Research on Interactive Content Development of Vocal Music Teaching in College Music Courses in the New Media Era

The core literacy of college students’ development is divided into three major areas, such as cultural foundation, independent development, and social participation, in addition to corresponding to six dimensions, namely, humanistic heritage, scientific spirit, learning to learn, healthy life, responsibility, and practice and innovation. The structural system of teaching core literacy ranges from the macroscopic summary of the three domains to the key competencies and necessary character of each branch point leading to the self-development of the individual and the healthy development of the society, presenting the genes and seeds needed for lifelong development and sustainable development of human beings, which is precisely the foundation and possibility for their further growth.

With the guidance of the concept of core literacy parenting in the new media era, the concept of scientific core literacy in the music curriculum has gradually been put forward. This concept, which elaborates on the nurturing value of the music discipline from three aspects: aesthetics and perception, artistic expression, and cultural understanding, has aroused multidimensional thinking in the academic community about the discipline’s nurturing. Thinking about the unique nurturing characteristics and strengths of the music discipline, the core literacy of the music discipline can be regarded as an intermediary for reaching the core literacy of student development, which serves to realise the general core literacy for the comprehensive development of human beings, and which explains that the music discipline can not be detached from its educational significance, social significance and instrumental attributes.

The connection between core literacy and students’ educational development in the music discipline is shown in Figure 2. Under the core literacy system, different disciplines bear the important responsibility of developing students’ core literacy, while at the same time reflecting their unique contribution to nurturing. The connection between core literacy and music core literacy can first be analysed by visualizing the three major areas, six dimensions and 18 basic points contained in core literacy and the three dimensions contained in the core literacy of the music discipline, from which the external relationship between the two can be basically presented, i.e., “aesthetic perception” and humanistic care, humanistic care, humanistic accumulation, and aesthetic interest are related in content. “Artistic Expression” can be related to Social Responsibility because the main content of the first stage is practical ability. “Cultural Understanding” is mainly related to national identity, social responsibility and international understanding. All in all, the basic points of the core literacy and the three aspects of the core literacy of the music discipline form an eight-point content correlation, and it can be seen from the correspondence between the core literacy expression that has already been subjectified in the discipline of music and the core literacy expression that has a macroscopic “transdisciplinarity” that the music curriculum bears the important task of cultivating the core literacy of the students.

Figure 2.

Music core literacy is associated with student education development

From the connotation and extension of core qualities, the cultivation of students’ “essential character” and “key abilities” should be carried out throughout the teaching of various disciplines, reflecting the contribution of the disciplines to the overall nurturing of human beings, i.e., the direction of nurturing people through disciplines under the leadership of the “people-centred” principle. This is the “people-centred” direction of subject education. The “interdisciplinarity” implied in the core qualities guides students to mould a person into a person with good “outward appearance” and “spiritual looks”, stable mental qualities, and a way of thinking that embodies perception and expression. The way of thinking reflects the shadow of perceiving, expressing and understanding music. Through the study of music, students will be able to create new wisdom in the practice of life and will be prepared for the development of their general competencies (interdisciplinary competencies) and their learning abilities. It is important and necessary to focus on the dimension of core literacy, to explain the significance and value of the core literacy of the music discipline on students’ development based on the nurturing value of the discipline, and to understand the unique nurturing value of the music discipline under the deep understanding of the meaning of the disciplinary view of education, which is also the foothold for the optimisation of the development of the interactive content of the vocal teaching in the music curriculum in this paper.

In vocal music teaching, the teacher-centred teaching philosophy severely binds students. The interaction mode is single, and students participate in the class passively. Although teachers put much effort before class, the actual effect is not satisfactory. Under the traditional interactive teaching mode of new media, teachers are involved in training related to new media technology and unilaterally indoctrinate students, which can only achieve little in the end.

Therefore, the optimisation of the new media interaction and the improvement of the teaching mode in the music discipline is especially crucial. The new media has the characteristics of intuitive image, timely feedback and immersion interaction, and the reasonable use of new media technology creates the conditions for the realization of interactive vocal music teaching. Teaching is a combination of “teaching” and “learning”, and we can’t ignore the teachers’ teaching work or the student’s learning situation. We should take students as the centre, understand their knowledge base, and collect and sort out their information. Teachers can flexibly design teaching links in the classroom and use new media technology to create interactive teaching situations so that the classroom can be revitalised and energised. Developing new pathways in students’ brains, cultivating students to think from different perspectives, learning a variety of artistic expressions, and improving IT skills and media literacy.

Based on the new media interactive content optimisation of the vocal music teaching mode shown in Figure 3, vocal music learning can be roughly divided into technical training, functional training, music aesthetic training, language learning, accompaniment courses and other courses. Therefore, the interactive content development and optimisation of this paper are diversified and targeted to the needs of vocal music teaching, which is mainly divided into the interactive optimisation of course content and the interactive optimisation of teacher-student communication. Among them, the interactive optimisation of course content is mainly reflected in the use of new media technology to create interactive teaching situations, so that the classroom is renewed with new vigour and vitality. The interactive optimisation of teacher-student communication is embodied in the cultivation of teachers’ ability and the mobilisation of students’ enthusiasm and interest.

Figure 3.

Interactive art teaching in the context of new media

Classroom teaching evaluation is to allow a specific evaluation subject, using scientific and reasonable evaluation methods, to make objective judgements on the whole process of classroom teaching, including teachers’ teaching and students’ learning, to reflect and diagnose the current situation of teaching, and to play an important role in supervising the process of teaching, assessing the effectiveness of teaching, guiding the implementation of teaching and promoting the reform of teaching. The most important of these is the assessment of students’ learning effects and teachers’ overall teaching behaviour, using the results of the assessment to reflect the quality of teaching and learning, and thus promoting the reform of teaching content, teaching methods and teaching tools.

In teaching evaluation, indicator weights are often assigned through hierarchical analysis (AHP), factor analysis and entropy value method.

Hierarchical analysis hair is a multi-level, multi-objective decision-making analysis method. Firstly, it establishes a multilevel structure, determines the relative importance of each element in the hierarchy by comparing two by two, and then gives the order of advantages and disadvantages of the alternatives at the end through people’s subjective judgement and scientific calculation.

In hierarchical analysis, when the matrix order is more than 3 orders (including 3 orders matrix), then need to carry out consistency test, consistency index is derived by calculating the random consistency index RI and consistency index CI.

The first and second order judgement matrices are regarded as having full consistency in the hierarchical analysis method, and only the division of importance between the indicators is required. When N>3, the judgement matrix consistency index CI is calculated as: 1CI=λmax−nn−1

Where n is the order of the matrix and λ_max is the largest eigenroot under the current matrix. Consistency ratio CR is a measure of matrix consistency and represents the ratio of matrix consistency index CI to random consistency index RI: 2CR=CIRI

It is generally accepted that a matrix is considered consistent if CR is less than 0.1. Otherwise, the weights in the matrix need to be readjusted or the matrix needs to be reconstructed until the consistency requirement is met. If the judgement matrix has full consistency. This means that the consistency index is equal to 0. So the following equation can be obtained: 3∑j=1naijωj=∑j=1n(ωi/ωj)ωj=nωi,(i=1,⋯,n) 4∑i=1n|∑j=1n(aijwj)−nwi|=0

The value on the left side of the equation reflects the consistency of the weight value ω_i with the judgement matrix A, the smaller the value is, the higher the consistency of the weight value with A. When it is satisfied, the matrix A has full consistency. So the consistency test problem can be converted into the following nonlinear programming problem: 5minCIF(n)=∑i=1n|∑j=1n(aijωj)−nωi|/n 6s.t.∑j=1nωj=1,ωj>0,j=1,2,⋯,n

Where minCIF(n) is the consistency index, n is the number of elements of the matrix, and α_ij is the element of the ith row and jth column of the matrix. ω_j and ω_i are the values of the jth and ith element weights, which can be used as optimisation variables in the subsequent work. The basic procedure of the operation is to solve for the eigenvalues and the weights of the indicators corresponding to the eigenvectors.

In hierarchical analysis, the one-time test problem is a nonlinear planning problem that is difficult to deal with by conventional methods. Although the judgement matrix for constructing the evaluation system has been determined, its corresponding weight values can be optimised thus satisfying the consistency test.

Factor analysis is the study of potential relationships between its attributes. The original data, i.e., the sample values, are provided with 2 random variables x and y, which represent two variables A and B. Their content values are measured for n specimens: 7x→=(x1,x2,⋯⋯,xn) 8y→=(y1,y2,⋯⋯,yn)

The samples were first standardised and the mean and variance were calculated according to the following formula: 9x¯=1n∑i=1nxi,y¯=1n∑i=1nyi 10σx2=1n∑i=1n(xi−x¯)2 11σy2=1n∑i=1n(yi−y¯)2

Re-order: 12xi′=xi−x¯σx,yi′=yi−y¯σy,i=1,2⋯⋯,n

The sample after standardisation meets the following conditions: 13x′¯=1n∑i=1nxi′=0,y′¯=1n∑i=1nyi′=0 14σx′2=1n∑i=1nxi′2=1,σy′2=1n∑i=1nyi′2=1

Here, x→ ,y→ is still used to represent the samples after standardisation, and their variances and correlation coefficients can be calculated according to the following formula: 15{ σx2=1n∑i=1nxi2=1nx′x→=1σy2=1n∑i=1nyi2=1ny′y→=1Yxy=1n∑i=1nxiyi=1nx′y→

It can be shown that the random variables x→ , y→ are uncorrelated and Y_xy = 0, algebraically equivalent to their inner product x→',y→=0 , is geometrically a two-vector direct intersection.

For n samples with m variables per sample, the original data matrix is as follows: 16X=[ x11x12⋯x1mx21x22⋯x2m⋯⋯⋯⋯xn1xn2⋯xnm ]=[ x→1,x→2,⋯,x→m ]

The column vector at the right end of the equation: 17x→j=(x1j,x2j,⋯,xnj)′,j=1,2,⋯,m

Represents the observed value of the jth variable on n samples, which can be viewed as a point or vector in an n-dimensional Euclidean space, denoted here by x→j . The relationship between the original variables is studied by examining the positional relationship of these m points or vectors.

If the sample data is normalised, i.e., X is a normalised matrix, i.e., there is: 18x→j=1n∑i=1nxij=0 19σj2=1n∑i=1nxij2=1nx→′jx→j=1,2,⋯,m

Then the correlation coefficient between x→i and x→k is: 20Yjk=1n∑i=1nxijxik=1nx→j′x→k,j,k=1,2,⋯,m

Correlation coefficient matrix R consists of correlation coefficients between two of the m variables: 21R=[ r11r12⋯r1mr21r22⋯r2m⋯⋯⋯⋯rm1rm2⋯rmm ]=1nx′x

The correlation coefficient matrix R is symmetric and at least semi-positive definite, which means that all of its eigenvalues are non-negative.

The correlation coefficient matrix is the starting point of the factor analysis method, and an important part of factor analysis is to study the structure of the correlation matrix. Also factor analysis often involves the matrix of correlation coefficients between two sets of variables, assuming that in addition to the previous m random variables, there are another p random variables. The matrix is as follows: 22y=[ y11y12⋯y1py21y22⋯y2p⋯⋯⋯⋯yn1yn2⋯ynp ]=[ y¯1,y¯2,⋯,y¯p ]

Assuming all standardised data, the correlation coefficient between y→k and x→j . is given by Eq: 23Skj=1ny→k′x→j,k=1,2,⋯,p;j=1,2,⋯,m

This section proposes to construct the evaluation index system for vocal teaching in college music courses. Firstly, the principles and basis of construction are determined, and the evaluation indicators are initially formulated. Then, the Delphi method is used to determine the index entries, and the weights of the indexes are calculated with the help of hierarchical analysis. The evaluation quantitative table is prepared based on the optimised evaluation index system of vocal music teaching, and its reliability and validity are tested. This study is constructed under the principles of scientificity, wholeness, development and operability by combing the existing relevant research results on teaching evaluation and vocal music teaching.

In order to safeguard the evaluation indexes of vocal music teaching formulated in this paper, the Delphi method was used to consult experts’ opinions, mainly including whether the indexes are set reasonably, whether the logic is clear and how important each index is. The total number of experts involved in this study was 50, and two rounds of expert enquiry were conducted. By collating and analysing the results of the correspondence, the evaluation indexes of vocal music teaching were finally determined, laying a solid foundation for the construction of the evaluation index system.

After the adjustment of the results of the first round of questioning, the second round of questioning collated and counted the results of the expert’s evaluation of the five first-level indicators, namely, teaching objectives, teaching content, teaching methods, teaching evaluation and teaching effects. The results of the experts’ enquiry on the first-level indicators are shown in Table 1. The average of the importance assignment is the lowest at 4.68 and the highest at 5.00, the standard deviation is between 0 and 0.468, and the coefficient of variation is in the range of 0 to 0.112. The five first-level indicators are in line with the screening criteria, and at the same time, none of the 50 experts put forward modification opinions on the first-level indicators. Therefore, the result of the correspondence in this round is that the first-level indicators are all included in the present study. Of the evaluation index system, no modification is made.

Collating and statistically evaluating the results of experts on the 18 secondary indicators, the secondary indicators of teaching objectives include clarity of objectives, plurality of objectives, and value of objectives. The secondary indicators of teaching content include completeness, value, richness and coverage of teaching content. The secondary indicators of teaching methods include teaching mode, teacher’s means, and teaching thinking. The secondary indicators of teaching evaluation are teacher evaluation, student evaluation, classroom evaluation, and education-related evaluation. The secondary indicators of teaching effectiveness are, in order, course effect, learning effect, performance improvement and problem improvement.

The results of the expert enquiry for the secondary indicators are shown in Table 2. The mean of the importance value is within the range of 4.06-4.95, the standard deviation is between 0.254 and 1.028, and the coefficient of variation is between 0.072 and 0.268. According to the above specified criteria for the inclusion of the indicators, the 18 level 2 indicators meet the screening criteria, and not only is the mean of the importance value greater than 4, but also the standard deviation and the coefficient of variation fall within the established range. At the same time, none of the 50 experts proposed any modifications to the secondary indicators, so the result of this round of correspondence was that all the secondary indicators were included in the evaluation indicator system of this study without any modifications.

Based on the evaluation results given by 50 experts in the 2nd round of correspondence, this study used hierarchical analysis and factor analysis to assign values to the indicators, and the evaluation index system and weights of vocal music teaching are shown in Table 3. The corresponding weights of the five first-level indicators, including teaching objectives, teaching content, teaching methods, teaching evaluation and teaching effect, are 0.315, 0.300, 0.198, 0.083 and 0.104, respectively, and at the same time, the scientificity and reasonableness of the constructed evaluation indicator system are further examined.