Research on the Evaluation of the Effectiveness of the Construction of First-Class Undergraduate Majors in Colleges and Universities Based on Student Input in the Context of Deep Learning

Abstract

Abstract This paper uses triangular fuzzy numbers to solve the weights of the evaluation indexes and then applies the improved TOPSIS method to evaluate the effectiveness of the construction of first-class undergraduate majors in colleges and universities. Empirical data were collected through questionnaires and materials related to the declaration of first-class major construction to verify the factors affecting the effectiveness of first-class undergraduate major construction and the comprehensive evaluation. The results show that four indicators, namely student learning outcomes, effective teaching practices, teacher-student interaction levels, and professional curriculum plans, explain 57.52% of the effectiveness of first-class undergraduate major construction. Among the ten universities, the relative closeness of the first-class undergraduate major construction effectiveness to the optimal solution of University D is 0.75221, and the major construction effectiveness is the most significant. The combination of students’ inputs and the effectiveness of first-class undergraduate major construction in universities under the background of deep learning can further enhance the depth of students’ learning for the major and promote the effectiveness of major construction.