College Music Education Research Articles

FEATURES AND PROBLEMS OF PROFESSIONAL VOCAL EDUCATION AND REFORM EXPLORATION IN MODERN CHINA

Vocal music teaching occupies an important place in the music education and teaching system of colleges and universities, and is also one of the core professional courses for music teaching in colleges and universities. Although vocal music teaching in colleges and universities in China has achieved good results, with the development of society, the traditional teaching model in the PRC has been unable to meet the needs of modern development teaching, especially in terms of concept as well as teaching content and method, which is serious hinders the development of modern teaching. Therefore, it is necessary to reform vocal teaching in Chinese universities. This article first briefly analyzes the current situation of vocal teaching in colleges and universities, and then suggests some strategies for reforming teaching that should be aimed at developing professional talents. The author talks about the need to reform modern teaching methods to diversify teaching and offer students flexible and targeted learning based on real situations and personal characteristics of students to meet the needs of social talent development.

Evaluation on Multimodal College Music Teaching Mode Based on Interactive Sentiment

Evaluation on Multimodal College Music Teaching Mode Based on Interactive Sentiment

The Growth of Contemporary Music Subject and the Reform of Music Teaching in Universities

In order to promote the growth of contemporary music and the reform of music, this article designs an improved collaborative filtering (CF) algorithm to solve the problem of sparse matrix in traditional recommendation algorithms. The data matrix is dimensionally reduced to find the nearest neighbor, so as to realize personalized recommendation of music teaching resources in colleges and universities. The test results show that the accuracy of the proposed teaching resource recommendation algorithm is improved by 22.56% compared with the traditional CF algorithm. The improved CF algorithm can provide more accurate prediction, and the recommendation effect of the improved algorithm is better than the original algorithm, which can effectively avoid the sparse matrix problem faced by the CF algorithm, and provide technical support for the development of contemporary music discipline and the reform of music discipline.

The Role of Current Computer Technologies in Developing Professional Competence of Music Teachers: A Model of a Personalized Educational Environment

The importance of the article lies in the fact that the profession of school music teacher today needs to create its own updated competence model of personal educational environment taking into account digital transformation and computerization of general education. The information competence of the music teacher, the issues of his professional development in working with music and computer technologies are taken into account. Analyzed the possibility of building a personal educational environment in the era of postmodern (including electronic) and their application in further professional activities. The author analyzes the possibility of introducing modern computer technology as a resource for the formation of musical needs of the teacher of the educational institution. In addition to professional competencies, some general requirements for music teachers today, which can be grouped into four groups: general professional, communicative, behavioral and analytical. The article presents a model of university music education in the postmodern era; ways of acquiring professional competence of a music teacher are considered; the stages of creating a model of personal educational environment of a music teacher in the postmodern era are described; the role of professional competence of a music teacher in the postmodern personal educational environment model is defined; the role of current music and computer technologies (MCT) in creating a model of personal educational environment of a music teacher was determined.

Application of Big Data Technology in College Music Education

Based on information technology to promote the upgrading of various industries, the core of Industry 4.0, the current internet big data has gradually become a hot spot of social development, and a huge amount of data gushes out. Music is the education of beauty, and it is a key to inspire people to be true and good. China has been a land of culture and music since ancient times, and has a very high position in the history of world music. Combining advanced information technology with traditional music education to promote the development of national music education is of great significance to quality education. Based on the high-speed development of the era of big data, this article studies the specific application of big data technology in college music education, helps college students to cultivate open thinking and improve their independent music learning ability through online teaching platform, and formulates different teaching plans according to students' music learning data.

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

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

Exploring the Diverse Practices of Artificial Intelligence in Blended Music Teaching in Colleges and University

Abstract With the development of the information age and the change in media technology, the education industry has also ushered in a new change, and this paper focuses on the application of artificial intelligence in blended music teaching in colleges and universities. The first part of the article utilizes artificial intelligence to appreciate music emotions, utilizing Thayer’s two-dimensional emotion model and emotion vectors to express emotions in music. Then, the short- and long-time feature extraction of music emotion is carried out separately and normalized. Then, a music emotion recognition model based on the CNN-SVM model is constructed by combining a deep learning network and a space vector machine model. The constructed music emotion recognition model and SPOC hybrid teaching are utilized to establish a blended music diversity curriculum in colleges and universities and practical exploration is conducted. The results show that the time range of the change in music emotion perception is about 2.5~3.2s, and the recognition success rate is more than 4/5 for all kinds of music emotion segments. The correlation between the effect of music teaching and music diversification practice and students’ satisfaction is less than 0.01. The diversification of music teaching in this study can effectively stimulate students’ learning interest and improve learning efficiency in the music classroom.

Introduction to the inheritance and development of national music culture in the teaching of music education in colleges and universities in the Internet era

Abstract Ethnic music culture is a rare and precious artistic treasure in all national cultures, and the inheritance and promotion of ethnic music culture is an essential part of music education teaching in colleges and universities. This paper delves into the characteristics of the development and dissemination of ethnic music culture in the Internet era, with the aim of studying the integration of ethnic music culture into music education teaching in colleges and universities through the use of Internet technology. Through the collaborative filtering algorithm, students’ preferences for ethnic music works are mined so as to recommend accurate teaching resources for college students on the ethnic music online teaching platform constructed based on the collaborative filtering algorithm. In order to fully prove the effectiveness of the teaching platform designed in this paper, the rough set theory and neural network algorithm are used to construct the online teaching quality evaluation model for college ethnic music. The questionnaire survey method is used to study the status quo of ethnic music teaching in colleges and universities, and it is concluded that at present, there are problems in ethnic music teaching in colleges and universities, such as insufficient concepts of students learning ethnic music and insufficient attention to ethnic music in colleges and universities themselves. Through the analysis of the effect of the recommended algorithm, the design of the evaluation index system, and the practical application, the quantitative assessment value of the teaching quality of the ethnic music online teaching platform designed in this paper in the practical application is 92.11, which indicates that the comprehensive rating of the quality of the ethnic music online teaching in colleges and universities in this paper is excellent.

Practical Teaching of College Music Courses Based on Vocal Rhythmic Pedagogy

Abstract Under the development needs of the times, cultivating students’ ability to master rhythms is crucial to improving the quality of English talent cultivation. The vocal rhythm teaching method is used to establish an optimization model for music teaching in colleges and universities in this paper. In this teaching model, a real-time music beat recognition method combining music styles is proposed based on recurrent neural network and long and short-term memory neural network, and the feature fusion of mutual attention mechanism is utilized to carry out emotion recognition of multimodal music, to accurately control the music learning effect of students. Concerning the effectiveness of the optimization model of music teaching in colleges and universities, it is applied to the teaching practice, and the data are quantitatively analyzed in terms of music recognition ability and teaching effect. The results show that the recognition rate of the multimodal music emotion recognition method for 10 kinds of emotion categories of folk songs reaches up to 0.98, and the mean value of the final music literacy assessment scores of the experimental class after the experiment is increased by 2.89-3.38 points compared with that before the experiment, and the positive classroom mood shows a very significant difference at the level of 5%. The application of the optimization mode of college music teaching based on vocal rhythmic teaching methods in the practical teaching of music courses can cultivate students’ interest in music, help students improve their aesthetic level, and promote their overall development.

The development path of micro-course concept and university music education mode under the background of “Internet+”

Abstract This study integrates the microcourse concept into the realm of university music education, leveraging the benefits of microcourses tailored specifically for this discipline. It introduces a problem-solving-oriented teaching model for university music microcourses. The paper delves into the unique attributes of music education microcourse resources and addresses the need for sharing these resources across universities. To this end, it advocates the use of the PBFT consensus algorithm, supplemented by enhancements to the algorithm itself, in conjunction with data security storage technologies to bolster the protection of educational resources in university music micro-courses. Furthermore, the research evaluates the effectiveness of the microcourse model in university music education by examining the independent learning capabilities and problem-solving skills of the learners. The analysis focuses on the impact of learning behaviors on the outcomes under the microcourse framework. The dimensions of learners’ independent learning ability are assessed in terms of implementation and adjustment, goal-setting and planning, and application and reflection. The findings reveal mean values of 3.9865±0.68024, 3.7696±0.68595, and 3.6508±0.60124 for these dimensions, respectively. Notably, the dimension of implementation and adjustment scored the highest, indicating that learners are capable of timely adjustments to their learning status in alignment with their educational goals and plans. The study concludes that support from music education micro-courses significantly enhances learners’ independence in learning, and the synergistic effect of this model presents distinct advantages.

Research on the Reform Path of Music Teaching in Colleges and Universities in the Era of Artificial Intelligence

Abstract In recent years, the rapid development of artificial intelligence technology represented by knowledge graphs and deep learning has provided an opportunity for educational innovation and learning mode change. A smart music learning model for colleges and universities is developed in this paper with the help of artificial intelligence technology. Learning data analysis is achieved through the use of a community discovery algorithm based on graph data in the model. In order to construct the learning community, the AGNES hierarchical clustering algorithm is used to cluster individual samples in the dataset. Learning big data and music professional ability are correlated through the mining of learner portrait features. Personalized learning paths are generated using the improved convolutional neural network. As experimental subjects, sophomore music majors at X institution were tested and analyzed for the teaching model in the end. The results show that the maximum learning interaction coefficient of the experimental subjects can be obtained as 5.34 and the maximum learning path coefficient as 2.84 under the smart learning mode. The correlation coefficients of the use of the smart learning mode with the usual test scores and the learning effort values are between 0.318 and 0.502. Teachers can obtain precise teaching data from this paper to quantitatively characterize subject competence goals and facilitate the smooth implementation of smart learning.

Curriculum Design and Implementation of Digital Music Teaching in Colleges and Universities

Curriculum Design and Implementation of Digital Music Teaching in Colleges and Universities

Correlation Analysis of Higher Education Music Teaching Quality and Students’ Artistic Literacy - Based on Regression Analysis Model

Abstract Music core literacy encompasses the essential skills students must acquire to form accurate concepts through music course learning. The enhancement of students’ artistic literacy within music education has emerged as a focal area of research. This study targets music majors at colleges and universities in H city, employing a questionnaire designed from dual perspectives: the quality of music teaching and the level of students’ artistic literacy. We applied open coding to the collected data, grounded in rooting theory, to formulate relevant research hypotheses. Subsequently, we assessed the reliability and validity of the questionnaire through factor analysis and conducted a preliminary analysis of the correlations using the Pearson correlation method. Furthermore, we developed a multiple regression model, positioning students’ artistic literacy as the dependent variable and music teaching quality as the independent variable, to explore and verify the interrelationships and hypotheses. The findings indicate that the equation can predict students’ artistic literacy: 0.882 + 0.255 × teaching ideology + 0.112 × teaching process + 0.176 × teaching ability + 0.225 × teaching effect. The results confirm that the quality of music teaching in colleges and universities significantly influences students’ artistic literacy levels. This research provides valuable insights for higher education institutions aiming to innovate music teaching methodologies and enhance the holistic literacy of their students.

Exploring the Current Situation and Innovative Development of Music Teaching in Colleges and Universities under the Background of Information Technology

Abstract The significance of emotion in college music education is becoming increasingly acknowledged. This paper delves into the nexus between music teaching and emotional engagement, advocating for a stronger emphasis on emotional connection within the curriculum. By selectively extracting middle and high-level music features, we aim to intensify the emotional resonance of music pieces. Utilizing the KGIFE model, we gather and analyze students’ emotional responses to music, enabling targeted feedback. Our findings suggest four innovative pathways for evolving music education in higher learning institutions. Comparative analysis reveals that music majors exhibit markedly higher levels of emotion recognition and musical emotion experience than their non-music counterparts, with noticeable differences of 0.1512 and 0.2654 respectively. Furthermore, the study highlights significant improvements in music teaching effectiveness, particularly in run-on training and dialect processing, evidenced by average scores of 3.565 and 3.346. These outcomes underscore the potential of information technology to enhance the delivery and impact of music education in colleges and universities.

Research on the digital construction mode of college music teaching curriculum based on knowledge mapping

Abstract The study uses the MKR (Multi-task Knowledge-aware Recommendation) model, which integrates knowledge graphs and multilayer perceptron networks, and focuses on mining users’ high-level music preferences from music information and user behaviors. The study first introduces the three core parts of the MKR model: the recommendation task module, the knowledge learning module, and the cross-compression unit. Then, by optimizing the MKR model, the A-MKR (Attention-based MKR) model is proposed, which introduces the attention mechanism to further improve the model performance. In the application of music teaching, the study explores the keyword analysis of music education, digital teaching methods and their effect evaluation. The experimental results show that compared with traditional teaching, the digital teaching model performs better in both field assessment and ability growth, and the error rate is significantly reduced. For example, the digital group scored 1.3 and 9 points higher than the traditional group in field assessment and ability growth, respectively, and the error rate was reduced by 5.8%. This study proves the effectiveness and practical value of knowledge mapping based on the MKR model in the digital transformation of music teaching in colleges and universities. It provides new perspectives and methods for future music teaching.

Exploration of the Teaching Model of Music and Art in Universities under the Cultivation of Core Competencies

Core competencies connect teaching objectives with teaching practices. The cultivation of core competencies is the fundamental task of implementing moral education and promoting the modernization of education in China. Under the guidance of core competencies, musical art education in universities should readjust its course, deepen teaching reform, optimize teaching models, comprehensively improve the teaching quality of music majors, and promote the long-term development of music teaching in universities. Based on this, this paper takes the cultivation of core competencies as the object of music and art education in universities, and explores its educational reality. The paper explores the path of modernization development of music education in universities under the concept of core competencies from five aspects, including creating a core nurturing environment, expanding music basic knowledge courses, enriching music literacy teaching content, optimizing music literacy teaching strategies, and strengthening music literacy teaching management. It aims to provide useful references for relevant research.

Exploring the Path of Integrating Traditional Music into Music Teaching in Colleges and Universities

Abstract Traditional music encapsulates the profound historical wisdom and cultural imprints of a nation, representing an invaluable asset within the enduring heritage of its people. It also serves as an essential resource for music education in higher education institutions. This study aims to delineate the methodologies for the productive incorporation of traditional music into the music curricula of colleges and universities. Utilizing a questionnaire survey, this research examines the current practices of integrating traditional music into academic settings while concurrently identifying and analyzing prevalent challenges encountered in this endeavor. A factor analysis was conducted to extract the principal components from the identified challenges, providing a structured understanding of the influencing factors. The findings from this study suggest that the primary determinants impacting the integration of traditional music into university music programs include the pedagogical approaches of educators, the educational environment, and the prevailing campus culture. Furthermore, the study proposes several effective strategies for the integration of traditional music into music education, which include fostering emotional connections with the music, emphasizing scientifically backed vocal techniques, and incorporating exemplary cases into the curriculum. These strategies aim to enhance the educational experience and cultural appreciation among students, thereby enriching the music education landscape in higher educational settings.

Research on the Path of Integrating Traditional Culture into Music Teaching in Colleges and Universities in the Era of Artificial Intelligence

Abstract As artificial intelligence (AI) matures, it presents challenges and opportunities for music education in higher education. This study investigates how AI can facilitate integrating and transmitting China’s rich traditional culture within modern music pedagogy. Utilizing literature review, surveys, and experimental teaching, our research demonstrates that AI enhances teaching efficiency and significantly boosts students’ engagement with traditional culture—evidenced by a 30% increase in their understanding and appreciation of traditional music compared to conventional methods. AI technology emerges as a powerful tool for preserving and digitalizing traditional music, thus sparking students’ interest and fostering a deeper connection with their cultural heritage. This paper suggests that AI can significantly improve music teaching quality and efficiency, offering a novel approach to artistic education and heritage preservation.

Exploration of Informatization Teaching Mode of College Music Courses in the Context of Traditional Music Culture

Abstract This paper discusses the implementation of informatization strategies in music teaching in colleges and universities, mainly through constructing an online teaching platform, using Bayesian nonparametric models and extreme gradient boosting algorithms to optimize the teaching process by classifying music and recommender systems. The study collected students’ specific information and learning styles, and found that most students solved problems by searching for tutorial books and consulting their classmates, and less often used online resources. The online teaching platform generated feedback reports based on the number of interactions, engagement and attendance. Classroom activities focused mainly on interaction and discussion in the first 15 minutes, while whole-class quizzes were conducted in the 18 to 30 and 40 to 50 minute segments. Finally, the teaching practice was evaluated through a questionnaire, and the results showed that the p-values of the five dimensions of aesthetic perception, artistic performance, cultural understanding, interactive learning and interest inquiry were significantly less than 0.001, indicating that the implementation of informative teaching strategies enormously helped to enhance students’ learning outcomes.

Construction of a Database for Vocal Music Teaching in Colleges and Universities Based on the Inheritance and Protection of the Central Plains Culture

Abstract Multi-cultural integration is an inevitable path to enhance the inheritance and protection of the Central Plains culture and to innovate the teaching mode of vocal music in colleges and universities. In this paper, we constructed a university vocal music teaching database system based on distributed database, and used semi-connection algorithm to query and optimize the data related to the integration of Central Plains culture into vocal music teaching resources. In order to make up for the defects of the semi-connection algorithm when facing a large number of tuples, the semi-connection algorithm is improved by introducing the idea of symmetric homology to realize the massive data scheduling of the vocal music teaching resources database. For the college vocal teaching database constructed in this paper, the query optimization test and performance test are carried out, and the teaching practice application is carried out with the vocal teaching database. The results show that the query throughput rate of vocal music teaching resources reaches 3.18*103MB/s under Read-only load when the number of running rounds reaches 16 rounds, and when the number of concurrent users of the vocal music teaching database is 1000, the CPU occupancy rate and memory utilization rate are 1.37% and 1.06%, respectively. By integrating Zhongyuan culture into the vocal music teaching database, more than 70% of the students indicated that it could enhance their interest in learning about Zhongyuan culture. The use of technology to realize the integration of vocal music teaching and the culture of the Central Plains provides a new reference for the inheritance and protection of the culture of the Central Plains.