Abstract
In regular dance teaching, teachers find it difficult to pay attention to and meet the specific needs of each student. Extended-reality (XR) has the potential to help students get more information to dance learning. This study developed an XR interactive learning system to assist teachers in dance teaching, and explored the impact of XR on the process and results of dance skills. To assess the effectiveness of XR in dance education, a quasi-experiment was designed. Students majoring in preschool education in vocational high school in Hangzhou, China were recruited as research participants. 54 female students were divided into two groups with the same teaching pace, each with 27 students. The experimental group was taught dance lessons using a developed XR interactive learning system, while the control group was taught dance lessons using traditional methods. The experiment lasted for eight weeks and consisted of 16 lessons. Finally, the results indicate that XR has the potential to improve students’ learning performance and facilitate learning interactions in dance learning; to provide real-time personalized feedback to students and improve autonomous learning. Moreover, XR has the potential to enhance learning experience by reducing the cognitive load of students in dance learning. The results of this study is expected to help those considering the use of XR to supplement dance instruction.
Similar content being viewed by others
Data availability
The database generated for this study is available upon request to the corresponding author.
References
Abraham, S., & Connor, K. A. (2020). Work in progress: Leveraging technology trends to develop a skills-based approach to engineering design. Paper presented at 2020 ASEE Virtual Annual Conference. https://peer.asee.org/35669. Accessed 29 Jan 2023
Akcayir, M., & Akcayir, G. (2017). Advantages and challenges associated with augmented reality for education: A systematic review of the literature. Educational Research Review, 20, 1–11. https://doi.org/10.1016/j.edurev.2016.11.002
An, X., Li, G., Xu, L., & Shi, Y. (2011). A survey on application of virtual reality technology in U.S. military simulation training. Electronics Optics & Control, 18(10), 42–46. https://doi.org/10.3969/j.issn.1671-637X.2011.10.011
Baumeister, J., Ssin, S. Y., Dorrian, E. J., Neven, A. M., Webb, D. P., Walsh, J. A., Simon, T. M., Irlitti, A., Smith, R. T., Kohler, M., & Thomas, B. H. (2017). Cognitive cost of using augmented reality displays. IEEE Transactions on Visualization and Computer Graphics, 23(11), 2378–2388. https://doi.org/10.1109/TVCG.2017.2735098
Billinghurst, M., Poupyrev, I., Kato, H., & May, R. (2000). Mixing realities in shared space: An augmented reality interface for collaborative computing. Multimedia and Expo, 2000. ICME 2000. 2000 IEEE International Conference on. IEEE. https://doi.org/10.1109/ICME.2000.871085
Billinghurst, M., & Kato, H. (2002). Collaborative augmented reality. Communications of the ACM, 45(7), 64–70.
Blackstock, F., & Pritchard, S. (2020). Psychomotor skill development: Learning what and how to do. In M. Moy, F. Blackstock, & L. Nici (Eds.), Enhancing patient engagement in pulmonary healthcare (pp. 27–40). Respiratory Medicine. Humana. https://doi.org/10.1007/978-3-030-44889-9_3
Bogicevic, V., Seo, S., Kandampully, J. A., Liu, S. Q., & Rudd, N. A. (2019). Virtual reality presence as a preamble of tourism experience: The role of mental imagery. Tourism Management, 74, 55–64. https://doi.org/10.1016/j.tourman.2019.02.009
Buchner, J., Buntins, K., & Kerres, M. (2022). The impact of augmented reality on cognitive load and performance: A systematic review. Journal of Computer Assisted Learning, 38(1), 285–303. https://doi.org/10.1111/jcal.12617
Case Westem Reserve University. (2015). CWRU, Cleveland Clinic collaborate with Microsoft On mixed-reality technology[EB/OL]. https://thedaily.case.edu/cwru-cleveland-clinic-collaborate-with-microsoft-on-mixed-reality-technology-for-education/. Accessed 29 Jan 2023
Chan, J. C. P., Leung, H., Tang, J. K. T., & Komura, T. (2011). A virtual reality dance training system using motion capture technology. IEEE Transactions on Learning Technologies, 4(2), 187–195.
Chang, Y. (2021). Effects of virtual reality application on skill learning for optical-fibre fusion splicing. British Journal of Educational Technology, 52(6), 2209–2226. https://doi.org/10.1111/bjet.13118
Chang, S. C., & Hwang, G. J. (2018). Impacts of an augmented reality-based flipped learning guiding approach on students’ scientific project performance and perceptions. Computers & Education, 125, 226–239. https://doi.org/10.1016/j.compedu.2018.06.007
Chen, X. J., Xue, C. Q., Chen, M., Tian, J., Shao, J. & Zhang, J.(2017). Quality assessment model of digital interface based on eye-tracking experiments. Journal of Southeast University (Natural Science Edition), (01), 38–42. https://doi.org/10.3969/j.issn.1001-0505.2017.01.008
Chu, L. Y., Chen, W. D., Tan, Y., & Zheng, S. S. (2019). Rebuliding the experience: Extended Reality (XR) technology and its education application outlook:also discuss the trend of “education and new technology integration.” Journal of Distance Education, 37(1), 17–31. https://doi.org/10.15881/j.cnki.cn33-1304/g4.2019.01.002
Coleman, B. (2009). Using sensor inputs to affect virtual and real environments. IEEE Pervasive Computing, 8(3), 16–23. https://doi.org/10.1109/MPRV.2009.60
Crosier, J. K., Cobb, S., & Wilson, J. R. (2002). Key lessons for the design and integration of virtual environments in secondary science. Computers and Education, 38(1), 77–94. https://doi.org/10.1016/S0360-1315(01)00075-6
Davcev, D., Trajkovic, V., Kalajdziski, S., & Celakoski, S. (2003). Augmented reality environment for dance learning. In International Conference on Information Technology: Research and Education, 2003. Proceedings. ITRE2003. (pp. 189–193). IEEE.
Dehn, L. B., Kater, L., Piefke, M., Botsch, M., Driessen, M., & Beblo, T. (2018). Training in a comprehensive everyday-like virtual reality environment compared to computerized cognitive training for patients with depression. Computers in Human Behavior, 79, 40–52. https://doi.org/10.1016/j.chb.2017.10.019
Denisova, A., & Cairns, P. (2015). Adaptation in digital games: The effect of challenge adjustment on player performance and experience. In Paper presented at the proceedings of the 2015 annual symposium on computer-human interaction in play, London, United Kingdom.
Dubovi, I., Levy, S. T., & Dagan, E. (2017). Now I know how! the learning process of medication administration among nursing students with non-immersive desktop virtual reality simulation. Computers & Education, 113. https://doi.org/10.1016/j.compedu.2017.05.009
Dumont, A., Kadel, N., Brunet, N., Colombie, J. B., Brain, P. L., & Couillandre, A. (2016). Dance and health. Science & Sports, 31(4), 236–244. https://doi.org/10.1016/j.scispo.2016.06.002
Fan, L. Y., Hou, S. M., Zhang, K. F., & Liao, X. L. (2021). Review on the hot spots of extended reality(XR) in 2020. Science & Technology Review, 01, 220–232.
Fitts, P. M., & Posner, M. I. (1967). Human performance. Brooks/Cole Publishing.
Frederiksen, J. G., Srensen, S. M. D., Konge, L., Svendsen, M. B. S., Nobel-Jrgensen, M., & Bjerrum, F., et al. (2020). Cognitive load and performance in immersive virtual reality versus conventional virtual reality simulation training of laparoscopic surgery: a randomized trial. Surgical Endoscopy (3), 34. https://doi.org/10.1007/s00464-019-06887-8
Gao, Y., & Xu, D. (2021). Application of posture recognition service system based on information fusion smart sensor in dance training. Journal of Sensors, (4), 1–7. https://doi.org/10.1155/2021/4284249
Guo, Y., & Tian, Y. Q. (2020). A review of research on infant dance teaching in China from 2009 to 2018. Survey of Education, 9(8), P.14-16,21.
Harrow, A. J., & Simpson, E. J. (1989). Taxonomy of educational objectives. Shang Hai: East China Normal University Press
Hart, S. G., & Staveland, L. E. (1988). Development of NASA - TLE( Task Load Index): results of empirical and theoretical research. In P. A. Hancock & N. Meshkati (Eds.), Human Mental Workload North Holland (pp. 139–183). Elsevier Science Publishers.
Hopwood, M., Mann, D., Farrow, D., & Nielsen, T. (2011). Does visual-perceptual training augment the fielding performance of skilled cricketers? International Journal of Sports Science & Coaching, 6(4), 523–535. https://doi.org/10.1260/1747-9541.6.4.523
Hu, Y. B., & Huang, R. H. (2016). Learning experience in smart learning environment: definition, elements and scale. e-Education Research, (12), 67–73. https://doi.org/10.13811/j.cnki.eer.2016.12.009
Huang, R. H., Yang, J. F., & Hu, Y. B. (2012a). From digital learning environments to smart learning environments. Open Education Research, 18(1), 75–84.
Huang, S. Y., Hogg, J., Zandieh, S., & Bostwick, S. B. (2012b). A ballroom dance classroom program promotes moderate to vigorous physical activity in elementary school children. American Journal of Health Promotion, 26(3), 160–165. https://doi.org/10.4278/ajhp.090625-QUAN-203
Huang, C. L., Luo, Y. F., Yang, S. C., Lu, C. M., & Chen, A. (2020). Influence of students’ learning style, sense of presence, and cognitive load on learning outcomes in an immersive virtual reality learning environment. Journal of Educational Computing Research, 58(3), 596–615. https://doi.org/10.1177/0735633119867422
Hwang, G., Yang, L., & Wang, S. (2013). A concept map-embedded educational computer game for improving students’ learning performance in natural science courses. Computers and Education, 69, 121–130. https://doi.org/10.1016/j.compedu.2013.07.008
Jang, S., Vitale, J. M., Jyung, R. W., & Black, J. B. (2017). Direct manipulation is better than passive viewing for learning anatomy in a three-dimensional virtual reality environment. Computers & Education, 106, 150–165. https://doi.org/10.1016/j.compedu.2016.12.009
Jiang, W. Y., Zhu, X. H., & Chen, C. (2007). Research progress of virtual tourism. Science & Technology Review, 14, 53–57. https://doi.org/10.3321/j.issn:1000-7857.2007.14.009
Lanzo, J. A., Valentine, A., Sohel, F., Yapp, A. Y. T., Muparadzi, K. C., & Abdelmalek, M. (2020). A review of the uses of virtual reality in engineering education. Computer Applications in Engineering Education, 28(3), 748–763. https://doi.org/10.1002/cae.22243
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2324. https://doi.org/10.1109/5.726791
Lee, M. J. C., Tidman, S. J., Lay, B. S., Bourke, P. D., Lloyd, D. G., & Alderson, J. A. (2013). Visual search differs but not reaction time when intercepting a 3D versus 2D videoed opponent. Journal of Motor Behavior, 45(2), 107–115. https://doi.org/10.1080/00222895.2012.760512
Leemans, S. J. J., Fahland, D., & van der Aalst, Wil M. P. (2016;2018). Scalable process discovery and conformance checking. Software and Systems Modeling, 17(2), 599–631. https://doi.org/10.1007/s10270-016-0545-x
Liu, G. P., & Gao, N. (2021). Mechanisms of gestural interaction virtual experiments on learning experience. Modern Distance Education Research, 33(2), 22-32 + 72. https://doi.org/10.3969/j.issn.1009-5195.2021.02.003
Lorenzo, G., Lledó, A., Pomares, J., & Roig, R. (2016). Design and application of an immersive virtual reality system to enhance emotional skills for children with autism spectrum disorders. Computers & Education, 98, 192–205. https://doi.org/10.1016/j.compedu.2016.03.018
Makransky, G., Terkildsen, T. S., & Mayer, R. E. (2019). Adding immersive virtual reality to a science lab simulation causes more presence but less learning. Learning and Instruction, 60, 225–236. https://doi.org/10.1016/j.learninstruc.2017.12.007
Manganaro, M. S., Morag, Y., Weadock, W. J., Yablon, C. M., Gaetke-Udager, K., & Stein, E. B. (2017). Creating three-dimensional printed models of acetabular fractures for use as educational tools. Radiographics, 37(3), 871–880. https://doi.org/10.1148/rg.2017160129
Mann, S., Furness, T., Yuan, Y., Iorio, J., & Wang, Z. (2018). All reality: virtual, augmented, mixed (x), mediated (x,y), and multimediated reality. Human-Computer Interaction. https://doi.org/10.48550/arXiv.1804.08386
Mao, C. C., Chen, C. H., & Sun, C. C. (2017). Impact of an augmented reality system on learning for army military decision-making process (MDMP) course. In Advances in Ergonomics Modeling, Usability & Special Populations: Proceedings of the AHFE 2016 International Conference on Ergonomics Modeling, Usability & Special Populations, July 27–31, 2016, Walt Disney World®, Florida, USA (pp. 663–671). Springer International Publishing.
Milgram, P., & Kishino, F. (1994). Taxonomy of mixed reality visual displays. IEICE Transactions on Information and Systems, E77-D(12), 1321–1329.
Mintah, E. K. (2014). Using group method of teaching to address the problem of large class size: An action research. International Journal of Learning and Development, 4(2), 82–97. https://doi.org/10.5296/ijld.v4i2.5707
Mott, K. (2019). State classification of cooking objects using a vgg cnn. Computer Vision and Pattern Recognition. https://doi.org/10.48550/arXiv.1904.12613
Mulryan-Kyne, C. (2010). Teaching large classes at college and university level: Challenges and opportunities. Teaching in Higher Education, 15(2), 175–185. https://doi.org/10.1080/13562511003620001
Nonaka, I., Umemoto, K., & Sasaki, K. (1998). Three tales of knowledge-creating companies. Knowing in firms: Understanding, managing, and measuring knowledge. 1998, 146–172.
Paas, F. G., & Van Merriënboer, J. J. (1994). Instructional control of cognitive load in the training of complex cognitive tasks. Educational Psychology Review, 6, 351–371.
Peng, H. C., Zhao, J. B., & Yan, B. H. (2022). Is technology enabling learning? -The impact of learners’ use of technology on the learning experience. Open Education Research, 28(2), 110–120. https://doi.org/10.13966/j.cnki.kfjyyj.2022.02.012
Qualcomm. (2017). The mobile future of extended reality (p. 4). Qualcomm Technologies Inc.
Ramadijanti, N., Fahrul, H. F., & Pangestu, D. M. (2016). Basic dance pose applications using kinect technology. 2016 International Conference on Knowledge Creation and Intelligent Computing (KCIC). IEEE.
Salisbury, J. P., Keshav, N. U., Sossong, A. D., & Sahin, N. T. (2018). Concussion assessment with smartglasses: validation study of balance measurement toward a lightweight, multimodal, field-ready platform. JMIR mHealth and uHealth, 6(1). https://doi.org/10.2196/mhealth.8478
Senecal, S., Nijdam, N. A., Aristidou, A., & Magnenat-Thalmann, N. (2020). Salsa dance learning evaluation and motion analysis in gamified virtual reality environment. Multimedia Tools and Applications, 79(33–34), 24621–24643. https://doi.org/10.1007/s11042-020-09192-y
Sullivan, M. E. (2020). Applying the science of learning to the teaching and learning of surgical skills: The basics of surgical education. Journal of Surgical Oncology, 122(1), 5–10. https://doi.org/10.1002/jso.25922
Sun, G. Y., Chen, W. J., & Sun, Q. J. (2018). Research on a Dance training system based on VR. Journal of Communication University of China (Natural Science), (5), 22–28. https://doi.org/10.16196/j.cnki.issn.1673-4793.2018.05.003
Sweller, J. (1988). Cognitive load during problem solving: effects on leaming. Cognitive Science, 12(2), 257–285.
Tan, Y., & Yuan, Q. J. (2019). Cognitive load theory and its application and perspectives in information systems research. Journal of Modern Information, 39(12), 10.
Tanaka, F., Fortenberry, B., Aisaka, K., & Movellan, J. R. (2005). Plans for Developing Real-time Dance Interaction between QRIO and Toddlers in a Classroom Environment. Proceedings of 2005 4th IEEE International Conference on Development and Learning, 142–147. https://doi.org/10.1109/devlrn.2005.1490963
Thompson, A., & Potter, L. E. (2017). Proposing augmentation of live sporting events with gamification and social sharing. In Paper presented at the 29th Australian conference on computer-human interaction, Brisbane, Australia. https://doi.org/10.1145/3152771.3156174
Van Ginkela, S., Gulikers, J., Biemans, H., Noroozi, O., Roozen, M., Bos, T., van Tilborg, R., van Halteren, M., & Mulder, M. (2019). Fostering oral presentation competence through a virtual reality-based task for delivering feedback. Computers & Education, 134, 78–97. https://doi.org/10.1016/j.compedu.2019.02.006
Van Merrienboer, J. J., & Sweller, J. (2005). Cognitive load theory and complex learning: Recent developments and future directions. Educational psychology review, 147–177. https://www.jstor.org/stable/23363899. Accessed 20 Jan 2023
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. https://doi.org/10.2307/41410412
Vignais, N., Bideau, B., Craig, C., Brault, S., Multon, F., Delamarche, P., & Kulpa, R. (2009). Does the level of graphical detail of a virtual handball thrower influence a goalkeeper’s motor response? Journal of Sports Science & Medicine, 8(4), 501–508. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3761546/. Accessed 16 Jan 2023
Wang, W. P., & Li, W. (2020). Regional differences in online learning experiences of Chinese college students and the factors affecting them-analysis based on survey data from 334 universities in China. Open Education Research, 26(06), 89–99. https://doi.org/10.13966/j.cnki.kfjyyj.2020.06.009
Wu, H. K., Lee, S. W. Y., Chang, H. Y., & Liang, J. C. (2013). Current status, opportunities and challenges of augmented reality in education. Computers & Education, 62, 41–49. https://doi.org/10.1016/j.compedu.2012.10.024
Wu, D., Li, H., & Wei, X. R. (2022). Digital transformation in education: International context, development needs and promotion path. Distance Education in China, (7), 9. https://doi.org/10.13541/j.cnki.chinade.2022.07.009
Xu, J. J., Tian, Y., Gao, B. Y., Zhuang, R. X., & Yang, L. (2018). Research on learner satisfaction based on learning experience in smart classroom. Modern Educational Technology, 09, 40–46.
Yang, Y., Leung, H., Yue, L., & Deng, L. (2013). Generating a two-phase lesson for guiding beginners to learn basic dance movements. Computers and Education, 61(1), 1–20. https://doi.org/10.1016/j.compedu.2012.09.006
Yilmaz, R. M. (2016). Educational magic toys developed with augmented reality technology for early childhood education. Computers in Human Behavior, 54, 240–248. https://doi.org/10.1016/j.chb.2015.07.040
Yu, Y., & Lin, G. R. (2022). Digital technology for quality postgraduate education: what is possible and what can be done. China Higher Education Research, 351(11), 53–60. https://doi.org/10.16298/j.cnki.1004-3667.2022.11.07
Zakariya, Q., Arafat, A. M., & Buket, D. B. (2017). Deep convolutional neural network for age estimation based on VGG-Face model. Computer Vision and Pattern Recognition. https://doi.org/10.48550/arXiv.1709.01664
Zatoń, K., & Szczepan, S. (2014). The impact of immediate verbal feedback on the improvement of swimming technique. Journal of Human Kinetics, 41(1), 143–154. https://doi.org/10.2478/hukin-2014-0042
Zhao, W., & Li, Y. (2022). Kinect-based human pose estimation optimization and animation generation. Journal of Computer Applications, 1-9[2022-02-02]. https://doi.org/10.11772/j.issn.1001-9081.2021061043
Acknowledgements
This work was supported the financial supports by National Science Foundation Youth Foundation of China “Research on Supporting Mechanism and Model Construction of Process Mining Technology for Teaching Decision-making in Online Sharing Regulation” Grant/Award Number: 62207026 and Higher Education "Fourteen Fifth" Teaching Reform Project in Zhejiang Province “Research and practice on the reform of evidence-based performance evaluation of AI technology” Grant/Award Number: jg20220101.
Funding
This work was supported by the [National Science Foundation Youth Foundation] under [Grant number 62207026], and the [Higher Education "Fourteen Fifth" Teaching Reform Project in Zhejiang Province] under [Grant number jg20220101].
Author information
Authors and Affiliations
Contributions
Wei Xu and Qian-Wen Xing designed and carried out the research and conducted the data analysis and summary. Jing-Dong Zhu and Xiao Liu conducted the research, and Pin-Nv Jin participated in the data analysis. All authors contributed to the article and approved the submitted version.
Corresponding author
Ethics declarations
Ethics approval
This study was carried out without ethics issue.
Consent to participate
All participants gave their consent for participation.
Consent for publication
All authors gave their consent for publication.
Competing interests
The authors have no relevant financial or non-financial interests to disclose.
Conflicts of interest
The authors declare no conflicts of interest.
Additional information
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Xu, W., Xing, QW., Zhu, JD. et al. Effectiveness of an extended-reality interactive learning system in a dance training course. Educ Inf Technol 28, 16637–16667 (2023). https://doi.org/10.1007/s10639-023-11883-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10639-023-11883-6