Construction and Practice of Interactive Learning Platform in Vocal Music Teaching in College Music Courses in Network Era

With the development of social economy and the progress of culture and education, vocal music teaching in colleges and universities is also under constant reform and development. Vocal music teaching is a very important part of musicology majors, which plays a vital role in cultivating music literacy and artistic cultivation of musicology majors in colleges and universities [1–4]. The development status and effective strategies of vocal music teaching in colleges and universities have attracted much attention. In the past, vocal music teaching focused more on students’ skill training and voice shaping, while modern vocal music teaching focuses on students’ personality play and the cultivation of musical expression ability [5–8]. Under the background of network era, the construction of interactive learning platform for vocal music teaching can adopt personalized teaching methods according to students’ characteristics and potentials, focusing on the development of students’ self-expression and creativity [9–11].

As a new type of education mode, interactive teaching platform can effectively improve the teaching effect and meet students’ individualized needs. It utilizes computer and network technologies to provide rich learning resources, learning tools and learning support to help learners learn more effectively and improve their academic achievement [12–15]. Interactive learning platforms for vocal music teaching are powerful learning tools that can provide students and teachers with rich learning resources and learning tools, support teachers and students in independent learning and communication and interaction, and provide personalized vocal music learning support and feedback [16–18]. Through the use of interactive learning platforms, students can obtain more flexibility, autonomy and personalized learning experience in the learning process, and improve vocal music learning effect and achievement [19–20].

In this paper, we first constructed an interactive online teaching platform and selected 200 students and 20 teachers from the freshman year of three universities in China as the research subjects for practice. After that, using the questionnaire survey method combined with Flanders interaction analysis and social network analysis method, it investigates the interactive relationship between students and teachers and the interactive status of students in terms of verbal structure in the process of music classroom teaching after using the interactive platform. Finally, based on the results of the survey, ideas are provided for solving the effects of the application of the interactive learning platform in the teaching of vocal music in music courses as well as the problems that arise in the teaching process.

2

Study design

2.1

Design of an interactive teaching platform for vocal music

2.1.1

Functional description of the platform

In the teaching process the platform is divided into mobile and PC, and the main functions are course learning, discussion questions, homework practice, 3D cloud video viewing, coursework assignment submission, question bank use, message management, and course syllabus uploading and access. Figure 1 displays the functional modules of the interactive platform.

In order to achieve the above functions, the functional modules are designed as follows: 1)

Login statistics module: It is used to count the login status of users, mainly based on the concurrency, region, time period, and the current number of online users to count the current and historical login data.

2)

Database Storage Module: Used to store the account information data when registering on the teacher’s side or the student’s side, the user’s login statistics and other data uploaded to the platform, so as to facilitate the subsequent searching and checking of the data information.

3)

Interface management module: used to differentiate the rendering of different roles of the user interface, so as to facilitate the display of the corresponding role interface after the login of different roles of the user, the display interface of the student side is different from the display interface of the teacher side.

4)

Resource management module: used for managing the resources stored in the cloud server in the software on the teacher’s side and the student’s side, including uploading, downloading, deleting, updating, and searching for 3D video resources, 3D model package resources, and audio resources on the teacher’s side and the student’s side, so as to facilitate timely requesting of resources by the teacher’s side and the student’s side from this module.

5)

Information relay processing module: it is used to relay and parse the control information sent from the teacher side to the student side and the information returned from the student side to the teacher side, so as to enable the teacher side to interact with the student side in the data flow.

6)

Data push module: it is used to push relevant data information to the student side or the teacher side according to the request sent by the student side or the teacher side.

7)

Log management module: it is used to record some log information of the system itself, which is convenient for operation.

8)

Teacher side includes: account registration and login module, which is used to fill in the personal information and the subject to be taught, etc., to register and become a platform user; at the same time, log in according to the account number and password, and after logging in, the teacher side establishes an association with the cloud platform side, so that the teacher user can check the corresponding information stored on the cloud platform side through the teacher side.

9)

Receiving module of the student side: It is used to receive the student user information of the student side to be taught, and the student side finds the online teacher side and joins in the teaching of the teacher side, and the connection between the teacher side and the student side is realized through this module.

2.1.2

Platform structural framework

After analyzing the functional and technical basis of the interactive teaching platform [21–22], it is logically divided into a three-layer architecture: interface representation layer, logical interface layer, and data storage layer. Among them, the interface representation layer is a variety of web pages, client interface software with which the user interacts directly, and the logical interface layer establishes an abstraction for the functions provided by the system, so that the services and data are separated. The data storage layer is responsible for permanently storing data from the logical interface layer. The structure of an interactive VR teaching platform is shown in Figure 2.

2.2

Research Subjects and Methods

2.2.1

Objects of study

In this study, teachers who teach vocal music in college music programs were given questionnaires. In this paper, 200 students and 20 teachers from the freshman year of three universities in China were selected as the research subjects. The 200 students and 20 teachers were arbitrarily divided into two groups, the experimental group and the control group, with 100 students per group and 10 teachers per group. Students in the experimental group were asked to adopt a teaching mode that incorporates the interactive learning platform proposed in this paper during class; students in the control group still adopted the traditional teaching mode. A total of 200 valid questionnaires were collected in this student questionnaire survey, the effective recovery rate of the questionnaire was 100%, and the reliability of the questionnaire was 0.9758 and the validity was 0.9587. 20 teacher questionnaires were distributed, the effective recovery rate was 100%, and the reliability was 0.9875 and the validity was 0.9646. It is evident that the questionnaire survey results presented in this paper are very representative.

2.2.2

Research tools

Adding coding on the basis of FIAS, Improved Flanders Interaction Analysis System, or iFIAS for short [23]. It improves the traditional FIAS to make up for the shortcomings, and takes the teacher-student verbal interaction as the starting point to record the teacher-student verbal interaction behaviors in teaching and analyze the impact it has on the learners’ learning outcomes. This quantitative analysis tool consists of three parts: coding system, coding specification, and decoding analysis method. The coding system is subdivided into four major categories and 14 subcategories: teacher language, student language, silence and technology. The coding system is used to quantitatively analyze the results of observation records of authentic instructional videos by forming a visual matrix for observation. It is also capable of performing variable calculations to illustrate the situation using real and credible data. It is also capable of performing dynamic curve analysis. The teacher-student verbal interaction can be clearly presented through these intuitive forms of expression, which can provide feedback on the teaching effect and effective teaching suggestions for teaching.

2.3

Research methodology

This paper adopts the research method of combining quantitative analysis and qualitative analysis, the main purpose is to observe and record the verbal interaction behavior of teachers and students in a more natural state of the teacher, so as to promote the perfection of the teacher’s teaching, therefore, we choose to take the classroom observation as the research method, and with the help of the quantitative analysis software FIAS to carry out a certain amount of statistical analysis, to complete the research.

2.3.1

Questionnaire method

This method uses positivism as a methodology for quantitative research. The questionnaires are usually distributed to the investigators in the form of field distribution or mailed to them, and then the questionnaires are recovered, sorted out, and finally statistically analyzed through the research analysis system, from which the results of the study are drawn. In order to be more specific about teachers’ perceptions and understanding of their instructional verbal interactions, this study designs questionnaires for teachers and students. The questionnaire research method is characterized by a wide range of questionnaire collection and easy distribution of questionnaires, therefore, in order to have a more comprehensive understanding of the current situation of classroom teacher-student verbal interaction, this study will conduct a questionnaire survey of students. Through the questionnaire, we try to understand students’ views on classroom verbal behavior and sort out their needs, so as to provide a theoretical basis for the research of this paper.

2.3.2

Social network analysis

1)

Concept of social network

A social network is a collection of nodes and the connecting relationships between them. In layman’s terms, a social network is a network structure constructed from different nodes linked to each other by intermediaries. Nodes are social enablers, and they can be any social unit or social entity that is responsible for transmitting or receiving information. Social network analysis can be used to judge the subjective energizer nature of nodes in the social network structure, including socioeconomic status, social value destination, and network roles. Connectivity is the relationship between nodes, which can be either a real social relationship or a nominal relationship based on a specific principle. The direction of information transfer is indicated by the arrow on the connecting line, and the thickness or assignment of the line shows the frequency of information transfer or the strength of the connection.

2)

Steps of social network analysis

Social network analysis [24] is a tool used to analyze the structure of relationships and relational networks, its basic elements are “nodes” and “lines”, and its formal definition is based on the points and lines to express the network. The steps of the analysis are as follows:

Step 1:

Identification of the units of analysis, definition of nodes and connectivity;

Step 2:

Selection of the software tool for the study, such as Ucinet, R or Gephi;

Step 3:

Collecting and organizing the relationship data;

Step 4:

Establishment of relationship matrices, such as occurrence array or adjacency matrix;

Step 5:

Data processing and analysis, calculating network structure metrics such as centrality measures, structural hole analysis, factions and associations discovery;

Step 6:

Interpretation of the analysis results, comments and recommendations.

3)

Centrality measure of network nodes

Based on topology theory social network can be abstractly represented as G = (V, E), set V denotes all nodes in the social network, set E denotes the connectivity between nodes based on a particular relationship. g = |V| denotes the total number of all nodes.

(1)

Point Degree Centrality. Point centrality refers to the total number of connections of a node, i.e., the more direct connections it has, the higher its participation in that network. The specific concept can be interpreted in both absolute and relative terms:

In an undirected graph, $X_{i j} = X_{j i} = {\begin{array}{l} 1, N o d e i i s r e l a t e d t o n o d e j \\ 0, N o d e i i s n o t r e l a t e d t o n o d e j \end{array}$

The absolute centrality is calculated as: 1 $C_{D} (I) = d (i) = \sum_{j} X_{i j} = \sum_{j} X_{j i}$

The relative centrality is calculated as: 2 ${C^{'}}_{D} (i) = \frac{d (i)}{g - 1}$

Where, g – 1 is the maximum possible number of absolute centrality in that network and hence the range of relative centrality is [0,1]. 0 denotes the case of an isolated point, i.e., the point is not connected to any other node. 1 denotes that the point is directly connected to any other node in the network.

In directed graphs, the point degree center degree is the point out degree and point in degree, $X_{i /} = {\begin{array}{l} 1, N o d e i c h o o s e s n o d e j \\ 0, O t h e r \end{array}$ , similarly X_ji = 1 when denotes node j selects node i.

The point out degree is calculated as: 3 $C_{D o} (i) = d_{o} (i) = \sum_{j} X_{i j}$

The point out degree is relative to the center degree: 4 ${C^{'}}_{D O} (i) = \frac{d_{O} (i)}{g - 1}$

The point entry degree is calculated by the formula: 5 $C_{D I} (i) = d_{j} (i) = \sum_{j} X_{j i}$

The relative centrality of point incidence is: 6 ${C^{'}}_{D I} (i) = \frac{d_{I} (i)}{g - 1}$

(2)

Proximity centrality. The proximity centrality index describes the ability of a node to approach other nodes without being controlled by other nodes, if the sum of the distances between a node and other nodes in the network is smaller, the higher the proximity centrality degree of the node is, indicating that the node is more closely connected to other nodes, and that the node is able to make the information travel farther in the network, and the proximity centrality degree is calculated by the formula: 7 $C_{c} (i) = {[\sum_{j = 1}^{g} d (i, j)]}^{- 1}$

Where $\sum_{j = 1}^{g} d (i, j)$ denotes the sum of distances between node i and all other nodes except node i.

The standardized proximity centrality degree is: 8 ${C^{'}}_{C} (i) = \frac{g - 1}{\sum_{j = 1}^{g} d (i, j)}$

It can be regarded as the inverse of the average distance between node i and all other nodes, so ${C^{'}}_{C} (i) \in [0, 1]$ . When a node in the network is adjacent to all other nodes, it means that the node has the maximum proximity to any other node, and at this time, the standardized proximity centrality degree of the node is 1.

(3)

Mediator centroid degree. The intermediary center point is the point on the shortest path between two nodes, and its intermediary centrality describes the size of the intermediary role of the node to contact other nodes that do not have a direct line between them, if the number of shortest paths through the point is greater, it means that the intermediary role of the point is greater, and its information in the network can flow more quickly.

Its normalization formula in the undirected graph is: 9 $C_{B} (i) = \frac{2 \sum_{j < k} g_{j k} (i) / g_{j k}}{(g - 1) (g - 2)}$

The normalization formula in directed graphs is: 10 $C_{n} (i) = \frac{\sum_{j < k} g_{j k} (i) / g_{j k}}{(g - 1) (g - 2)}$

Where g_μ denotes the number of shortest paths through node j and node k, g_jk(i) denotes the number of shortest paths through node i in the shortest paths of node j and node k, and $\sum_{j < k} g_{j k} (i) / g_{j k}$ is the absolute mediator centrality of node i.

(4)

Eigenvector centrality, the basic idea is that the eigenvector centrality of the node depends on the centrality of this node and other nodes connected to it. In layman’s terms, the more important people you are connected to, the more important you are in the network structure. A node with a high degree of centrality indicates that it has a large number of other nodes directly connected to it, but its eigenvector centrality is not necessarily high. Even if a node is connected to a small number of other nodes, if all the other nodes connected to it have a great deal of influence, it is highly likely that the eigenvector centrality of this node exceeds that of nodes with a large number of mediocre connectivity relationships.

The formula for this is as follows: 11 $E C (i) = x_{i} = c \sum_{j = 1}^{r} a_{i} x_{j}$ where c is a constant of proportionality, denoted x = {x₁, x₂,…x_g}. When the steady state is reached after many iterations, the matrix form is obtained: x = cAx, x denotes the eigenvector corresponding to the eigenvalue c⁻¹ of matrix A, i.e., it can also be expressed as: Ax = λx, so λ = c⁻¹.

4)

Completeness of social networks

The completeness of a social network refers to the closeness of the associations between the nodes in the network, which is usually expressed in terms of network density. The density of a network is defined as the ratio of the number of relationships that actually exist in the network to the maximum number of relationships it is possible to have. The range of network density is [0,1], and the density of a complete network is 1.

3

Results and analysis

3.1

Social network analysis of classroom teacher-student interactions

The interaction relationship between teachers and students in the classroom is examined in terms of the overall characteristics of the network in this paper. The analysis of the overall attributes of the classroom teacher-student interaction network research can understand the overall structural attributes of the classroom interaction structure, and diagnostic analysis of the status and role of teachers and students in the interaction network, as well as the characteristics and shortcomings of the characteristics and deficiencies shown to provide optimization of decision-making. The overall attributes of the teacher-student interaction network include the overall structure of the network, network density, centrality, concentration of small groups, and the overall analysis of the degree of each actor.

3.1.1

Analysis of the overall structural model of the network

It was found that both in the experimental class and the control class, the teacher-student interaction network in the classroom showed a star structure, which can be divided into two categories: the first one is a complete star structure, where the teacher is at the center of the interaction network and the other student nodes surround the teacher nodes. The model of classroom interaction network is shown in Figure 3. In this type of network, there is little direct interaction between students, and students use the teacher to exchange and transfer information indirectly. This interaction network is a very fragile social network, and the teacher’s role directly determines the existence of the interaction network.

The second network model of teacher-student interactions is similar to the first one, with the difference that there are some direct interactions between students in the network. The second network model of classroom interactions is shown in Figure 4. These interactions between students are done under the guidance or encouragement of the teacher, but the percentage is very small. The interaction network remains a star-shaped structure with the teacher at its center.

These two models of teacher-student interaction networks were present in both experimental and control classes. As can be seen from the data in the figure, these two structural models appeared in the same proportion in the experimental and control classes. That is, the variable of vocal interactive platform did not cause any difference in the distribution of these two star structures in this empirical study.

3.1.2

Analysis of the completeness of the network of interactions

For the network of teacher-student interaction in the classroom, the network density indicates the degree of closeness of teacher-student interaction during the same lesson. Generally speaking, the higher the network density is, the more favorable it is for the transmission and diffusion of interactive information between teachers and students, while too high or too low a density is not conducive to knowledge sharing and diffusion.

Since the network of teacher-student interaction relationship in this study belongs to a local network in the overall network of classroom teacher-student interaction relationship, which is manifested in the fact that the teacher is the center of the interaction, and the exchange and transmission of the contents of knowledge, cognition, and experience is the goal. Therefore, only direct interactions between the teacher and students and interactions between students under the guidance of the teacher are considered. The direct interaction between students in private is not concerned. Therefore, the density of the classroom teacher-student interaction network under the scope of this study is generally low, ranging from 0.2 to 0.6, which means that the actual occurrence of teacher-student interaction only accounts for about all possible teacher-student interactions.

The findings on the density of teacher-student interaction networks are shown in Figure 5. Comparing the interaction network of the experimental class and the control class can be found that there is a significant difference between them. The average density of teacher-student interaction network of the experimental class and the control class is 0.5007 and 0.2985 respectively, the experimental class is 20.22 percentage points higher than the control class, and the limit value of the density of teacher-student interaction network is 55.93%. Comprehensive analysis found that 20.22% is precisely the essence of the role of the vocal interactive platform. From this, we can preliminarily conclude that in face-to-face classroom teaching, the interactive relationship between students is relatively limited.

3.1.3

Group centrality analysis of interaction networks

The description of centrality is divided into three forms: degree centrality, proximity centrality and mediation. Of these, degree centrality and mediation are the main indicators of an actor’s position and structure in the team. The proximity centrality metric uses the concept of distance to calculate the degree of centrality of a node. Since proximity centrality requires that the network must be fully connected, and a network with direction requires that all nodes are strongly connected two by two, this study pays no attention to proximity centrality and focuses only on degree centrality and mediation metrics. Next, this paper investigates the in-degree group degree centrality (CEDG), out-degree group degree centrality (DP), and group intermediary centrality (GIC) of students in the experimental and control classes.

The results of the survey on the centrality of the interaction network of the experimental class are shown in Figure 6. The average values of the density of the interaction network of the experimental class that introduced the interactive platform of vocal music are 0.9495, 0.9525 and 0.9673 for the “centrality of the in-group degree, the centrality of the out-group degree and the centrality of the up-group mediator” respectively.

The results of the survey on the centrality of the interaction network of the control class are shown in Figure 7. The density mean values of “in-group degree centrality, out-group degree centrality and up-group mediated centrality” of the interaction network of the control class are 0.4519, 0.9506 and 0.4595 respectively.

By observing the network model of interaction in the vocal classroom of the music course, it can be found that this interactive information reception is more centralized on the teacher, which is manifested in the teacher’s ability to receive interactive information from all the students in the class, and his role in the classroom teacher-student interaction activities is greatly strengthened. It can be seen that the introduction of the vocal interactive platform into the classroom can play a role in improving the overall process of teacher-student interaction, increasing students’ participation initiative and creativity, making students play a greater role in interactive activities. The overall process of teacher-student interaction in the classroom found that the vocal interactive platform allowed the teacher to have the time and energy to simultaneously obtain and process the opinions and responses of the entire class, which gave the teacher more access to the correct opinions and answers of the students, which allowed the teacher to build on the students’ opinions and conduct further interactive teaching. Students are more active in the classroom, realizing the change from “the teacher wants me to answer” to “I want to answer”. An analysis of the language distribution of students and teachers in the classroom recordings of the inquiry and creativity model reveals that the ratio of students’ language exceeds that of the teacher’s, and that the students’ subjectivity has been fully developed.

3.2

Analysis of the Language Structure of Teachers and Students in Interactive Teaching in Music Classroom

3.2.1

Analysis of speech structure in music classrooms of experimental and control classes

In order to more intuitively reflect the contrast of classroom speech structure ratios in the videos of the control group and the experimental group, this paper integrates the data of the experimental group (E) and the control group (C) in the Teacher’s Verbal Behavior (TVB), Students’ Verbal Behavior (SVB), Sedentary or Confused Behavior (SCLB), and Teacher’s Verbal Behavior and Students’ Verbal Behavior (TVB-SVB). The results of the comparison of the ratio of speech structure in the music classroom between the students in the control group and the experimental group are shown in Figure 8. The horizontal coordinates in the figure represent the ratios of the experimental group and the control group under different four languages. For example, E-TVB represents teachers’ speech behavior in the experimental group; C-TVB represents teachers’ speech behavior in the control group. As can be seen from the figure, the mean value of teacher verbal behavior (TVB) ratio is higher in the control group (88.93%) than in the experimental group (61.66%). Student Verbal Behavior (SVB) and Sedentary or Confused Behavior (SCLB) rates were higher in the experimental group (60.73% and 17.17%) than in the control group (41.99% and 10.50%). The greater difference between the control and experimental groups is the Teacher Verbal Behavior and Student Verbal Behavior (TVB-SVB) ratio, in terms of the mean value of the Teacher Verbal Behavior and Student Verbal Behavior ratios, the control and experimental groups were 3.83% and 1.51%, respectively, and there was an increase of 2.32% in the control group compared to the experimental group. This comparison shows that the experimental group had more interaction between teachers and students, and that the students were more dominant than the control group, while the teachers played a better guiding role.

3.2.2

Analysis of Speech Behavior of the Two Groups of Students in the Music Classroom

By integrating the data of the five codes of students’ speech behaviors and performing the ratio operation, the control group and the experimental group were compared one by one in the five students’ speech behaviors of “answering (A), questioning (Q), discussing with peers (DP), students’ manipulation of technology (SOT), and students’ observation (SO)”, so as to analyze the students’ speech behaviors of the two groups in the music classroom in terms of language. The results of the speech ratio of the control group and the experimental group of students in the music classroom are shown in Figure 9. As can be seen from the figure, the experimental group’s proportion of the five students’ speech behaviors, in descending order, were responding, engaging in discussion with peers, student observation, student manipulation of technology, and asking questions, with the corresponding mean values of the ratio of the ratio of speech behaviors of 73.05%, 21.72%, 5.01%, 0.71%, and 0.50%, respectively. The ratio means of the experimental group in the five verbal behaviors, in descending order, were responding, engaging in discussion with peers, student manipulation of technology, student observation, and asking questions, which corresponded to 58.24%, 30.07%, 7.50%, 3.20%, and 1.21% of the mean values of the ratio of the verbal behaviors, respectively. In contrast, the rates of students in the experimental group decreased by 14.81% and 1.81% for passive responding and speaking after observation, respectively; and increased by 8.35%, 6.79%, and 0.71% for engaging in discussions with peers, student manipulation techniques, and asking questions, respectively. It can be seen that the experimental class using interactive teaching has a higher rate of students’ active behavior, with a higher rate of active response and questioning than the control group. This reflects that the students in the experimental music class have more initiative, while the students in the control group have relatively weak learning initiative, which requires teachers to provide reasonable guidance to help students actively participate in music classroom learning and mobilize students’ learning interest.

4

Conclusion

This paper applies the constructed interactive teaching platform to the process of vocal education in the music classroom, after using using adopting the Flemish interaction analysis method and network analysis method to investigate and analyze the teacher-student interaction relationship network in the social network structure and the students’ classroom speech structure. The main conclusions are as follows: 1)

The overall structure of the interactive classroom network used in this paper remains a star-shaped structure, with the teacher at the center of the interaction network. The results of the comparison of the interaction network between the experimental class and the control class show that there is a significant difference between the two, the average value of the interaction network density of the experimental class is 20.22% higher than that of the control class, and the limit value reaches 55.93%. After the introduction of the vocal interactive platform, the average density of the interactive relationship network in the experimental class was very high (0.9495, 0.9525 and 0.9673), while that in the control class was relatively low (0.4519, 0.9506 and 0.4595). It can be seen that the introduction of an interactive platform for vocal music in the classroom can improve the process of teacher-student interaction and increase the initiative and creativity of student participation.

2)

In terms of the mean value of the ratio of verbal behavior of teachers, the control group (88.93%) was higher than the experimental group (61.66%). The mean values of the ratio of verbal behavior and silence or disorganization behavior of students in the experimental group (60.73% and 17.17%) were increased by 18.74% and 6.67%, respectively, compared to the control group (41.99% and 10.50%). The greatest difference in the mean values of the rates of teacher verbal behavior and student verbal behavior was found in the control group, which increased by 2.32% compared to the experimental group. It can be seen that the application of interactive learning platforms in teaching can increase the interaction between teachers and students, making the teacher’s guiding role stronger. In the experimental class using interactive teaching, in terms of students’ active behavior, the rate of students in the experimental group decreased by 14.81% and 1.81% in passive responding and speaking after observation, respectively, compared with the control group; and the rate of students in discussion with peers, student manipulation techniques, and questioning increased by 8.35%, 6.79%, and 0.71%, respectively, compared with the control group, which indicates that students of the music class in the experimental group were more actively initiative is higher, while students in the control group have relatively weak initiative in learning. In summary, the interactive platform for vocal music proposed in this paper can significantly improve the enthusiasm and initiative of college students in vocal music learning.

Language:: English

Publication timeframe:: 1 times per year
Journal Subjects:: Life Sciences, Life Sciences, other, Mathematics, Applied Mathematics, General Mathematics, Physics, Physics, other

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Construction and Practice of Interactive Learning Platform in Vocal Music Teaching in College Music Courses in Network Era

Lizhang Luo

Published Online: Mar 19, 2025

Received: Nov 02, 2024

Accepted: Feb 08, 2025

DOI: https://doi.org/10.2478/amns-2025-0434

KeywordsVocal interactive teaching platform, iFIAS, Social Network Analysis, Teacher-Student Interaction, Vocal music

© 2025 Lizhang Luo, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Keywords
Vocal interactive teaching platform, iFIAS, Social Network Analysis, Teacher-Student Interaction, Vocal music