The Evaluation of Music Teaching in Colleges and Universities Based on Machine Learning

Abstract

With the implementation of the strategic policy of rejuvenating the country through science and education, many innovative and practical teaching concepts and teaching models have been comprehensively developed. This breaks the backward teaching mode of traditional teaching activities. With the development of science and technology and Internet technology, deep learning is widely used in the field of education. Music teachers in colleges and universities constantly update their teaching methods and comprehensively use a variety of methods to carry out in-depth teaching in the classroom, and strive to stimulate students’ learning Interest and enthusiasm, and comprehensively enhance students’ music aesthetic ability. This article uses decision tree algorithms, support vector machines, Bayesian theory, and random forest four different classification techniques to evaluate the student curriculum evaluation dataset. Classification experiment: through the analysis of the experimental results, the performance of the four classifier models was compared, and the data showed the difference in accuracy, precision, recall, and F1 value of the four classifiers. At the same time, each of the classifier models was analyzed. This article verifies the effectiveness of machine learning models in curriculum evaluation and higher education mining, the importance of evaluation features.