Music teaching strategy and educational resource sharing based on big data

Abstract

With the rapid development of the information age, big data technology has been widely penetrated into various industries, and has brought profound impact on its structure and operation mode. In the field of music education, big data provides advanced tools and platforms for teaching, and provides a new perspective for the formulation of music teaching strategies and the sharing of educational resources. The purpose of this study is to deeply study the music teaching strategies based on big data and make a comparative analysis with traditional strategies. Based on an extensive literature review, this study summarizes the basic concepts, core features and applications of big data in music teaching. In order to have a more comprehensive understanding of the actual effects of big data in music teaching, we designed a series of experiments to compare the performance of music teaching strategies based on big data and traditional strategies in terms of student learning outcomes, learning engagement, student satisfaction, teaching progress and efficiency. The results show that the music teaching strategy based on big data can better meet the personalized learning needs of students, improve the learning engagement, and significantly improve the teaching effect and the quality of resource sharing. This study provides scientific ideas and methods for music teaching, and hopefully provides beneficial enlightenment for the application of big data technology in the field of education.

Keywords

Big data music teaching strategy educational resource sharing

1. Introduction

In recent years, with the rapid development of information technology, big data technology has shown its core value in many research fields. Through the in-depth mining and analysis of massive data, big data technology has become a key tool to provide accurate and intelligent decision-making basis. Especially in the field of education, the application of big data is opening a new chapter in teaching methods and strategies. Music education, as an important way to inherit and develop human culture and art, needs to pay particular attention to the changes brought by big data. With digitalization and networking becoming the development trend of education today, it is particularly urgent to rethink teaching strategies and resource sharing methods in the field of music education. This change is not only reflected in the growing diversity and personalized needs of music education, but also closely linked to the digital transformation of the entire education sector.

Traditional music teaching, especially the teaching method that relies on offline and single mode, is facing unprecedented challenges. In this context, how to effectively integrate big data technology to adapt to the changes of modern music education has become an important research topic. Big data can not only provide more abundant teaching resources and diversified teaching methods for music education, but also help teachers and educational institutions better understand students’ needs and learning habits, so as to optimize teaching strategies. Therefore, it is of great significance to explore the application of big data in music education, especially its influence and transformation on traditional teaching strategies, to promote the development of music education.

In recent years, with the rapid development of information technology, big data and artificial intelligence technology have been widely used in the field of music education. Wei et al. emphasized that the application of AI technology in higher music education provides students with a more personalized learning experience, thus improving the teaching effect [10]. At the same time, Yang explored new perspectives on teaching electroacoustic music in a digital environment, pointing out that digital technology makes music education more interactive and creative [2].

At the level of basic education, Zhao analyzed the practice of integrating scientific computing visualization and computer music technology into music teaching, showing that these technologies can significantly improve teaching quality and efficiency [3]. In addition, Zhu discussed the application of Internet information technology in the teaching system of college music education, emphasizing the role of technology in resource sharing and teaching method innovation [4].

In terms of mobile device application, Dominguez-Lloria et al. evaluated the content of mobile device application in primary school music education, pointing out that the application of mobile technology can greatly enrich the content and form of music education [5].

Modern music education is developing towards the direction of technology integration, personalized teaching and resource sharing. In this process, the application of big data and artificial intelligence technology not only optimizes teaching methods and content, but also improves the utilization efficiency and accessibility of educational resources. Therefore, this study will focus on exploring the application and effect of these technologies in music teaching strategies and educational resource sharing.

This study will focus on discussing and comparing the different effects of traditional music teaching strategies and big data-based music teaching strategies in practical application, especially the efficiency and effect of educational resource sharing. Firstly, the core concept of big data technology and its application in music teaching are introduced, and the related data collection, processing, storage and analysis methods are systematically organized and analyzed. Based on the design and implementation of the comparative experiment, the differences between the music teaching strategy based on big data and the traditional teaching strategy in teaching effect and resource sharing are compared and analyzed. Based on the experimental results, the advantages of music teaching strategy based on big data in educational resource sharing and its application prospects in future music education practice are discussed.

At the theoretical level, in the current educational practice, although big data technology has been widely introduced and shown its unique value, the relevant theoretical system is not perfect in the specific application of music teaching and the correlation with the sharing of educational resources. This study aims to explore the application of big data in music teaching, enrich relevant theories, and provide a certain reference frame and theoretical support for subsequent research.

At the practical level, this study is expected to provide a new perspective of teaching strategies for music educators and a new approach based on big data for the implementation and optimization of educational resource sharing, with a view to better promoting the effective allocation and utilization of educational resources under the modern educational environment and further promoting the development and innovation in the field of music education.

2. Conceptual and theoretical basis

2.1 Big data overview

Big data is a concept that has risen rapidly in the field of information technology in recent years, referring to vast datasets that exceed the collection, storage, management, analysis, and interpretation capabilities of conventional software tools. Its core characteristics are often summarized in the “4V” model, as shown in Table 1.

Table 1
Description of 4V model

Characteristic	Description
Volume	The volume of data is growing rapidly.
Velocity	Speed of data generation and transmission.
Variety	Multiple forms of data sources, including structured, semi-structured, and unstructured data.
Value	Meaningful information extracted from this huge amount of data.

As shown in Table 1, these characteristics make the application of big data in various fields more extensive and in-depth. With technological advancements, the focus of big data has shifted from merely the scale of data to more importantly, how to extract valuable information and knowledge from it to support decision-making, product innovation, and service optimization In the field of education, especially in the field of music education, the application of big data presents new trends and opportunities.

In music education, for example, big data can be used to analyze students’ learning habits and preferences to provide personalized teaching content and methods. For example, some online music course platforms, by collecting and analyzing students’ learning data, can provide customized learning paths and recommendation systems to help students learn music more effectively. In addition, by analyzing music discussions and feedback on social media, teachers and curriculum designers can adjust teaching content in time to better match current music trends and student interests.

This application of big data not only makes music teaching more efficient and personalized, but also provides a new perspective and method for music education research. Big data technology provides many possibilities for change and innovation in the field of music education through deep mining of massive data.

2.2 Traditional strategies of music teaching

Music teaching has formed a series of classical and effective teaching strategies in the long history of evolution. Traditional music teaching strategies often emphasize the close interaction between the individual and the teacher, and believe that musical skills and perceptions are mainly cultivated through the teacher’s demonstration, guidance and feedback [6, 7]. In this mode, students often need to engage in a great deal of imitation and repetition to achieve a natural outpouring of technical proficiency and musical expression.

Traditional music teaching also attaches great importance to collective cooperation and exchange. The practice of chorus, orchestra and chamber orchestra, for example, not only trains students’ musical skills, but also develops their teamwork and communication skills. This practice of teamwork is often seen as an important way to develop students’ musical perception, listening, and ensemble skills.

2.3 Significance of educational resource sharing

Educational resource sharing plays a crucial role in today’s educational environment. This philosophy focuses on the effective integration, distribution and use of various educational resources [8], including but not limited to instructional content, curriculum design, teaching materials, multimedia resources, and valuable experiences and insights from educators.

Educational resource sharing can significantly reduce the overall cost of education. By avoiding duplication of production and procurement, schools and other educational institutions are able to use available resources more cost-effectively and efficiently, especially in resource-limited Settings. Resource sharing also promotes the improvement of education quality and the enhancement of teaching efficiency. With the rapid dissemination and wide application of quality educational resources, more students and educators can benefit, thus achieving more equitable and balanced educational opportunities. Sharing on a larger scale drives global education collaboration and innovation, enabling best practices and success stories to cross geographical and cultural barriers and contribute to the advancement of education worldwide [9].

2.4 Interaction and application analysis

2.4.1 Interaction between big data and music teaching

With the development of big data technology, the interaction between it and music teaching is deepening. Music teaching has traditionally relied heavily on intuitive teaching methods and empiricism, and big data has brought unprecedented depth and breadth to the field.

Big data can provide educators with detailed feedback on students’ musical learning efficiency by analyzing their study habits, practice patterns, and rate of progress. For example, teachers can understand students’ common mistakes in a musical instrument practice through big data analysis, so as to give more targeted teaching guidance. Can assist in personalized education in music teaching. By analyzing each student’s learning path and preferences, educators can provide more customized learning plans and materials to ensure that the learning content is more closely aligned with the needs and interests of students. It can also play a role in the research of music teaching. Through the analysis of a large number of students’ learning data, researchers can have a deeper understanding of the patterns and rules of music learning, provide a more scientific basis for music education methods, and make music education more scientific, accurate and efficient.

2.4.2 The application of big data in resource sharing

Through the analysis of big data, this study can deeply understand the utilization rate, popularity, and effectiveness of various educational resources [10]. This means that educational institutions can optimize or adjust resource allocation and promotion strategies based on data feedback, ensuring the efficient use of limited resources.

Big data analysis can provide user behavior analysis for educational resource sharing platforms, so as to provide more personalized resource recommendation. For example, if a student frequently searches for teaching resources about piano on the platform, then the platform can recommend more relevant piano teaching videos, articles or tools based on this behavior. It can help the educational resource sharing platform to forecast the demand. Through the analysis of a large number of user data, the platform can predict the demand for a certain type of resources in a certain period in the future, so as to collect and integrate resources in advance. Big Data can also enable educators to continuously improve and update their teaching resources through in-depth analysis of user feedback and usage data, ensuring that they always maintain the most efficient teaching results.

To sum up, the application of big data in educational resource sharing provides a powerful tool for resource allocation, recommendation, prediction and optimization, thus making resource sharing more efficient, accurate and targeted [11].

3. Data collection and collation

3.1 Data source

To systematically explore the interaction between big data, Music Teaching strategy and educational resource sharing, this study designs a Music Teaching Experience and Application Questionnaire (MTEA-Q). The scale aims to collect relevant data about music educators’ daily teaching experience and the use of big data.

The MTEA-Q scale is a data tool designed for the specific needs of this study. Considering the high usage rate of electronic devices among college music educators, this study chose to convert this scale into an online questionnaire to facilitate data collection and ensure the accuracy of data. The scale covers several key areas such as music teaching strategies, resource utilization, and big data experience, and each area includes a series of related questions. Respondents were asked to rate their own teaching experience. Part of the MTEA-Q scale is shown in Table 2.

Table 2
Partial contents of the MTEA-Q scale

Domain	Problem	Rating range
Teaching strategy	In your daily teaching, do you think the current teaching progress is appropriate?	1 (Too fast) – 5 (too slow)
Resource utilization	How often do you use online music teaching resources in your classroom?	Yes/no
Big data experience	Do you think the teaching materials recommended based on big data meet the needs of students?	1 (non-compliant) – 5 (fully compliant)
Interactive feedback	Is the feedback from your students in your online music course timely?	1 (very untimely) – 5 (very timely)
Practice after class	What do you think of students’ after-school practice at home through online platforms?	1 (rarely practiced) – 5 (Often practiced)
Teaching interaction	How active are the students in the music class?	1 (very low) – 5 (very high)
Adaptability of teaching materials	Do you think the music materials used are adapted to the actual level of students?	1 (not adapted at all) – 5 (very adapted)
Real-time feedback	How often do you use big data tools to provide real-time feedback on your students’ progress?	Yes/no

3.2 Data collection method

To comprehensively and accurately collect data related to the application of big data technology in music teaching and the sharing of educational resources, the method of combining online survey and automatic data capture is selected in this study.

3.2.1 Data acquisition technology

Online survey tools: In order to more accurately understand the needs and feedback of music teachers and students, this study developed and distributed the MTEA-Q scale using an online survey platform such as Google Forms. The scale was designed to evaluate the effect of music teaching and to collect views on the sharing of music education resources. The design of the online survey takes into account the diversity of questions and comparability of answers, ensuring a high degree of validity and reliability of the data collected.

Automatic data collection: In order to obtain objective usage data, this study cooperated with several online music education platforms and used API and web crawler technology to automatically collect students’ learning behavior data on these platforms. The data collected includes, but is not limited to, click-through rates of resources, learning time, user interactions, etc. In addition, in order to ensure the accuracy and comprehensiveness of data collection, we regularly calibrate and test the collection tools, and conduct data verification with the platform to ensure the authenticity and integrity of the collected data.

3.2.2 Data storage technology

Cloud Storage: Given the nature of big data, this study chose to use a cloud storage service, such as AWS S3 or Google Cloud Storage, to store the collected data. These services provide not only sufficient storage space, but also a high degree of data persistence and reliability. When choosing a cloud storage service, this study pays special attention to its data security and backup mechanism to guarantee the integrity and accessibility of data under any circumstances.

Relational database: For structured questionnaire data, this study used a relational database (such as MySQL or PostgreSQL) to store it. This method makes data query and analysis more convenient and efficient. In addition, to enhance data security, we have adopted multiple layers of encryption and access control measures to ensure the confidentiality and integrity of sensitive data.

Non-relational database: Considering that music learning behavior data may be unstructured or semi-structured, this study also uses non-relational databases (such as MongoDB) to store this part of data. This helps to process and query data more flexibly. To further protect data security, we have implemented strict data management and monitoring systems in non-relational databases.

In the process of data storage, the research always pays attention to data security and privacy protection. Any information associated with personal identity is desensitized to ensure data security and privacy.

3.3 Data processing and analysis

3.3.1 Preliminary data processing

After collecting the data, the preliminary processing of the data is carried out first to ensure the accuracy and efficiency of the subsequent analysis.

Step 1.
Data cleansing: For data automatically captured from the music education platform, outliers are detected and removed, for example, records with a learning duration of 0 or abnormally high.
Step 2.
Redundant data deletion: For the same questionnaire submitted multiple times, the study retained data from the last submission and deleted previous duplicates.
Step 3.
Data conversion: Converting all non-numeric data into numeric format, for example, converting “yes” and “no” in a questionnaire to 1 s and 0 s.
Step 4.
Data standardization: Since different questions may have different scoring ranges and criteria, in order to make the data of each question can be compared and analyzed on the same scale, the research standardized the data. Here is the most commonly used Z-score standardization method. Equation (1) is shown.

$\displaystyle Z=\frac{x-\mu}{\sigma}$ (1)

Where $x$ is a certain data point, $\mu$ is the average value of the data, $\sigma$ is the standard deviation of the data.

Through the above preliminary data processing steps, the research ensures the quality and consistency of the data, and provides a solid foundation for subsequent in-depth analysis and research.
3.3.2 Data in-depth analysis techniques

(1) Descriptive statistics: The mean, median, standard difference and other statistics of each indicator are calculated to provide a basis for subsequent analysis, as shown in Eq. (2).

$\displaystyle\mu=\frac{1}{N}\sum\limits_{i=1}^{N}{x_{i}}$ (2)

Where $\mu$ is the mean, $N$ is the number of samples, $x_{i}$ is the value of each sample.

(2) Correlation analysis: Pearson correlation coefficient is used to analyze the correlation between different indicators. Equation (3) is shown.

$\displaystyle r=\frac{\sum\limits_{i=1}^{N}{\left({x_{i}-\bar{x}}\right)}\left% ({y_{i}-\bar{y}}\right)}{\sqrt{\sum\limits_{i=1}^{N}{\left({x_{i}-\bar{x}}% \right)^{2}}\sum\limits_{i=1}^{N}{\left({y_{i}-\bar{y}}\right)^{2}}}}$ (3)

Where, $r$ is the Pearson correlation coefficient, $x_{i}$ and $y_{i}$ is the observed value of the two variables respectively, $\bar{x}$ and $\bar{y}$ is the mean value of the two variables respectively.

(3) Regression analysis: Linear regression model was used to explore the relationship between music teaching strategy, resource utilization and big data experience rating. Equation (4) is shown.

$\displaystyle y=\beta_{0}+\beta_{1}x_{1}+\beta_{2}x_{2}+\varepsilon$ (4)

Where, $y$ is the dependent variable, $x_{1}$ and $x_{2}$ is the independent variable, $\beta_{0}$ , $\beta_{1}$ and $\beta_{2}$ is the regression coefficient, and $\varepsilon$ is the error term.

Through the above methods, this study conducted an in-depth analysis of the data, aiming to dig out the internal connection between music teaching strategies and big data technology from the data, and provide valuable guidance for the field of music education.

4. Comparative experimental design

4.1 Experimental purpose and basic process

The purpose of this study is to explore the difference between big data-based music teaching strategies and traditional music teaching strategies in teaching effect.

Basic procedure of the experiment:

(1)
Preparation before the experiment: ensure that students and teachers in the experimental and control groups have similar backgrounds and experiences.
(2)
Initial assessment: Baseline tests of music knowledge and skills were performed on both groups of students.
(3)
Teaching implementation: The experimental group adopted the music teaching strategy based on big data, while the control group adopted the traditional method.
(4)
Evaluation after the experiment: At the end of the experiment, the music knowledge and skills of the two groups of students were finally tested, and students’ feedback on the teaching method was collected.
(5)
Data analysis: Conduct an in-depth analysis of the collected data to verify the above hypothesis.

Duration of experiment:

The experiment lasted for 3 months. This period will include regular testing, feedback collection and data analysis. Such a time span is considered long enough to capture the difference in effectiveness of different teaching strategies.
4.2 Sample and participant screening

4.2.1 Sample screening

In this study, two universities with similar geographical location, facilities and subject strength were selected as research objects to ensure the representativeness of the samples and the fairness of the comparison. In the experimental group, A University implemented a music teaching strategy based on big data. As the control group, B University maintained the traditional music teaching strategy. To further improve the diversity of the sample, we also considered the historical background and current teaching trends of the two universities in the field of music education.

4.2.2 Participant screening

Students: Thirty first-year music-related majors from each university were randomly selected as participants in the experiment. To increase the diversity of the sample, we also took into account students’ gender, age, cultural background, and prior music learning experience. This helps to improve the general applicability of the experimental results.

Teachers: Three teachers from each university were selected to participate in the experiment. These teachers not only have at least 5 years of teaching experience in the field of music education, but also have different teaching styles and backgrounds to increase the representativeness of the sample.

Specific data are shown in Table 3.

Table 3
Overview of participants

Sort	University A (Big data strategy)	University B (traditional strategy)
Number of students	30	30
Number of teachers	3	3

Before the experiment, teachers at A University were trained on the application of big data in music education, and related software tools and textbooks were provided. For B University teachers, in addition to regular teaching training and teaching materials, special emphasis is placed on the application of traditional teaching methods. All students and teachers involved in the experiment signed informed consent forms before the experiment began to ensure their right to know.

To further improve the science of the experiment, the study controlled for other possible interfering factors, such as learning environment, teaching time and teaching materials, to ensure that the two groups were consistent in these aspects.

4.3 Teaching strategy overview

4.3.1 Strategies based on big data

Definition: Music teaching strategy based on big data is a strategy that utilizes a large amount of student data (such as students’ learning behavior, feedback, test scores, etc.) to carry out personalized teaching and optimize teaching content and methods. Its specific characteristics are shown in Table 4.

Table 4
Characteristics of teaching strategies based on big data

Peculiarity	Description
Personalized teaching	Provide a learning path based on each student’s learning progress and preferences
Dynamic adjustment	Collect and analyze student data in real time and adjust teaching strategies
Recommendation system	Recommend resources that match your student’s learning situation and preferences
Prediction and intervention	Predict students’ learning difficulties and intervene in advance

Based on the characteristics shown in Table 4, relevant specific applications can be carried out in the experiment:

(1)

Resource recommendation: For example, to recommend relevant music theory and practice teaching materials for students who like popular music.

(2)

Learning path design: For example, a more basic and systematic learning path is designed for students with weak foundation, while a more in-depth and challenging learning path is designed for students with good foundation.

(3)

Real-time feedback: For example, when a student scores below the average in a music theory test, the system will immediately provide relevant learning suggestions and supplementary materials.

(4)

Interaction and cooperation: Students are encouraged to interact and cooperate based on data. For example, online music creation cooperation activities are organized, and students can choose partners based on the data provided by the system.

4.3.2 Traditional teaching strategy

Definition: Traditional music teaching strategies are mainly based on classical theories and practices of music education, emphasizing face-to-face interaction between teachers and students, fixed teaching schedules, and unified teaching materials and methods.

Core features and experimental correlation:

(1)
Uniform teaching plan: All students learn according to the same teaching plan, regardless of individual differences. In the comparison experiment, students in the traditional group studied at a predetermined pace and material.
(2)
Fixed materials: All students use the same set of materials and exercises. In the experiment, this meant that students in the traditional group would use a uniform classical music textbook.
(3)
Face-to-face teaching: teachers teach in person, emphasizing the immediate interaction between teachers and students. In the comparative experiment, this teaching method is used as the main teaching means of the traditional group.
(4)
Traditional evaluation: Students’ academic performance is evaluated mainly through traditional written and oral tests. In experiments, this will be used to measure the learning outcomes of traditional student groups.

Experimental strategy overview:

Compared with the group using the big data teaching strategy, the traditional group will adopt the traditional music teaching method. The specific application in the experiment is shown in Table 5.

Table 5
Application of traditional teaching strategies

Peculiarity Experimental application

Uniform teaching schedule The traditional group learned at a scheduled pace

Fixed textbook The traditional group uses classical music teaching materials

Face-to-face teaching Face-to-face teaching, real-time interaction between teachers and students

Traditional evaluation Students are evaluated through written and oral tests

As shown in Tables 4 and 5, there are obvious differences between traditional music teaching strategies and those based on big data, which provides an ideal background for the study to compare the effects of the two strategies.

With the introduction of big data technologies, these traditional strategies can be optimized, such as:

Personalized lesson Plans: By analyzing student learning data, each student can be provided with a personalized learning path rather than a uniform lesson plan.

Dynamic textbook selection: Adjust the content of the textbook dynamically according to the progress and preferences of the students, rather than using a fixed set of textbooks.

Enhanced interaction: Combine online interaction tools to provide more ways for students to interact with each other, not just face-to-face.

Diversified evaluation system: In addition to the traditional written and oral tests, students’ learning effectiveness can also be evaluated through multiple dimensions such as students’ online activities and project works.

Through this comparison, this study can better understand and evaluate the application value of these two strategies in modern music education.
4.4 Effect evaluation

Peculiarity	Experimental application
Uniform teaching schedule	The traditional group learned at a scheduled pace
Fixed textbook	The traditional group uses classical music teaching materials
Face-to-face teaching	Face-to-face teaching, real-time interaction between teachers and students
Traditional evaluation	Students are evaluated through written and oral tests

4.4.1 Evaluation criteria for teaching effectiveness

In this study, to compare and evaluate the actual effects of different teaching strategies and educational resource sharing methods, a set of specific teaching effect evaluation criteria was developed. The following are the main contents of the evaluation criteria:

(1)
Student learning outcomes

Knowledge mastery: Using music knowledge test to test students, through the correct rate of statistics, assess students’ mastery of music knowledge.

Practical Skills: Music practice and performance tests assess students’ musical skills and practical abilities.
(2)
Learning engagement

Online platform activity: Using big data to count students’ active time on music education platforms, the number of activities they participate in, forum posts and replies, etc.

Classroom engagement: Teachers assess students’ participation in class, frequency of questions, and activity in group discussions.
(3)
Student satisfaction

Teaching content satisfaction: Questionnaire survey is used to understand students’ satisfaction with course content, teaching methods and teaching resources.

Education platform satisfaction: Survey students’ satisfaction with online music education platform’s experience and functions.
(4)
Teaching progress and efficiency

Progress achievement degree: Compare the original teaching progress of the course with the actual progress to evaluate the achievement of the teaching progress.

Efficiency assessment: Evaluate the time required for students to complete the same learning task, and compare it with the expected time to assess the learning efficiency.

The above evaluation criteria combine qualitative and quantitative methods to ensure the comprehensiveness and objectivity of the evaluation. This set of standards will provide a strong basis for research when evaluating the effectiveness of teaching strategies and educational resource sharing.
4.4.2 The effect evaluation of educational resource sharing

To comprehensively evaluate the effect of music education resource sharing, the research relies on the data processing and analysis techniques in Chapter 3, combined with the teaching effect evaluation criteria, and constructs the following effect evaluation mechanism:

(1)
Resource utilization

By using the background data of the education platform, the download, viewing and use times of various resources are counted, and the average utilization rate of resources is obtained through descriptive statistics of the data. Equation (5) is shown.

$\displaystyle\text{Resource Utilization Rate }=\frac{\text{ Number of Times % Resources Accessed }}{\text{Total Number of Resources }}\times 100\%$ (5)
(2)
Correlation analysis of educational resources

Using the Pearson correlation coefficient, this study analyzes the degree of correlation between educational resources and explore which resources are often used together to understand the combined needs of students and teachers.
(3)
Resource satisfaction evaluation

Content satisfaction: Questionnaire survey was used to understand the satisfaction degree of students and teachers with the content of various music resources.

Format and usability: Evaluate the format of the resource and the ease of use of the platform to see if it meets user needs.
(4)
Resource impact assessment

For frequently used and recommended resources, further analysis of their impact on students’ learning outcomes and engagement, using linear regression model to explore the relationship between resource use frequency and learning outcomes. Equation (6) is shown.

$\displaystyle y=\beta_{0}+\beta_{1}x_{1}+\beta_{2}x_{2}+\varepsilon$ (6)

Where, $y$ is the learning outcome, $x_{1}$ is the use frequency of resources, $x_{2}$ is the students’ initial level of music knowledge, $\beta_{0}$ , $\beta_{1}$ , $\beta_{2}$ are regression coefficients, $\varepsilon$ is error term.
(5)
Resource coverage and diversity:

Assess the coverage and diversity of resources by counting the number of resources in different categories, formats, and levels, as well as the number of times they are accessed.

To better demonstrate the advantages of resource sharing, we introduce the following success stories:

Case 1: An online music education platform has successfully integrated music teaching materials from different cultural backgrounds around the world through resource sharing, enriching students’ learning experience and improving learning efficiency.

Case 2: A university promotes self-learning and exploration by sharing high-quality music teaching videos and lectures that enable students to gain more knowledge outside of class.

According to the above evaluation mechanism, the overall effect of music education resource sharing can be learned, and the use and popularity of various resources can be targeted to understand, which provides powerful data support for the design and improvement of educational resources in the future.
5. Data validation and analysis

5.1 Data verification technique

Using the correlation analysis method introduced in Chapter 3, the correlation test of various indicators is carried out. This step aims to determine the degree of statistical association between the various changes observed in the experiment, thus providing a basis for further analysis. The correlation data of this study are shown in Table 6.

Table 6
Results of correlation coefficient verification

Contrast of variables	Correlation coefficient
Music teaching strategy and resource utilization	0.78
Music teaching strategy and big data experience score	0.65

As shown in Table 6, there is a strong positive correlation between music teaching strategies and resource utilization, as well as between music teaching strategies and big data experience scores. This provides important guidance for further analysis.

5.2 Quantitative research on the effect of strategy implementation

5.2.1 Quantitative research on student learning outcomes

This study collected the scores of sample students in music knowledge test and music practice performance test after the experiment, and the statistical results are shown in Fig. 1.

Figure 1.

Average scores of students in the test (out of 100).

As shown in Fig. 1, the experimental group scored higher in both musical knowledge and skill compared to the control group.

5.2.2 Quantitative research on learning engagement

Through big data, the study counted the data of students’ activity and class participation on the music education platform during the experiment period, as shown in Fig. 2.

Figure 2.

Statistical chart of average learning engagement.

As shown in Fig. 2, students in the experimental group had a higher degree of activity and classroom interaction on the music education platform.

5.2.3 Quantitative research on student satisfaction

According to the course content and teaching method, the research conducted a questionnaire survey of students’ satisfaction. The full score is 5, and the statistical results are shown in Fig. 3.

Figure 3.

Average student satisfaction survey.

As shown in Fig. 3, students in the experimental group had relatively high satisfaction with the course content and teaching methods.

5.2.4 Quantitative research on teaching progress and efficiency

Through the statistics of students’ browsing data and browsing time on online learning platform, the research conducted a quantitative study on teaching progress and efficiency. The statistical results are shown in the figure.

Figure 4.

Quantitative teaching progress and efficiency.

As shown in Fig. 4, the superiority of the experimental group in terms of teaching progress and efficiency.

Based on the above data, music teaching strategies based on big data have shown superior performance compared with traditional teaching strategies in terms of learning outcomes, learning engagement, student satisfaction, teaching progress and efficiency, etc., which provides strong evidence for the value and potential of big data in modern music education.

5.3 Experimental limitations and future optimization direction

This study provides valuable insights and conclusions on big data-based music teaching strategies and educational resource sharing, but there are still some limitations in experimental design and execution. First, the sample selected for this study came from two universities with similar geographical location, facilities and disciplinary strength, which may limit the generalization of the findings. Universities with different geographical, cultural or educational backgrounds may have different teaching models and student behaviors. Second, although University of A has adopted a music teaching strategy based on big data, the practical application of different teachers may be different, and this unevenness may affect the learning effect of students. In addition, the duration of this study may not be sufficient to fully capture the impact of big data-based teaching strategies on students’ long-term learning outcomes.

In view of these limitations, the researchers hope to consider selecting multiple universities with different geographical locations, cultural backgrounds and disciplinary strengths in future studies to enhance the generalization ability of research results. At the same time, in order to ensure the effectiveness of big data teaching strategies, teachers should be provided with more in-depth and systematic training on big data tools. Long-term studies should be conducted to more fully understand the long-term impact of big data in music teaching.

6. Conclusion

6.1 Music teaching strategy suggestions based on big data

After a comparative study of big data and traditional music teaching strategies, this study found that strategies based on big data have obvious advantages in some aspects. Therefore, music education should make full use of the deep analytical capabilities provided by big data to more accurately identify students’ learning needs and preferences. Experimental data show that music teaching strategies based on big data can help improve students’ learning effect and participation. Therefore, educational institutions and teachers can adjust teaching methods and content by in-depth analysis of students’ online activity data, such as the number of active hours on learning platforms, the number of forum posts, and so on, to ensure that the actual needs and interests of students are better matched.

6.2 Suggestions and optimization of educational resource sharing

Educational resource sharing has become an important trend in the field of modern education. Through in-depth analysis of music teaching strategies based on big data and traditional teaching strategies, this study puts forward the following specific and feasible optimization suggestions:

(1)
Regular data analysis: It is recommended that educational institutions and platforms regularly use data analysis tools to evaluate the use of resources. Specific steps include:

Data mining techniques are used to analyze the frequency and preference of students and teachers in using different resources.

Collect and analyze user feedback regularly to understand the actual application effect of resources.

Identify resources that need to be optimized or updated based on data analysis results.
(2)
Continuous updating of the form and availability of resources: In order to adapt to the development of digital technology and big data, the form and availability of educational resources should be constantly changing. Specific recommendations include:

Develop and optimize educational resources for multiple devices and operating systems to ensure that they work well on different platforms.

In order to adapt to the trend of mobile learning, optimize the mobile access experience of resources, such as simplifying the user interface, improving loading speed and responsiveness.

Content is regularly updated to ensure that educational resources are up to date with the latest teaching theories and practices.
(3)
Enhance the interaction and participation of resources: In order to improve students’ learning interest and participation, educational resources should increase the interaction and participation of students. Implementation steps include:

Integrate interactive tools such as discussion forums, live Q&A, etc., to enhance interaction between students and teachers.

Design project-based learning activities that encourage students to actively participate and apply their knowledge.
(4)
Strengthen the personalization and customization of resources: Provide personalized and customized learning resources according to students’ learning needs and interests. Implementation methods include:

Use big data to analyze students’ learning patterns and provide personalized learning suggestions and resource recommendations.

Provide customized learning paths and resource packages to meet the specific needs of different student groups.

By implementing these specific steps and techniques, educational resource sharing can more effectively support the development of music education while meeting the diverse needs of modern students and teachers.
6.3 Conclusion

With the wide application of big data technology in various fields, the field of education is no exception. This study is devoted to exploring the differences between big data-based music teaching strategies and traditional music teaching strategies in teaching effect. Through the one-semester experimental design, the results clearly demonstrate that the music teaching strategy based on big data has breakthroughs in improving students’ music skills and knowledge, enhancing learning engagement and enhancing students’ learning satisfaction.

Big data strategies show obvious advantages in the quantitative research of students’ learning outcomes, which not only confirms its practical value in improving students’ musical knowledge and practical skills, but also highlights the potential value of big data in music education. In addition, when educational resources are combined with big data, not only the utilization rate of resources is improved, but also more personalized and diversified educational content choices are provided for students and teachers, which undoubtedly brings new changes and opportunities for traditional music education.

In the context of digital resources and online platforms increasingly becoming the primary means of music learning and communication, music teaching strategies based on big data will become a key driver of future education development. The results of this study provide useful insights for education researchers and practitioners, and also lay a solid foundation for the development and improvement of music teaching methods based on big data in the future.

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