Exploring the Learning Psychology Mobilization of Music Majors Through Innovative Teaching Methods Under the Background of New Curriculum Reform.

The research expects to explore the psychological mobilization of innovative teaching methods of Music Majors under the new curriculum reform. The relevant theories of college students' innovative teaching methods are analyzed under deep learning together with the innovation and construction of music courses. Thereupon, college students' psychological mobilization is studied. Firstly, the relationship between innovation and entrepreneurship teaching and deep learning is obtained through a literature review. Secondly, the music classroom model is designed based on the deep learning theory, and the four dimensions of the music curriculum are defined to innovate and optimize the music teaching model. Finally, the Questionnaire Survey (QS) is used to analyze the design classroom model. Only 15% of the 180 respondents understand the concept of deep learning, 32% like interactive music learning, and 36% like competitive comparative music classroom learning. And the students who study instrumental music have higher significant differences in learning motivation than those who study vocal music. In addition to classroom learning, 16% of people improve their music skills through music equipment. College students like interactive music classes and competitive comparison classes that can give more play to their subjective initiative. After the new curriculum reform, the music curriculum based on deep learning can stimulate students' interest in learning and participate in the mobilization of students' learning psychology. Therefore, in the future of music education and teaching, there is a need to pay more attention to students' psychological status. The research results can provide references and practical significance for the innovative teaching activities of music classrooms after the new curriculum reform.

Introduction

At the beginning of the twenty-first century, the eighth basic education curriculum reform has been vigorously developed in China, in which academic evaluation has become crucial. In this basic education curriculum reform, new evaluation concepts have continuously been introduced to schools and classroom teaching (Qian et al., 2018). The curriculum and cultural diversity have also been supported by many in higher education, but insufficient attention has been paid to the reform of students’ academic examinations and evaluations. The research on the theoretical teaching practice of the new curriculum reform has become a research hotspot. In-depth learning returns to the essence of learning, points out deep cognitive activities and thinking exercises, and focuses on self-development and social development in the learning process. Meanwhile, deep learning emphasizes the theoretical guidance to help teachers identify major music problems and can expand the depth of knowledge and transfer knowledge to students.

Students’ learning psychology with different learning objectives can be reflected by studying students’ learning motivation and self-efficacy in music teaching under the current music school environment (Rogoza et al., 2018). Students’ learning psychology includes learning psychology and stage performance anxiety. Thus, based on the current school education strategy, an equal and objective curriculum value system should be created to improve the quality of China’s music schools in the education cycle of output-input (college students going to society), thereby comprehensively understanding the learning psychological activities and music learning effects of various students. The comprehensive curriculum value system can provide some help and suggestions for improving the level of music teaching and reflecting teachers’ teaching content, teaching methods, and strategic teaching behaviors, and it also has some reference values for the implementation and formulation of relevant education policies (Wu and Song, 2019), thus comprehensively understanding students’ learning psychological activities and music learning effects (Chen, 2019).

At present, there are some problems in music teaching. Some Music Majors in local universities have narrow professional caliber, weak professional foundation, weak comprehensive quality, and creative ability, and are not enthusiastic about education. The reasons for these problems are various. The main reason is that the curriculum structure of Music Majors in local universities is unreasonable, the content is old and divorced from reality. Specifically, too much emphasis is placed on the discipline standard of music and the pursuit of the systematicness and integrity of single-subject courses, while there is a lack of horizontal connection, penetration, and integration between courses. Local universities should cultivate applied music education talents for basic education, who should have enough music practical skills and teaching ability, can be engaged in music teaching and research work in the field of basic education, and have a love of music education. Specifically, local universities should cultivate music professional applied talents with research spirit and a lifelong love of music and has a good foundation in music teaching, as well as the ability to inherit the Chinese nation’s excellent music culture and literacy. Therefore, the practical teaching of basic education is particularly important.

To better study the innovative teaching methods of Music Majors in higher institutions under the background of the new curriculum reform, this paper adopts the methods of Questionnaire Survey (QS), literature survey, and Field Investigation (FI). Specifically, the construction mode of music innovative teaching is studied under deep learning, and the teaching of music courses is innovated, in an effort to solve the problems in music teaching under the background of the new curriculum reform. Meanwhile, the innovative teaching methods are studied under the background of the new curriculum reform together with the mobilization of Music Majors’ learning psychology in higher educations to improve the understanding of deep learning, improve students’ psychological state during music learning, and lay the foundation for the innovative model of deep learning classroom in the future. This study creatively combines deep learning with music education and studies the possible psychological problems of students in the process of education. Additionally, combined with the current situation of music education, this paper makes a statistical analysis of the music items in the current teaching materials and obtains the optimized teaching methods of curriculum reform.

Concept Definition and Theoretical Basis

Innovative Teaching Methods

At present, there are many ways of innovative teaching methods, including independent inquiry classrooms, open teaching, mutual assistance-cooperation teaching, and talent display teaching. In classroom teaching, novel, ingenious, and creative teaching methods are effective to stimulate students’ potential and learning interest (Wu et al., 2019), thereby creating a happy learning environment. Only novel and diversified teaching methods can give full play to students’ imagination and cultivate their innovation and creativity (Wu Y. J. et al., 2020). Besides, innovative teaching methods enable students to fully feel the music, understand music, and express music (Wu W. et al., 2020). It has been argued that schools should develop independent inquiry classroom teaching methods to enable students to actively acquire knowledge, apply knowledge and solve problems, experience music, and learn music in practice, thus improving their ability to obtain music and process information. This is more than just a conclusion but rather, an experience (Lane et al., 2020). Inquiry teaching can fully reflect the modern educational concept of taking students as the main body and focusing on ability development (Yuan and Wu, 2020). Now, in music classroom teaching, more attention should be paid to students’ participation and exploration, the importance of students’ experience should be emphasized together with exploration and practice in teaching activities, and plenty of practice time should be given to students to practice with their hands and brains, thereby improving students’ cognitive level of music (Wu Y. J. et al., 2020). At the same time, it is believed that more effort should be exerted on the cultivation of students’ innovative and creative spirit to make music learning a pleasant and successful learning process (Zheng et al., 2018).

Concept of Deep Learning

Scholars worldwide have developed multiple genres of understanding of deep learning. Here, the view of the genre of ternary theory (Panagakis et al., 2021) is quoted, which is represented by Nelsonlarid et al. (2020) Particularly, the ternary theory has put forward the scale of deep learning (Yang et al., 2021), and deep learning is defined from three specific dimensions (Zaidi and Naqa, 2021). The first dimension is higher-order thinking, which uses the learning process to promote the construction of meaning and a more comprehensive understanding. For example, through the analysis and synthesis of materials, the value of information can be judged and used to solve practical problems. The second dimension is integrated learning, which establishes a connection between facts and ideas, old and new knowledge, or different knowledge (Krstikj, 2021). The third dimension is reflective learning, which analyzes one’s original cognitive process or the views of others, thereby improving students’ views on a music theme (Tien et al., 2021).

Higher-order thinking is a core feature of deep learning. According to Bloom’s cognitive classification, deep learning corresponds to four advanced cognitive levels of application, analysis, evaluation, and creation, pointing to high-level music thinking training (Hojatian et al., 2021). In-depth learning is different from shallow learning and advocates that based on critical reflection, students can fully perceive information, turn point and linear knowledge into a network structure through proactive analysis and synthesis, expand thinking and discover essence through meaning construction, break and reconstruct their cognitive schema through reflection and immersion, and finally realize adaptation (Wu and Wu, 2017). As one of the teaching concepts of music education, deep learning requires that teaching should not only consider the particularity of music education but also innovate the traditional teaching concept and knowledge concept. Music instructors under deep learning are required to combine music knowledge and practical skills, as well as to illustrate the hierarchy of music content. Music is intangible, perceptual, and non-semantic, and in-depth thinking can only be carried out effectively by fully considering these features. Listening, as one of the basic contents of teaching, can boost students’ attention and memory. In terms of aesthetics and culture, music education is supposed to cultivate positive emotion and aesthetic education and guide students to experience music expression and creativity.

College music classroom questioning and deep learning point to a higher level in thinking training, requiring students to carry out in-depth cognitive processing activities (Jin et al., 2021). In-depth learning has the following characteristics: 1: It emphasizes high-order thinking, which involves high-order thinking mode and complex cognitive processing activities, such as analysis, synthesis, application, and creation. 2: It emphasizes connection integration, requiring students to connect new information with existing knowledge, integrate it into their original cognition, and realize the assimilation and adaptation of knowledge. 3: It emphasizes meaning construction and requires learners to establish connections between new and old knowledge. 4: In-depth learning emphasizes transfer and application, which requires learners to accurately judge and grasp the new learning situation and then transfer and apply the newly learned knowledge and methods to effectively solve problems (Xu et al., 2021). 5: In-depth learning is problem-oriented and problem-solving is the ultimate goal of deep learning. In-depth learning requires learners to use their knowledge, skills, and methods to solve complex problems in real life. 6: In-depth learning focuses on critical reflection, which is a kind of reflective learning. In the learning process, deep learning advocates using metacognitive strategies to reflect on the thinking process and thinking results, thereby finding out the unreasonable cognition through self-monitoring (Pirrie et al., 2020) and realizing the transformation of cognitive concepts.

Analysis of Music Education

Curriculum reform usually includes three levels: curriculum connotation, curriculum evolution, and curriculum system. The curriculum reform put more emphasis on respecting and appreciating students in their relationship with teachers, and it helps and guides students in teaching, self-reflection, and cooperation. In the new curriculum reform, teachers are positioned as the disseminator of culture, the developer of potential, the promoter of learning, the companion of development, and the explorer of education. Students are believed to benefit from teachers’ new curriculum reform, such as up-to-date educational concepts, renovated teaching methods and classroom models, comprehensive knowledge structure and skills.

As a result, the courses get more and more detailed and deeper, while students’ knowledge is too narrow and their social adaptability is weak. In terms of course content, many music professional textbooks have remained unchanged for decades. Although some contents have been modified, they still fail to keep up with innovative teaching methods. The contents are old, non-standard, and non-supporting, and cannot reflect the research results of music discipline in time. In terms of teaching methods, there are common phenomena, such as mechanical training, indoctrination, and one speech hall, which have seriously affected the cultivation and shaping of high-quality music professionals. Compared with other disciplines, music learning, especially in the acquisition of music skills, depends on the individual knowledge transfer and experience transfer between teachers and students.

Music learning at the artistic level belongs to the tacit knowledge that can only be understood but cannot be explained. Therefore, the traditional music education model of oral and heart-to-heart continues to this day. In the field of instrumental performance and vocal music singing, the one-to-one teaching model is typical. The advantages and disadvantages of traditional music learning lie in the closeness of the system. Teachers can hardly exchange teaching experiences with each other, so their teaching can only be limited to the closed system of the traditional teaching model (Arculus, 2020). This is the inevitable result of the apprenticeship learning model but also the teaching link and teacher biography system formed under the oral and heart-to-heart teaching model. At the same time, the creation of a music teaching environment is also very important. A healthy and safe educational environment structured reasonably enables students to perceive and understand the teaching content in an all-around way. And the optimized environmental creation can facilitate students to learn comfortably.

Music plays a very important role in alleviating and stabilizing college students’ emotions to help them maintain a positive state and cultivate their positive personalities. The psychological defense mechanism of college students can be enhanced through the regulation of music. Moreover, music education can also be used for individual counseling. Instructive music can user students into their imagination safety island, encourage them to build self-confidence in life, and alleviate stress and anxiety. On the other hand, creative music can stimulate students’ positive emotions and potential through the learning and expression of music.

At present, some Music Majors in local universities have narrow professional caliber, weak professional foundation, weak comprehensive quality, and creative ability, and are not enthusiastic about education (Resta, 2020). Therefore, clear and accurate talent training objectives and positioning must be formulated. Meanwhile, the training specifications of music education teachers and music researchers, and social art teachers should be focused on in primary and secondary schools, and great attention should be paid to the learning and training of students in educational theory and teaching skills, as well as the latest research on music teaching methods worldwide (Tan, 2020). Then, three music textbooks commonly used in music teaching are selected and marked as A, B, and C, respectively, to count the number of music and popular music in the music textbooks. The results show that, among the three music textbooks, Textbook A contains the maximum pieces of music and pop music; Textbook C contains overall small pieces of music and pop music in teaching materials; the proportion of pop music in the total amount of music in Textbook B is relatively low.

Research Route

According to the background and requirements of the new curriculum reform, the psychological mobilization of college Music Majors is taken as the research object and judgment benchmark, and the deep learning and innovative teaching in music education are studied and summarized. At the same time, the reform of music classroom content in colleges is evaluated based on Psychology and the new curriculum reform (Zucchero and Gibson, 2019). Given the QS results, a new teaching method based on deep learning is designed, which provides some ideas for the teaching curriculum of a Music major. Based on the analysis of the learning psychology of Music Majors in universities and the particularity of music discipline, as well as the core idea of deep learning theory, it is believed that there are similarities between music classroom learning and deep learning in universities, which are reflected in four dimensions: cognitive level, knowledge construction (Good, 2021), goal tendency, and learning process. Specifically, these similarities are reflected in high-level cognitive thinking training (Popkova et al., 2020), the construction of the connection between old and new knowledge, the generation orientation of problem-solving, and the learning process of reflective music equipment (Tien et al., 2021). These four common features are the expression of the core idea of deep learning (Jorgensen, 2020), but also the essence of music learning in universities.

The so-called deep learning requires students to find the hierarchical and interactive relationship between knowledge units, and then build a non-linear network or modular knowledge structure. During integrated learning (Artacho et al., 2020), with the repeated integration of music information, the intersection of multi-disciplinary knowledge, and the integration of new knowledge and life experience, the old and new information continue to conflict and connect (Osman and Hirsh, 2020). Knowledge is understood based on practical experience in life, and students’ music knowledge structure has been dynamically reorganized, generated, and developed. Music knowledge units in different periods no longer exist independently but can formulate an overall knowledge architecture (Das et al., 2020), thus establishing a logical connection between chaotic and complex independent information points and realizing the construction of the connection relationship between new and old knowledge, as shown in Figures 1, 2. Thus, Figure 2 suggests that by assimilating new knowledge and integrating old knowledge in different stages, students can quickly master music courses and improve their learning efficiency.

FIGURE 1

Comparison of the total number of music and popular music in the three teaching materials.

FIGURE 2

Structural diagram of the construction of the connection between new and old knowledge under the orientation of deep learning.

Based on the structural diagram of the connection between new and old knowledge, combined with relevant theories, an innovative teaching method is proposed for music classrooms based on deep learning, and the teaching process is shown in Figure 3. Figure 3 signifies that the music classroom model based on deep learning improves students’ independent thinking ability and knowledge learning ability through continuous feedback teaching to students, thereby promoting the new curriculum reform of the music curriculum.

FIGURE 3

Flowchart of deep learning music class.

Questionnaire Survey and Model Design

Purpose and Method of Investigation

The aim is to understand the current situation of applying deep learning to classroom teaching in polytechnic school X and analyze the current cognition and understanding of college teachers and students on the application of deep learning to the music classroom through data statistics. The design steps of the QS are 1: The current situation of college music classroom teaching method based on deep learning is understood through the statistics of QS data; 2: Through the combination of QS data statistics and literature research, the significance of the data is analyzed to find out the regularity and commonality, thereby providing more real data and materials for the follow-up targeted suggestions and proving the inevitability of the application of deep learning to the teaching of music in universities.

This QS mainly makes a statistical analysis on students’ ideological cognition of deep learning, students’ preference for music classroom mode, t-test of students’ self-efficacy grade score, students’ learning motivation level, students’ self-energy efficiency level, and students’ initiative performance, thus making a comprehensive psychological analysis on college Music Majors.

Questionnaire Survey Design

Concerning the research on the learning psychological mobilization of the innovative teaching methods of deep learning on college Music Majors under the background of the new curriculum reform, the QS method is chosen to conduct the corresponding research. The QS is completed through the MM platform, mainly for sophomores and juniors Music Majors in universities. Additionally, members of the student union have been recruited to assist the QS distribution to ensure effective recovery. Totally, 200 QSs are distributed, and 180 are recovered, with a recovery rate of 90%. The reliability and validity of the collected QS are analyzed by SPSS 25.0. The results show that the designed QS has good reliability and validity. The purpose of this QS is to investigate and analyze the learning psychology of Music Majors in universities. The key questions of the QS are shown in Table 1. The QS involves 10 questions with individual options and scores. The respondents’ scores are collected through the QS to complete the research smoothly. It is ensured that the collected research data are only used for this paper, and there are no potential moral or ethical safety problems.

TABLE 1

Key questions design.

Questions:	Content and specific options	Scoring
Q1: How much do you know about deep learning?	M11: Very well	10
	M12: General	8
	M13: Understand	6
	M14: Do not understand	5
	M15: Don’t understand at all	3
Q2: What is the motivation for majoring in music?	M21: Instrumental music	10
	M22: Vocal music	3
Q3: What kind of instrument do you like?	M31: Classical musical instruments	3
	M32: Modern musical instruments	6
Q4: Do you spend much time learning professional music knowledge?	M41: There are many opportunities to learn music at ordinary times	10
	M42: There are few opportunities to learn music at ordinary times	5
Q5: What is your favorite music type?	M51: Classical music	8
	M52: Modern music	5
	M53: Rock music	6
Q6: What kind of music classroom model do you like?	M61: Traditional teaching style	6
	M62: Interactive communication	6
	M63: Competition comparison style	3
	M64: Other forms	4
Q7: What is the impact of learning music on you?	M71: Optimistic	2
	M72: Pessimistic	8
Q8: What’s your current music learning mentality?	M81: Positive	5
	M82: Negative	10
	M83: Nothing	15
Q9: Which music equipment are you best at?	M91: Piano	20
	M92: Violin	20
	M93: Guitar	20
	M94: Zither	20
	M95: Other	20
Q10: What’s your average weekly practice time for musical instruments?	Q101:2 h	3
	Q102:3 h	4
	Q103:4 h	5
	Q104:5 h	6

Analysis of Questionnaire Survey Results

According to the results of the QS, 81 respondents are male and 99 are female, accounting for 45 and 55% of the total respondents, respectively. Meanwhile, 38 students are 19–21 years old, 86 are 20 years old, and 56 are 21 years old, accounting for about 21%, about 48%, and about 31%, respectively. Additionally, 94 respondents are sophomores, and 86 are juniors, accounting for about 52 and 48%, respectively.

T-test Method

For the independent sample t-test, the original hypothesis should be constructed first, and then the statistics are established, based upon which the T statistics and the corresponding P-value are calculated. Finally, statistical inference is made at a given significance level.

Normally, the difference between the two groups of the normal distribution is tested to prove whether the high measure data of the two columns of the normal distribution are different, which can be achieved by t-test and ANOVA (Analysis Of Variance). The t-test is generally used for single factor and two-level analysis, while ANOVA can be used for multi-factor and multi-level analysis. Generally, the t-test can be divided into a single sample, paired sample, independent sample equal variance, and heteroscedasticity test. Paired samples are used for two data sets with one-to-one correspondence, and if there is no correspondence between the two data sets, it is an independent sample. Before the t-test of independent samples, the variances of the two data sets should be checked for equals, and either the equal variance test or heteroscedasticity test of independent samples is selected according to the test value. Noticeably, the data set of the t-test should be from the population satisfying normal distribution, and the samples must be random. Here, SPSS is used for t-test analysis of independent data sets. The specific operations follow.

Test variables and grouping variables are selected through (analyze)-(compare mean)-(independent sample t-test).

Click (OK) to output the result.

The results are analyzed according to the set significance level.

Students’ Ideological Cognition of Deep Learning

Through statistics and analysis of the QS results, the respondents’ answers to each question are studied. Figure 4 demonstrates the students’ ideological cognition of deep learning.

FIGURE 4

Students’ ideological cognition of deep learning.

Figure 4 indicates that only 27 respondents have a clear concept of deep learning, and half of the total respondents are unclear about deep learning, but 63 students know a little about the concept of deep learning. In short, there are still great misunderstandings about the curriculum reform method of deep learning for Music Majors in universities, which needs attention from students and teachers.

T-test of Grade Score of College Students’ Self-Efficacy

Through statistics and analysis of the QS results, the score of students’ self-efficacy grade is tested by t-test, and the significance level is set as 0.05, as shown in Figure 5.

FIGURE 5

T-test of grade score of college students’ self-efficacy.

Under the comparative analysis of the scores of sophomores and juniors in Figure 5, the self-efficacy of sophomores and juniors are 22.1167 and 20.9083, respectively. The average score of self-efficacies of sophomore Music Majors is higher than that of junior students. This is because sophomores have just entered the university. After 1 year of study, they are full of hope and expectation for their future. However, juniors are about to enter society. After 3 years of learning and training, their thoughts are more mature and rational.

Analysis of College Students’ Preference for Music Classroom Model

Through statistics and analysis of the QS results, this section makes a statistical analysis on college student’s preference for the music classroom model to clarify students’ views and feelings, as presented in Figure 6.

FIGURE 6

Results of college students’ preference for music classroom model.

Figure 6 illustrates that 41 of the 180 respondents hope to conduct deep learning in music through the traditional teaching method; 58% of the respondents like to learn music through the interactive classroom model; 65 students like the competition comparison method for music learning; additionally, 16 students prefer other ways of music classroom learning. The QS results also provide a reference for the future deep learning in the education model of the Music Majors in universities.

Analysis of Learning Motivation Level

Through statistics and analysis of the QS results, the learning motivation level of students toward different types of music is analyzed by t-test, and the significance level is set as 0.05, as depicted in Figure 7.

FIGURE 7

Level of learning motivation of different types of music.

Figure 7 reveals that there are differences in learning motivation levels among students learning different types of music. The QS results show that most students’ music learning motivation is affected by the degree of contact with the teaching content. Most teachers simply teach the content of teaching materials without considering the current situation or technological trends, and their teaching forms are relatively single. The independent sample t-test analysis shows that [t(200) = 4.59, P < 0.05]. The level of learning motivation of students learning instrumental music (score = 87.14, learning motivation = 9.52) is significantly higher than that of students learning vocal music (score = 82.98, learning motivation = 12.09).

Self-Efficacy Level Analysis

Through statistics and analysis of the QS results, the students’ self-efficacy level in learning different types of music is analyzed by t-test, and the significance level is set as 0.05, as outlined in Figure 8.

FIGURE 8

Self-efficacy level of students learning different types of music.

Figure 8 reveals that there are differences in the self-efficacy level of students learning different types of music The analysis results of the independent sample t-test show that [t(200) = 4.59, P < 0.05]. The self-efficacy level of students learning instrumental music (score = 105.6, learning motivation = 9.52) is significantly higher than that of students learning vocal music (score = 82.98, learning motivation = 12.09).

Initiative Performance Analysis

Through statistics and analysis of the QS results, the students’ initiative performance is statistically analyzed to obtain the integrity and reliability of the results, as evinced in Figure 9.

FIGURE 9

Initiative performance analysis chart.

Figure 9 demonstrates that most respondents are willing to participate in music-related activities and have high enthusiasm. They are willing to accept other music forms while focusing on appreciation so that music occupies a high position in the learning process, and the students are full of enthusiasm for music. According to their habits, needs, and interests, they can freely choose music activities and forms to participate in, which are all under their own control. The QS results show that only 18% of students can master the concepts of deep learning, while 35% of students cannot.

To sum up the above research and analysis results, students hope to change the singleness of teaching content, enrich the on-site discussion, and improve the enthusiasm and efficiency of music classroom through deep learning; students hope to increase interaction, learn and use flexibly through more visual teaching contents; at the same time, teachers’ thinking is changing toward deep learning thinking model. Most teachers know innovative teaching methods and the concept of deep learning. They try to let students use deep learning methods in the class. deep learning is helpful to music classroom teaching. Generally, most teachers are trained with the thinking model of innovative teaching, which provides technical support for the application of deep learning in music education. Both teachers and students believe that deep learning can give full play to students’ t initiative and stimulate students’ learning motivation, which plays a positive role. However, today, when using innovative teaching methods, they haven’t formed a unified way for deep learning, and rather the teaching methods are flexible and diverse and cannot achieve a unified learning effect.

Conclusion

Interest is believed to be the fundamental driving force of students’ music learning but also an important topic of psychology research. By cultivating students’ interest in the course content, it is possible to improve the teaching efficiency of music classroom education. It is also one of the practical significance of music education to awaken students’ self-awareness through knowledge learning. Therefore, under the background of the new curriculum reform, the psychological mobilization of college Music Majors’ deep learning and innovative teaching methods need to be studied in depth. To explore the changes of learning mentality of college Music Majors when learning music knowledge there is a need for music teachers to impart knowledge vividly and interestingly in the music classroom, introduce deep learning into music teaching, and build a new music curriculum teaching model. Correspondingly, it is essential for students to avoid wrong music learning habits to adapt to the new innovative teaching methods. According to the QS results, students hope to improve the unity of teaching content, enrich the on-site discussion, and improve learning enthusiasm and efficiency through deep learning; students hope that teachers can increase interaction through more intuitive teaching contents and avoid students’ rote memorization; this way, students can improve their learning enthusiasm, efficiency, and autonomous learning, as well as to understand their own ideological situation; students believe that teachers should improve thinking transformation and carry out deep learning. The data results show that among the 180 students represented by effective QS, only 27 students know the concept of deep learning; half of the respondents are not clear about the concept of deep learning, and 63 students only have a little understanding of the concept of deep learning; under the independent t-test, students learning instrumental music have higher learning motivation and self-efficacy than students learning vocal music; among the innovative teaching methods based on the new curriculum reform, the deep learning method, in particular, can alleviate the learning anxiety of music students and the phased anxiety of learning music.

However, there are still some shortcomings. The research results and the sample selection are limited, unable to cover all aspects. The improvement of music skills is a gradual success. It is critical for college Music Majors to cultivate good learning habits and the feature of perseverance. There is a demand for teachers to explore more novel and effective teaching methods suitable for Music Majors. The future research will encourage college Music Majors to consciously cultivate their music skills, improve their musical instrument performance ability, and actively participate in stage performance to reduce the anxiety in learning and performance and adapt to the rapid development of the future music field; moreover, the future research will further study the curriculum development model of music teaching under the background of the new curriculum reform to improve the learning efficiency of music teaching.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics Statement

The studies involving human participants were reviewed and approved by the Ethics Committee of Gannan Normal University. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.

Author Contributions

Both authors listed have made a substantial, direct, and intellectual contribution to the work, and approved it for publication.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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