An Effective Way to Enhance Learning Interactivity and Immersion with Virtual Reality Technology in Dance Education

Virtual reality technology (VR) is one of the internationally recognized modern educational technology in recent years, its application in the school practice teaching, can give some traditional teaching concepts and modes of change, to carry out virtualized experimental training for school students to provide employment advance practice [1-4], to solve the long-standing and widespread problems of practice teaching in the school has an important technical guidance significance. Virtual reality technology creates a three-dimensional virtual scene, providing students with a sense of immersion and interactive learning space [5-8].

One of the most significant changes that virtual reality technology brings to dance education is the ability to create an immersive performance space. Traditional dance and performance are usually limited by the size of the actual stage and scene layout, but in the virtual reality environment, teachers and students can get rid of these limitations [9-12], to build a fantastic, unlimited possibilities of performance space. Teachers and students can move and perform freely in these virtual spaces, for and feel the very powerful visual impact [13-15].

In addition, students can practice repeatedly in the virtual environment and interact with virtual characters or scenes. By simulating real performance scenes and audience reactions, it helps them to better adapt to the stage pressure and improve their performance skills and self-confidence [16-19]. Moreover, virtual reality technology can also collect and analyze the data of students’ training process, provide them with accurate feedback, help them discover their shortcomings and make targeted improvements [20-22].

The application of virtual reality technology in dance teaching is a long-term process, we should pay attention to the role of the virtual reality of the actual interactions, to play the role of the virtual reality dance teaching system, so that the students learn without the limitations of time and place, to improve the level of students’ dance, and promote the modernization of China’s dance teaching [23-26].

Literature [27] examined the application of virtual reality technology in dance teaching, and by comparing it with traditional teaching methods, it was shown that putting virtual reality technology is beneficial to improve students’ participation and motivation. Literature [28] proposed the application of virtual reality technology in dance teaching based on the shortcomings of current dance teaching. It shows that the application of virtual reality technology provides students with an immersive and interactive experience, which deepens their understanding of dance content. Literature [29] compares virtual reality technology dance teaching in China and other countries. It provides elaboration on the design requirements of virtual reality technology in dance teaching and puts forward relevant suggestions, aiming to provide reference for peers. Literature [30] examined the VRT Acceptance Model (TAM) in dance education, revealing the potential, limitations and applications of motion capture technology in dance visualization. It was pointed out that motion capture as has great promise in dance education. Literature [31] constructed a teaching system for physical dance with VR technology and compared its effects with traditional teaching methods on students’ physical dance performances. It shows that the teaching mode of virtual reality teaching system has improved both students’ dance performance and teaching quality. Literature [32] describes the application of virtual reality technology in dance classroom teaching, aiming to provide some references for colleges and universities to use virtual reality technology teaching through analysis. Literature [33] explored the integration of virtual reality technology and dance. And based on case studies, it provides insights for understanding the intersection of technology and art, dance creation, performance and education in the digital era.

It is not difficult to see that virtual reality technology is not only widely used in the field of education, but also has a positive impact on dance education. However, according to the results of the current research, academics have only skimmed over the content of “interactivity” and “immersion” brought by virtual reality technology, which is the biggest feature of virtual reality itself and should be the focus of the research. Interactivity and immersion are the most important features of virtual reality itself, so they should be the focus of research.

Virtual reality technology has been integrated into dance teaching practice and has achieved better results in application. For the problem of enhancing learning interactivity and immersion, this paper mainly proposes three effective ways to assess dance movements, interactive feedback, and virtual environments. And based on this, the virtual reality-based dance teaching mode is designed using motion capture technology, three-dimensional modeling, and other technologies. Mainly using the questionnaire survey method, multiple regression analysis, correlation score, experimental comparison method and other research methods, the virtual reality technology as an auxiliary means, applied to the process of dance teaching, according to the post-experimental statistical analysis of the data, to verify the effect and feasibility of the application of virtual reality technology in dance teaching.

2

Instructional design based on virtual reality technology

2.1

Virtual reality-based dance teaching pathway

This paper focuses on the use of virtual reality technology in dance teaching and in various dance forms. Virtual reality technology is characterized by immersion, interactivity, and imagination, which can improve the efficiency of movement. Dance is a very practical discipline, and its teaching method has professional characteristics such as flexibility and richness, which requires students to have better mental and physical abilities to understand, learn, imitate and perform movements in the teaching process. In this process, virtual reality plays an important role. Virtual reality technology has changed the way of face-to-face teaching in traditional classroom, and it can receive movement or other instruction information from users through input devices such as helmet and gloves, process them in the system and form influence on human’s visual, auditory, tactile and other senses through some output devices to make people feel as if they were performing a certain kind of behavior in real world, and immerse the students in it from multi-sensory. This paper analyzes the use of virtual reality in different dance forms, which creates new learning forms for dancers, allowing them to learn various movements and skills at their own convenience, without the constant presence of an instructor. In order to achieve this goal, virtual reality needs to have three basic technological elements as shown in Figure 1.

The ways in which virtual reality technology enhances learning interactivity and immersion are as follows: 1)

Virtual reality technology is applied to dance performances as a tool for the audience and performers to analyze dance performances and interact with the creative elements of the dance. By using virtual reality technology to analyze the dance movements and make records, virtual reality technology can extract more detailed and richer information about the new movements, match them with pre-inputted dance movements, and judge whether they are consistent with the previous movements.

2)

With the development of virtual reality technology, it can help people understand the various movements of various dances, allowing them to learn and appreciate the dance rhythm, interact and feedback through their own internal perspective, and learn and imitate the dance.

3)

Virtual reality technology provides students with a simulated practice environment as if they were on a real stage. Students can interact with other dancers in the virtual environment and better master the sense of space, fully immersed in the dance teaching.

In conclusion, the digital teaching of fitness dance greatly enhances the attractiveness and teaching effect of the course, deepens the teaching content and learning experience, meets the deeper needs of students, and is an effective way to enhance the interactivity and immersion of learning.

2.2

Design of Dance Teaching System Based on Virtual Reality Technology

1)

The construction of the dance action dataset firstly requires the use of optical motion capture technology to obtain the 3D coordinate points of the dance action, including the preparation of the data acquisition and the formal acquisition of the data work through the Vicon Shōgun, and then the acquired dataset will be pre-processed using the Vicon Shōgun. Finally, the high-precision 3D dance movement dataset has been obtained.

2)

Based on three-dimensional modeling software Maya, MD, 3Dmax and other tools to achieve this part of the character model, clothing, scene required object modeling and design. After the character modeling is completed, the dance action data collected in the previous step is associated and bound to the model produced, and a professional virtual dancer incarnation that can dance and has an excellent appearance is formally constructed.

3)

Develop on Unity, mainly to realize a scientific, reasonable and humanized scene for one-to-one teaching between the dancer avatar and the user avatar. According to functional requirements, some of the other scenes include a dance demonstration scene, a sub-movement display scene, and a detailed introduction scene, as well as initial interface design and other virtual environment construction work. After successful construction, the free linkage between buttons, UI interfaces, scenes and functions is realized through C# scripts.

3

Multiple linear regression models with multiple dependent variables

Statistical relationships are categorized into linear and non-linear correlations, and linear correlations are further categorized into positive and negative linear correlations. According to the number of independent variables can be divided into single independent variable and multiple independent variables, where single independent variable refers to only one independent variable, while multiple independent variables refers to two or more independent variables in terms of.

In this paper, the learner satisfaction scale is used as the dependent variable, and the four dimensions of virtual reality technology use, immersion, interaction, and learning effect are used as independent variables to test the regression model.

Linear regression is a mathematical category that examines the correlation between two or more variables. In the economic category of activities, there often exists a phenomenon caused by several variables that are jointly determined, and the link between the dependent variable of the explained phenomenon and the independent variable that explains the phenomenon is a dependency link. There are many independent variables used to explain a phenomenon, and the degree of their influence is differentiated between primary and secondary; sometimes it is difficult to distinguish between primary and secondary, and it cannot be said that secondary factors can be ignored.

There is m independent variable: x₁, x₂, ⋯, x_m, p dependent variables: Y₁, Y₂, …, Y_p. Assume that each dependent variable is linearly related to the independent variable. There are now n sets of measured data for the independent and dependent variables: (1) $(x_{t 1}, x_{t 2}, \dots, x_{t m}; y_{t 1}, y_{t 2}, \dots, y_{t p}) (t = 1, 2, \dots, n)$

The data matrices are denoted by X, Y, respectively: (2) $X = (\begin{matrix} x_{11} & x_{12} & \dots & x_{1 m} \\ x_{21}^{-} & x_{22} & \dots & x_{2 m} \\ ⋮ & ⋮ & ⋱ & ⋮ \\ x_{n 1} & x_{n 2} & \dots & x_{n m} \end{matrix}), Y = (\begin{matrix} y_{11} & y_{12} & \dots & y_{1 p} \\ y_{21} & y_{22} & \dots & y_{2 p} \\ ⋮ & ⋮ & ⋱ & ⋮ \\ y_{n 1} & y_{n 2} & \dots & y_{n p} \end{matrix})$

Let n set of data satisfy the following relation: (3) $\begin{array}{l} y_{t j} = β_{0 j} + β_{1 j} x_{t 1} + \dots + β_{m j} x_{t m} + ε_{t j} \\ (t =, 2, \dots, n; j = 1, 2, \dots, p) \end{array}$

Order: (4) $\begin{array}{l} β = (\begin{matrix} β_{01} & β_{02} & \dots & β_{0 p} \\ β_{11} & β_{12} & \dots & β_{1 p} \\ ⋮ & ⋮ & ⋱ & ⋮ \\ β_{m 1} & β_{m 2} & \dots & β_{m p} \end{matrix}) = (\begin{matrix} β_{(0)}^{'} \\ β_{(1)}^{'} \\ ⋮ \\ β_{(m)}^{'} \end{matrix}) = (β_{1}, β_{2}, \dots, β_{p}) \\ E = (\begin{matrix} ε_{11} & ε_{12} & \dots & ε_{1 p} \\ ε_{21} & ε_{22} & \dots & ε_{2 p} \\ ⋮ & ⋮ & ⋱ & ⋮ \\ ε_{n 1} & ε_{n 2} & \dots & ε_{n p} \end{matrix}) = (\begin{matrix} ε_{(1)}^{'} \\ ε_{(2)}^{'} \\ ⋮ \\ ε_{(n)}^{'} \end{matrix}) = (ε_{1}, ε_{2}, \dots, ε_{p}) \end{array}$

Then there is: (5) $Y = (1_{n} | X) β + E = C β + E$

where C is the n × (m + 1)-matrix; and it is assumed that $ε_{(i)} = {(ε_{i 1}, ε_{i 2}, \dots, ε_{i p})}^{'} (i = 1, 2, \dots, n)$ are independent of each other, that their mean vectors are 0, and that the covariance arrays are all Σ.

We define the model: (6) ${\begin{matrix} Y = (1_{n} | X) β + E = C β + ε \\ ε_{i} ~ N_{p} (0, Σ) \cdot (i = 1, 2, \dots, n) mutually independent \end{matrix}$

is a linear regression model for multiple dependent variables with multiple independent variables, where Y and E are random matrices, $β = (β_{i j})$ , $Σ = (σ_{i j})$ are unknown parameter matrices, X is a known matrix, $C = (1_{n} | X)$ , and rank(C) = m + 1.

Similar to the linear regression analysis with a single dependent variable, we can apply the least squares method to estimate the parameter β, and we begin by examining the residual sum of squares. It can be obtained from the definition above: (7) $E = Y - (1_{n} | X) β = {(ε_{i j})}_{n \times p}$

where $(i = 1, 2, \dots, n; j = 1, 2, \dots, p)$ , then the residual sum of squares $Q = \sum_{i = 1}^{n} \sum_{j = 1}^{p} ε_{i j}^{2}$ .

Straightening the model becomes: (8) $(\begin{matrix} Y_{1} \\ Y_{2} \\ ⋮ \\ Y_{p} \end{matrix}) = (\begin{matrix} C & O & \dots & O \\ O & C & \dots & O \\ ⋮ & ⋮ & ⋱ & ⋮ \\ O & O & \dots & C \end{matrix}) (\begin{matrix} β_{1} \\ β_{2} \\ ⋮ \\ β_{p} \end{matrix}) + (\begin{matrix} ε_{1} \\ ε_{2} \\ ⋮ \\ ε_{p} \end{matrix})$

Among them: (9) $Y_{j} = (\begin{matrix} Y_{1 j} \\ Y_{2 j} \\ ⋮ \\ Y_{n j} \end{matrix}), β_{j} = (\begin{matrix} β_{0 j} \\ β_{1 j} \\ ⋮ \\ β_{m j} \end{matrix}), ε_{j} = (\begin{matrix} ε_{1 j} \\ ε_{2 j} \\ ⋮ \\ ε_{n j} \end{matrix}), (j = 1, 2, \dots, p)$

Order: (10) $D = (\begin{matrix} C & \dots & O \\ ⋮ & ⋱ & ⋮ \\ O & \dots & C \end{matrix}) = (\begin{matrix} 1_{n} X & \dots & O \\ ⋮ & ⋱ & ⋮ \\ O & \dots & 1_{n} X \end{matrix})$

Then the straightened model can be written: (11) $V e c (Y) = D V e c (β) + V e c (E)$

where D is the np × (m + 1)p-matrix, β is the (m + 1) × p-matrix and E is the n × p-matrix.

Then: (12) $Q [V e c (β)] = \sum_{i = 1}^{n} \sum_{j = 1}^{p} ε_{i j}^{2}$

It is calculated that $V e c (\hat{β}) = (\begin{matrix} {(C' C)}^{- 1} C' Y_{1} \\ {(C' C)}^{- 1} C' Y_{2} \\ ⋮ \\ {(C' C)}^{- 1} C' Y_{p} \end{matrix})$ is the least squares estimate of the parameter vector $V e c (β)$ .

4

Practical application and effect evaluation of virtual reality technology

4.1

Descriptive analysis of the questionnaire

The subjects of this dissertation study were selected as 50 students from each of the two classes (parallel classes) of a dance academy in X city. This study intends to combine quantitative statistics and qualitative analysis in a 16-week empirical study, which was analyzed by questionnaires to the students. Where 1 to 5: strongly disagree, disagree, average, agree, strongly agree.

In this part, the data recovered from the questionnaires of the experimental class were descriptively counted and analyzed in the following five dimensions: 1)

Descriptive statistical analysis of the use of virtual reality technology

The descriptive statistical analysis of the use of virtual reality technology is shown in Table 1, where questions 1-5 in the questionnaire are aimed at understanding the students’ use of virtual reality technology. Overall, the mean values of all five questions were greater than 3, indicating that students perceived a high level of their virtual reality technology usage. The mean values for questions 1 and 2 were 3.4769 and 3.5579, indicating that the virtual reality technology was highly operational and that the majority of the students in the experimental class were able to log on and learn with ease.

2)

Descriptive statistical analysis of immersion based on virtual reality technology

The descriptive statistical analysis of immersion is shown in Table 2, where questions 6-10 of the questionnaire were aimed at understanding the immersion of teaching based on virtual reality technology. As a whole, the mean value of all five questions in this dimension was greater than 3, indicating that the students had a better sense of immersion in this mode.

3)

Descriptive statistical analysis of the interaction situation based on virtual reality technology

The descriptive analysis of the interaction situation is shown in Table 3, where questions 11-15 of the questionnaire lie in understanding the interaction situation of the VRT-based teaching model. In general, the mean value of all five questions was more than 3, indicating that the students considered their interaction situation to be better. The mean value of question 11 is 3.6328, which indicates that students are capable of actively participating in cooperative group learning activities and achieving good learning outcomes.

4)

Descriptive statistical analysis of learning effects based on virtual reality technology

The descriptive statistical analysis of the learning effect is shown in Table 4, where questions 16-20 of the questionnaire aim to understand the learning effect based on virtual reality technology. Overall, the mean value of the learning effect is 3.4536 points, which indicates that the students are effective in after-school learning.

5)

Descriptive statistical analysis of the satisfaction situation of learners based on virtual reality technology

The descriptive statistical analysis of the satisfaction situation is shown in Table 5, in terms of recognition, the mean values of question 24 and question 25 are 3.7014 and 3.6399 respectively, which indicates that the dance students think that the teaching mode based on virtual reality technology is more suitable for teaching dance.

Table 1.

Virtual reality technology usage

	N	Minimum value	Maximum value	Mean	Standard deviation
Virtual reality technology usage	50	1.4	4.7	3.5278	0.8412
Q 1Login learning	50	1	5	3.4769	0.9124
Q2 initiative	50	1	5	3.5579	1.1212
Q3 Seek help	50	1	5	3.4826	1.0214
Q4 Participate in groups, message boards	50	1	5	3.5748	1.0322
Q5 Screen interaction	50	1	5	3.5237	0.9976

Table 2.

Descriptive statistical analysis of immersion

	N	Minimum value	Maximum value	Mean	Standard deviation
Immersion	50	1	5	3.6025	0.8836
Q6 Be able to study	50	1	5	3.5526	1.1254
Q7 Better experience	50	1	5	3.6458	1.0742
Q8 Improve learning efficiency	50	1	5	3.5684	0.9785
Q9 dependence	50	1	5	3.6469	0.9828
Q10 focus	50	1	5	3.5894	1.1573

Table 3.

Descriptive statistical analysis of interactions

	N	Minimum value	Maximum value	Mean	Standard deviation
Interaction situation	50	1	5	3.6017	0.8359
Q11 participation	50	1	5	3.6328	1.0354
Q12 Answer questions	50	1	5	3.5526	1.0829
Q13 Express oneself	50	1	5	3.6539	0.9846
Q14 Help the teacher	50	1	5	3.6539	1.0475
Q15 Learn more	50	1	5	3.5157	1.0624

Table 4.

Descriptive statistical analysis of learning effects

	N	Minimum value	Maximum value	Mean	Standard deviation
Interaction situation	50	1.3	4.82	3.4536	0.9114
Q16 Integrated learning resources	50	1	5	3.3826	1.2033
Q17 Timely completion of task	50	1	5	3.4438	1.0320
Q18 Upload learning results and show them	50	1	5	3.5787	1.0221
Q19 Get certain experience	50	1	5	3.3899	1.1125
Q20 Corrective action	50	1	5	3.4982	1.0724

Table 5.

Descriptive statistical analysis of satisfaction

	N	Minimum value	Maximum value	Mean	Standard deviation
Interaction situation	50	1.6	5	3.6284	0.7452
Q21 Improve the performance of the dance	50	1	5	3.7421	0.8552
Q22 Like virtual reality teaching mode	50	1	5	3.6528	0.9885
Q23 More applicable to dance teaching	50	1	5	3.7483	1.0235
Q24 It will continue to use	50	1	5	3.7014	0.8896
Q25 Recommend to others	50	1	5	3.6399	0.9528

4.2

Correlation analysis

Correlation analysis is a statistical method to investigate whether there is a correlation between variables and the strength of the relationship. When the correlation coefficient is between 0 and 1, it is recognized as a positive correlation. When there is ** after the value, the correlation is considered significant. This section statistically analyzes the correlation between the dimensions in the Questionnaire on the Effectiveness of Classroom Teaching and Learner Satisfaction in Dance Based on Virtual Reality Technology, and the correlation analysis is shown in Figure 2:

The correlation coefficient between the use of virtual reality technology and classroom learning satisfaction is 0.741, the correlation coefficient between immersion and classroom learning satisfaction is 0.782, the correlation coefficient between interaction and classroom learning satisfaction is 0.745, and the correlation coefficient between learning effect and classroom learning satisfaction is 0.642, and all of them are shown to be significant at the 0.00 level, which indicates that there is a significant correlation between virtual reality technology usage, immersion, interaction situation, learning effects and learner satisfaction.

In this part, the learner satisfaction scale in the questionnaire was used as the dependent variable, and the four dimensions in the teaching effectiveness scale were used as the independent variables for the regression model test, in which Model 1 represents the effect of the use of virtual reality technology on classroom learning satisfaction. Model 2 represents the effect of immersion on classroom learning satisfaction. Model 3 represents the effect of interaction with virtual reality technology on classroom learning satisfaction. Model 4 represents the effect of learning effect on classroom learning satisfaction, aiming to explore the feedback of learner satisfaction on learning effect in the questionnaire, and whether students are satisfied with the learning effect produced by the dance teaching mode based on virtual reality technology.

The results of the multiple regression analysis are shown in Table 6, with *** indicating that a significance level of 0.001 was reached.

Table 6.

Regression test of learner satisfaction

Variable	Learner satisfaction
Variable	Model 1	Model 2	Model 3	Model 4
Interaction situation	0.653***
immersion		0.647***
Virtual technology			0.664***
Learning effect				0.520***
R²	0.547	0.618	0.559	0.412
Adjust R²	0.546	0.597	0.541	0.402
F	54.428***	67.459***	55.428***	32.745***

The regression coefficients for each model were as follows: model 1 the coefficient of influence of virtual reality technology on classroom learning satisfaction was 0.653. Model 2 the coefficient of influence of immersion on classroom learning satisfaction is 0.647. Model 3 the coefficient of influence of the interaction situation of virtual reality technology on the satisfaction of classroom learning is 0.664. Model 4 the coefficient of influence of learning outcomes on classroom learning satisfaction is 0.520. This shows that all the dimensions in the Teaching Effectiveness Scale have a significant positive correlation on the Learner Satisfaction Scale.

4.3

Comparative Analysis of Teaching Effectiveness

With the continuous development of information technology, virtual reality technology has become a widely used technology. In college dance teaching and training, virtual reality technology has also begun to be widely used. Virtual reality technology has been used in college dance teaching training to bring many benefits, such as improving the teaching effect, increasing student participation and enthusiasm. The following experiment is designed and implemented to specifically analyze the application effect of virtual reality technology in college dance teaching and training. 1)

Research purpose. The purpose of this experiment is to explore the application effect of virtual reality technology in college dance teaching and training, to compare the learning effectiveness of students under the use of virtual reality technology and traditional teaching methods, and to validate the advantages and shortcomings of virtual reality technology in college dance teaching by comparing the learning effect of traditional teaching with that of virtual reality technology teaching, so as to provide reference for college dance teaching.

2)

Research object. The research object of this experiment is the students majoring in dance at a college, totaling 100.

3)

Experimental design. This experiment will use the control group experimental design, 100 students will be randomly divided into the experimental group and the control group of 50 people each. The experimental group uses virtual reality technology for dance training, while the control group uses traditional teaching methods for dance training. In the experimental design, the virtual reality technology used in the experimental group mainly includes motion capture technology, 3D modeling technology, animation rendering technology, and so on.

During the experiment, all students will receive the same teaching curriculum, including basic dance movement practice, stage performance skills and so on. The traditional teaching group will be taught using traditional face-to-face methods, while the virtual reality technology group will be taught using virtual reality technology. Students will enter the virtual reality scene to simulate dance movements through visual and physical sensations, and will be corrected and coached by virtual reality technology. The experimental group will be trained through virtual simulation in the laboratory, while the control group will be trained in the traditional teaching venue. At the end of the experiment, the experimental data will be analyzed and compared to determine the effects of using virtual reality technology in college dance teaching and training.

At the end of the experiment, the learning speed of the two groups of students will be compared and analyzed through the record of learning time to assess the time cost and efficiency of virtual simulation technology in dance teaching. The experimental results were analyzed and compared with the data. Descriptive statistical analysis and T-test analysis were carried out, and the test results were shown in Figure 3, and the training effects of the experimental group and the control group were compared from the perspectives of training effects and student feedback.

In terms of the overall movement completion time, the average value of the score of the students in the posttest of the experimental group was 4.10s, which was better than the average value of 4.35 in the posttest of the control group.

The results of learning interest scoring and dance movement mastery and skill improvement test are shown in Table 7. 1)

In terms of learning interest, the difference between the experimental group and the control group is significant (T=4.77, P<0.01).

2)

In terms of dance movement mastery and skill improvement, the difference between the experimental group and the control group is significant (T=3.39, P<0.05).

Table 7.

Study interest rate and dance movement control and skill improvement

Group	Learning interest rating	Dance movement control and skill improvement
Experimental group	8.9	8.5
Control group	7.5	6.8
T	4.77	3.39
P	<0.01	<0.05

This experiment verifies the value and advantages of the application of virtual reality technology in college dance teaching by comparing traditional teaching and virtual reality technology teaching. The application of virtual reality technology was analyzed and summarized for the learning effect, learning speed, learning interest, mastery of dance movement, and skill improvement.

In summary, the training effect of the experimental group was significantly better than that of the control group. Students in the experimental group mastered dance movements to a higher degree, performed more smoothly and naturally, and were more capable of mastering dance techniques and movements within a limited time and applying them in actual performances. The control group students displayed more variability, with different students possessing varying levels of expertise in techniques and movements, leading to less effective performance than the experimental group.

The students in the experimental group had a high evaluation of the learning and training effect of virtual reality technology, believing that virtual reality technology could help them better understand and master dance skills, as well as a higher degree of enjoyment and a richer learning and training experience in the virtual environment. The students in the control group, on the other hand, had relatively consistent feedback that traditional teaching methods could not fully touch students’ needs and interests, and the learning effect was not as good as that of virtual reality technology.

5

Conclusion

This paper analyzes the application of virtual reality technology in dance teaching, and provides effective ways to enhance learning interactivity and immersion, such as evaluating dance movements, interactive feedback, and constructing virtual environments. Motion capture, three-dimensional modeling, and other technologies are used to design the virtual reality teaching system. After the research and analysis, it can be seen that the correlation coefficients between the use of virtual reality technology, immersion, interaction, learning effect and classroom learning satisfaction are 0.741, 0.782, 0.745, 0.642, respectively, all of which are significant at the level of 0.00, with a positive correlation. Through the comparative analysis of traditional teaching and virtual reality technology teaching, it can be found that the training effect of the experimental group is significantly better than that of the control group in terms of learning interest and dance movement mastery and skills. Future research should further explore the refinement of virtual reality technology in the detailed presentation of dance instruction, such as integrating motion-sensing devices to enhance movement feedback. This would enable students to more authentically perceive the nuances of dance movements, thereby enhancing embodied experiences and achieving a higher level of skill acquisition and cultural immersion.

Langue:: Anglais

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Sujets de la revue:: Sciences de la vie, Sciences de la vie, autres, Mathématiques, Mathématiques appliquées, Mathématiques générales, Physique, Physique, autres

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An Effective Way to Enhance Learning Interactivity and Immersion with Virtual Reality Technology in Dance Education

Ruiqi Peng

Publié en ligne: 24 mars 2025

Reçu: 09 nov. 2024

Accepté: 16 févr. 2025

DOI: https://doi.org/10.2478/amns-2025-0760

Mots clésCorrelation analysis, Regression analysis, Virtual reality technology, Learning interaction

© 2025 Ruiqi Peng, published by Sciendo

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

Mots clés
Correlation analysis, Regression analysis, Virtual reality technology, Learning interaction