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

Authors: Xia Xiongjun; Danmeng Lv
Journal: Journal of Mathematics
 (2022)

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## 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.


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## Body

## 1. 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. The use of machine learning technology can effectively analyze and extract hidden information in large datasets, that is, discovering patterns and knowledge in large amounts of data and predicting results or behaviors. In recent years, the application of machine learning in higher education has become increasingly popular, involving the optimization of educational resource allocation, predicting student academic performance, academic planning, and enhancing the future development of alumni. It has also given birth to the birth of new educational research fields [8–12]. Excavating in-depth information from educational entities such as students, teachers, teaching assistants, alumni, and education administrators can help colleges and universities allocate various teaching resources and organize educational activities more effectively, and more effectively improve students’ satisfaction with courses, to enhance the learning effect of students and increase the enrollment rate of majors [13]. One of the common problems in higher education is to evaluate the performance of teachers in the curriculum. Students are the only source of information for the learning process and the only person who can evaluate the quality, effectiveness and satisfaction of course content, teaching methods, teaching materials, and assignments. Student evaluation is mainly used to improve the quality of course teaching and, at the same time, as the basis for teaching assessment of teaching staff. Most colleges and universities still use student evaluation as an effective measure of teaching evaluation. Music as an artistic form of expression contains very rich emotions. In the process of teaching in universities in my country at this stage, relevant education departments pay more attention to the teaching of music as a subject. With the continuous improvement of my country’s social economy, culture, and people’s pursuit of spiritual civilization, the requirements of college music majors have become increasingly demanding. In order to meet people’s growing spiritual needs, music teachers in colleges and universities, in the process of organizing teaching, should contact the reality of life, aiming at students’ hobbies and diversified classroom teaching to fully mobilize students’ learning enthusiasm.Currently, 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. There are a few applications of information mining for student evaluation. This 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 of course information and compare the performance indicators of various classification techniques. Section2 of our study reviews the related works and the literature. In Section 3, the methodology of our work is described along with the description of proposed techniques. Moreover, the study also proposes the experimental section, that is, Section 3, which carries out tests and verifies the strength and working of the proposed techniques through the results of tests. In Section 5, the conclusion of the study is given.


## 2. Literature Review

In order to further advance the reform of music education in colleges and universities, promote the development of music education, and strengthen the management system of music education, many countries are constantly studying the basic theories and practical applications of music teaching evaluation in colleges and universities, so that it can better achieve the teaching goals service. In some of these countries, the research on the evaluation of music teaching is earlier, and the theory is relatively mature, with a certain degree of operability. Among them, the United States and Japan have conducted in-depth research on the evaluation of music education [14–18].Overview of foreign music teaching evaluation. The American music teaching evaluation mainly includes the music test movement, the goal model of curriculum design, the taxonomy of educational goals, and the achievement responsibility system movement. In the early 20th century, American school music education mainly consisted of music tests. Music tests were used to collect objective data on the development characteristics and achievements of students in music, as well as music courses and evaluation effects in music education, so that music teaching could be measured based on measurement. These objective materials come to evaluate the results and process of music education in the past, and to revise and improve new goals, processes, courses, and teaching. The tools used are musical aptitude test (such as “Westshore Musical Abilities Measurement”) and musical achievement test (such as “Beach Music Test”). In the 1940s and 1950s, the evaluation committee led by Professor Taylor, based on the “eight-year study,” proposed that teaching evaluation should focus on describing the consistency between educational results and educational goals. Therefore, a goal-centered evaluation model was established. Based on the “eight-year study,” the Evaluation Committee of China proposed that teaching evaluation should focus on describing the consistency between educational results and educational goals. Therefore, a goal-centered evaluation model was established. In the 1960s and 1970s, the performance accountability movement was popular in the United States. Boyer and Radoqi proposed in their book “Musical Experience Measurement and Evaluation” that the service functions of music education program evaluation may have the following types: responsibility system; teaching effect; teacher effect; classroom effect; subject knowledge taught and so on. The abovementioned theoretical and practical research has established important value standards for the development of an evaluation index system for teaching effects. Educational evaluation is developed from educational measurement [19, 20].Overview of Music Teaching Evaluation in my country. Educational measurement can be traced back to various tests in the 19th century. The initial germination of education evaluation in my country originated from the ancient imperial examination system. It is considered to be the earliest comprehensive examination in the world. Some people call it the “civilian examination.” The purpose of the examination is to become an official. People flew up and down overnight. Due to various reasons, the research on educational testing, measurement, and evaluation in my country has not been developed since the last century. After the founding of the People’s Republic of China, my country’s education evaluation has gradually developed. Since the 1950s, my country has been following the Soviet Union’s music education evaluation model. It shows the phenomena of “compulsion” (ignoring the development of the main body), “rigidity” (ignoring development vitality), “one-sided” (ignoring overall development), and “separation” (ignoring the integration of evaluation and development) of evaluation. Music teaching evaluation emphasizes the purpose of “evaluation,” and “selection” pays attention to serving the society through the management function of evaluation and talent selection but ignores the aesthetic education function and development function of evaluation. Evaluation emphasizes the guidance of external objectives but ignores the objectivity of the needs of the evaluation object and cannot focus on stimulating the internal motivation of the evaluation object, so as to promote the positive development of the subject of the evaluation object, and most of the evaluation objects are in a passive position. The evaluation is to use standardized examination or paper and pencil test as the only means to evaluate students. Teaching evaluation often only pays attention to the level of scores and ignores them. The objectivity of the evaluation object is based on the degree of effort: too much attention is paid to the evaluation result and the meaning of the evaluation process itself is ignored; teaching evaluation is a partial and nonholistic objective evaluation. For example, the school considers students’ progress to higher education (advanced to higher art colleges) and awards in various competitions at all levels that students participate in as important indicators for evaluating music teachers’ teaching performance. The school organizes relevant personnel to attend classes in the classroom and scores according to a predesigned scale, and then calculates the total score uniformly to determine the pros and cons of the class. This kind of evaluation work is carried out in some parts of the chain. Therefore, they are not scientific enough or comprehensive enough for teaching evaluation. The abovementioned various teaching evaluations affect a series of operation sequences and judgment methods, which make the functional structure of evaluation deviate from the track of quality education, and thus, the content and methods of music teaching cannot be truly reformed. At present, the evaluation of music teaching in my country mainly presents the following basic characteristics:(i)
Evaluation content-excessive reliance on subject knowledge (a static knowledge), especially knowledge in textbooks: teachers rely on participating in textbooks to strengthen the knowledge and knowledge of textbooks. Analysis and practice of skills. Ignore the evaluation and analysis of students’ emotions and attitudes, the internalization and integration of knowledge and skills.(ii)
Evaluation subject-teachers are the mainstay, ignoring evaluations at the same level and evaluations from different sources, for example, parent evaluation, student evaluation, manager evaluation, and multiple evaluation. Although some students self-evaluate, in general, students are still in a negative position to be evaluated.(iii)
Evaluation results—Most of the evaluation results pay attention to the final results, pay more attention to horizontal comparison, and emphasize the conclusion of summary evaluation, while ignoring the progress of each stage and the scientific process of the result feedback. Therefore, teaching evaluation has not achieved good results.(iv)
Evaluation index—single: the status quo places more emphasis on commonality and general trends, ignoring the differences and uniqueness between individuals.(v)
Evaluation method—monotonous: pay more attention to “quantification” and ignore “qualitative” evaluation. Because the formulation of reliability and validity is a description of quantification, it is difficult to describe a person’s overall characteristics as a result of quantification, and “qualitative” evaluation focuses on the process and can promote the development of the individual.(vi)
Evaluation methods—traditional paper and pen methods are mostly used, and there is a lack of techniques and methods that reflect the latest evaluation ideas.(vii)
Evaluation implementation process: the current evaluation implementation process is closed, static, and lacking in flexibility and dynamics. The evaluation process should be dynamic, allowing multiple evaluations.In recent years, with the rapid growth of knowledge, the original knowledge structure can no longer meet the needs of the development of the times. Music teaching has shifted from focusing on skills to focusing on cultivating students’ emotions, attitudes, and values. Music teaching evaluation has shifted from summative evaluation to formative evaluation; from partial evaluation to overall and comprehensive evaluation; from passive waiting for evaluation to active participation in evaluation development. In terms of evaluation theory, it emphasizes the evaluation of comprehensive qualities such as the ability to solve practical problems, innovative ability, practical or practical ability, good psychological quality and scientific spirit, and positive learning mood. Evaluation is no longer for selection and screening, but for the growth and progress of students. Evaluation is no longer “selecting children suitable for education” but “creating education suitable for children.” Evaluation should be expressed in terms of motivation and integration functions. To evaluate a teacher’s teaching is to enable the teacher to understand his own development direction in his evaluation and self-evaluation, so as to use the power of evaluation to integrate his/her teaching abilities and gain lessons in class [21, 22]. Better, complete the teaching work better. With the continuous deepening of reform and opening up and the introduction of advanced foreign education and teaching methods, art education, especially music education, as an important part of quality education, has received more and more attention. In recent years, the evaluation of music teaching has developed rapidly. In the process of music teaching evaluation, people expect a more scientific and feasible way to make an objective and feasible evaluation of teaching quality; hope that through evaluation, it will help teachers and students diagnose problems in the teaching and learning process and improve teaching strategies. We should make clear the direction of our efforts. It is hoped that through evaluation, communicating the existing problems, improving teaching strategies, and exchanging information, teachers can continuously improve education and teaching methods and improve teaching ability and level. We hope to promote the continuous development of students' personality through evaluation. And we hope to adjust the school management strategy and improve the overall level of school education and teaching through evaluation.


## 3. Method

Machine learning is a discipline that studies how to use machines to simulate human learning activities. It is the study of machines to recognize existing knowledge, acquire new knowledge and new skills, continuously improve performance, and achieve self-improvement. The initial research purpose of machine learning is to make computer systems have human learning capabilities in order to realize artificial intelligence. It has mainly experienced three processes: the enthusiastic period (from the mid-1950s to the mid-1960s), the calm period (from the mid-1960s to the mid-1970s), and the revival period (from the mid-1970s to the mid-1980s). Among them, the successful holding of the first machine learning international academic exchange seminar at Carnegie Mellon University in 1980 marked the rise of machine learning research all over the world. Nowadays, with the continuous development of computer technology, human beings have collected and accumulated a large amount of data in various fields, such as search engines, geological prospecting, speech and handwriting recognition, information security, robot application, securities market analysis, and other fields. However, how to analyze the potential laws from these massive data has become a common demand in almost all fields. This is why machine learning technology has received increasing attention. At present, machine learning has become another important research field of artificial intelligence applications after expert systems (subject to the bottleneck problem of knowledge acquisition), and it has become one of the most active and promising fields in computer science. Currently, the widely used machine learning methods mainly include decision trees, SVM, Bayesian theory, and RFs.Decision tree algorithm is one of the most classic machine learning algorithms. It is an algorithm that classifies and learns data through a top-down, well-organized process. The purpose of the decision tree algorithm is to recursively divide the observations into mutually exclusive subgroups until there are no more differences in the given statistics. Among the statistics used to find the classification attributes of tree nodes, information gain, gain ratio, and Gini index are the most commonly used. Usually, iterative dichotomy (ID3) uses information gain, C4.5 and C5.0 use gain ratio, and classification and regression tree (CART) uses Gini index. The support vector machine tries to find a hyperplane to separate the classes, minimize the classification error, and maximize the margins. SVM is a good classification and regression technique proposed by Vatnik of Bell Laboratories, AT&T. Through calculating the probability of an object belonging to a certain category, the Bayesian classifier classifies the samples. The theoretical basis of its classification is Bayes’ theorem. According to the Bayesian formula, the posterior probability of an object is calculated according to the prior probability, and the class with the largest posterior probability is selected as the class to which the object belongs. In other words, the Bayesian classifier is an optimization in the sense of the minimum error rate. Random forest is a type of ensemble learning, which is an algorithm that integrates the classification effects of multiple decision trees. It consists of multiple base classifiers, and each base classifier is a decision tree. Each decision tree acts as a separate classifier, independently performing learning and classification prediction. These predictions are finally integrated to obtain a total prediction that is better than all the individual classifiers. There are many performance indicators for evaluating the classification model based on the correctness of the model classification decision. Suppose that in a binary classification task, the value of the class variable can be assumed to be positive (P) and negative (N). The classification of actual positive cases (P) correctly marked as positive cases is named true cases (TP), and the classifiers where actual positive cases are incorrectly marked as negative cases are false-negative cases (FN). In a similar manner, actual negative examples (N) that are correctly marked as negative examples are regarded as true-negative examples (TN), and actual negative examples that are incorrectly marked as positive examples are regarded as false-positive examples (FP).


## 4. Experiment and Result Analysis

Data collection and characterization. We selected the student evaluation data of a university in recent two years and randomly selected 3150 scoring data, of which the number of positive samples is 1769 and the number of negative samples is 1183. Each of scoring data has 37 variables, which come from students’ online scoring, involving classroom preparation, classroom performance, course implementation, case organization, homework correction, teacher attitudes, and course network construction. The range of values is from 1 to 5. The numbers on the scale from 1 to 5 point to “strongly disagree,” “disagree,” “neutral,” “agree,” and “strongly agree”; or “never,” “rarely,” “sometimes,” “often,” and “always.” The last variable is a binary variable, which is the student’s overall evaluation information of the teacher, where 1 means “unsatisfied” and 2 means “satisfied.” This variable serves as the class label of the data sample. Divide all samples into training set and test set. In order to ensure the balance of positive and negative samples, 70% of them were randomly selected from the positive and negative sample sets, totaling 2205 as the training set. The positive samples were 1377, the negative samples were 828, and the remaining 945 were used as the test set. The number of positive samples was 590, and the number of negative samples is 355.This article generates four classification models. Its performance is evaluated by the precision rate, recall rate, F1 value, and accuracy rate of the test data. In the decision tree algorithm, the gain ratio is used as the measurement index, and multipath partitioning is adopted, and the minimum value of each subinstance is set to 2. The confusion matrix of DS classification is shown in Table1.Table 1
Confusion matrix of DS classification results.
Actual valuePredictive valuePositive exampleCounter exampleTotalPositive example52862590Counter example38317355Total566379945In the parameter selection of the support vector machine classifier, a polynomial function is used as the kernel function. The confusion matrix of SVM classification is shown in Table2.Table 2
Confusion matrix of SVM classification results.
Actual valuePredictive valuePositive exampleCounter exampleTotalPositive example52070590Counter example30325355Total550395945Naive Bayes parameter selection. Since the sample feature value is a discrete value in the range of 1–5, we choose the naive Bayesian polynomial NB with polynomial distribution a priori, and the prior probability is calculated from the training set sample. The confusion matrix of the NB classification results is shown in Table3.Table 3
Confusion matrix of NB classification results.
Actual valuePredictive valuePositive exampleCounter exampleTotalPositive example51080590Counter example55300355Total560380945When the random forest classifier is used for classification learning, in order to prevent the occurrence of overfitting or underfitting, the number of base classifiers is selected as 10. Since the number of dataset samples selected in this article is not large, the decision tree is not limited depth. The classification results of confusion matrix of RF are shown in Table4.Table 4
Confusion matrix of RF classification results.
Actual valuePredictive valuePositive exampleCounter exampleTotalPositive example55040590Counter example28327355Total578367945Finally, use the evaluation index to compare the performance of the applied classifiers. It also can be seen from Table4 that the classifier gives similar results in the test dataset. When comparing the model performances, it is found that all the methods used in this article can effectively classify the “satisfied” and “unsatisfied” teacher performances, which are shown in Figures 1–3.Figure 1
Comparison of precision rates.Figure 2
Comparison of recall rates.Figure 3
Comparison of F1 and accuracy.The accuracy of all classifier models is at least 89%. The random forest classifier has the best performance, followed by SVM, and NB has the worst performance. The results of accuracy rate F1 and recall rate once again show that random forest is the best classifier. There are some differences in the results of other classifiers. It can be said that all classifiers can better predict the results of students’ evaluation of teachers. However, according to the given performance index, random forest is the best among all the selected classifiers.Then, analyze the evaluation indicators used in student evaluation, and dig out the importance of each evaluation indicator under each classifier. The management department can improve the evaluation and assessment indicators according to the importance of the indicators and formulate the assessment mechanism more scientifically. Among the four methods used, three methods, decision tree, support vector machine, and random forest, are selected for eigenvalue importance analysis.For decision tree classification algorithms, information gain (IG) is used to calculate feature importance for the decision tree. For each feature a, calculate the “information gain” obtained by dividing the sample set D with the attribute a. The larger the information gain, the more important the attribute.(1)GainD,a=EntD−∑v=1VDvDEntDv,where EntD is the information entropy of D:(2)EntD=−∑k=1ypklog2pk,where pkk=1,2,…,y represents the proportion of the k sample in this set D. In the decision tree classifier, the first three important variables are V23: “This class has helped me increase my knowledge and interest in the subject,” V13: “The teacher is very enthusiastic about this subject,” and V5: “The teacher is very enthusiastic about the subject.” The knowledge of the company is well mastered. The importance of features in support vector machines is usually considered the absolute value of the feature coefficient, that is, the weight corresponding to the independent variable. For the dataset used in this article, the three most important variables of SVM are V25: “Teacher’s rating (homework/examination) is fair,” V23: “This course helped me increase my knowledge and interest in the subject,” and V22: “I need to spend a lot of time preparing homework/exams.” In the random forest algorithm, the feature importance is calculated by dividing the sum of the Gini coefficients of each feature classification by the Gini coefficient of the total feature. First, calculate the importance of each feature based on a single tree, explore the contribution rate of each feature to each tree, and then take the average. The three most important variables for calculating RF are V22: “The homework/exam fully tests the knowledge taught”, V17: “The course materials (textbooks, handouts, cases, etc.) are perfect,” and V23: “This course helps me Increased knowledge and interest in the subject.”


## 5. Conclusion

In this article, we classified the online course evaluation data through machine learning methods and analyzed the importance of characteristics of student evaluation. The different dimensions of college courses and teacher effectiveness are measured by the online course evaluation. It has been proved by the experiments that the machine learning method accurately classifies teachers’ “satisfaction” and “dissatisfaction.” In this article, we used different classification techniques, which include decision tree algorithms (DS), support vector machines (SVM), Bayesian theory, and random forest (RF) methods. The results showed that performance indicators of all the mentioned techniques were around 90%. Among them, students’ personal interests and knowledge gained are the main basis for students to evaluate courses. These results can be used to improve the establishment and improvement of online evaluation indicators, so as to set more reasonable and scientific evaluation indicators.On daily life datasets, data mining techniques are proposed, especially the effectiveness of DS, SVM, NB, and RF in higher education data mining. This can help teachers carry out reasonable teaching reforms and experiments based on the analysis results in a timely manner and improve their own teaching ability. The analysis result of the feature importance of the classifier shows that there are many possible improvements in the design of the measurement indicators used in the teacher performance evaluation, and it provides a certain reference for the management department to improve the evaluation indicators.Generally speaking, in the process of teaching work, ordinary colleges and universities reform the teaching evaluation system of musicology, which can carry out the teaching work of musicology in a scientific and regular manner, and integrate the main tasks of musicology teaching in colleges and universities. Completed smoothly, so in the process of organizing classroom teaching activities, music teachers should respect the students’ personalities, make reasonable references to the teaching evaluation system, and give full play to the students’ learning enthusiasm, so that through classroom teaching activities, every student can obtain a certain amount of music knowledge. In this way, a large number of music talents in line with social development can be cultivated and the progress of music education can be promoted.



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