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

Xia Xiongjun,Danmeng Lv,Naeem Jan

doi:10.1155/2022/2678303

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.

Highlights

Art and science technology have never been separated
The main focus of machine learning research in the education field is to explore the learning environment and web-based teaching systems, and improve student performance. ere are a few applications of information mining for student evaluation. is article studies the potential of data mining in the teacher performance measurement standards perceived by students and chooses decision tree algorithm (DS), support vector machine (SVM), naive Bayes (NB), and random forest (RF), the four commonly used machine learning classifiers, which model the dataset of students’ online evaluation information and compare the performance indicators of various classification techniques
We classified the online course evaluation data through machine learning methods and analyzed the importance of characteristics of student evaluation. e different dimensions of college courses and teacher effectiveness are measured by the online course evaluation

Summary

Introduction

Art and science technology have never been separated. As Li Zhengdao said, “Science and art are inseparable, just like the two sides of a coin [1–5].” Music as a form of art is naturally inseparable from science [6]. With the advent of the Internet era, higher education institutions continue to improve their own informatization teaching level, resulting in a large amount of data related to the teaching process [7]. How to use this information to improve the quality of teaching and scientific research services in universities and the level of management decision-making has become the biggest challenge facing universities. Is article studies the potential of data mining in the teacher performance measurement standards perceived by students and chooses decision tree algorithm (DS), support vector machine (SVM), naive Bayes (NB), and random forest (RF), the four commonly used machine learning classifiers, which model the dataset of students’ online evaluation information and compare the performance indicators of various classification techniques.

Literature Review

Method

Experiment and Result Analysis

Accuracy

Findings

Conclusion