of implementing the principle of establishing morality and educating people, as well as an essential hand in cultivating college students with all-round development of morality, intelligence, physicality, aesthetics, and labor. This paper focuses on the objective evaluation method of college students’ labor education to conduct research. Using the improved binary tree support vector machine multi-class classification algorithm, an evaluation model for college labor education based on a binary tree support vector machine has been constructed, and an evaluation system for labor education has been developed. The results show that the majors that have compulsory labor education courses, elective labor education courses, and have added labor education modules in their major courses are all art and sports, accounting for more than 50% on average. The percentages of liberal arts, science, economics and management, art and sports, medicine and science, and agriculture, which did not offer labor education-related courses, were 26.35%, 22.16%, 19.91%, 13.59%, 11.03%, and 13.19%, respectively. It is evident that labor education courses are not distributed equally in colleges and universities, and there are still some majors that do not offer labor education courses. In terms of labor education, most of the labor education through Civics and Political Science classes is provided to students. Managers are those who choose the most specialized courses. Students also tend to participate in bicultural education and social practice activities, accounting for 29.02% and 43.01%, respectively. It shows that the algorithm in this paper can objectively and effectively evaluate labor education in colleges and universities and identify the deficiencies in education. © 2023 Xin Wang and Wenjie Zhou, published by Sciendo.
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