Exploring University Teachers’ Perceptions of Metaverse Integration in Higher Education: A Quantitative Study from China

Abstract

Understanding these perceptions is crucial for successful adoption and implementation, particularly in the context of higher education institutions in Jiangsu Province, China, as little is known about university teachers’ perceptions of these technologies and their potential applications in educational settings. This study employed a quantitative research approach, collecting data through a questionnaire sent to 389 university teachers from institutions offering Bachelor’s programs. The data were analyzed using SPSS 26, applying statistical techniques such as independent sample t-tests, Analysis of Variance (ANOVA), and factor analysis to examine the influence of demographic variables on teachers’ perceptions. The analysis revealed that demographic factors, including teachers’ age, academic qualifications, years of teaching experience, and attitudes toward new educational technologies, significantly influenced their perceptions of the application of Metaverse technologies in higher education. The findings contribute to the growing body of knowledge on technology acceptance in educational contexts and offer practical implications for policymakers and educators aiming to integrate innovative technologies into teaching and learning practices.

Keywords

• University teachers
• Metaverse
• Perception of Metaverse
• Higher education

References

1. Wei, F., & Yuxiang, F. (2024). yuanyuzhou: gainian jishu ji yingyong yanjiu zongshu [Metaverse: conceptions, key technologies and applications]. Nanjing Xinxi Gongcheng Daxue Xuebao (ziran kexue ban) [Journal of Nanjing University of Information Science and Technology (Natural Science Edition)], 54(1), 18–43. https://doi.org/10.13878/j.cnki.jnuist.20221129003
2. Zhang, X., Chen, Y., Hu, L., & Wang, Y. (2022). The metaverse in education: Definition, framework, features, potential applications, challenges, and future research topics. Frontiers in Psychology, 13, 1016300. DOI: 10.3389/fpsyg.2022.1016300
3. Suzuki, S. N., Kanematsu, H., Barry, D. M., Ogawa, N., Yajima, K., Nakahira, K. T., ... & Yoshitake, M. (2020). Virtual Experiments in Metaverse and their Applications to Collaborative Projects: The framework and its significance. Procedia Computer Science, 176, 2125-2132. https://doi.org/10.1016/j.procs.2020.09.249
4. Muijs, D. (2015). Improving schools through collaboration: a mixed methods study of school-to-school partnerships in the primary sector. Oxford Review of Education, 41(5), 563-586. https://doi.org/10.1080/03054985.2015.1047824
5. Yang, L., & Zhu, D. Q. (2022). jiaoyu yuanyuzhou: weilaijiaoyude wutuobang xiangxiang yu jishu lunli fansi [The Metaverse of Education: Utopian Imagination of Future Education and Reflections on Technical Ethics]. Yunnan Shifan Daxue Xuebao (zhexue shehui kexue ban)[Journal of Yunnan Normal University (Philosophy and Social Sciences Edition)], 54(4), 18–43.
6. Mustafa, B. (2022). Analyzing education based on metaverse technology. Technium Soc. Sci. J., 32, 278. https://doi.org/10.47577/tssj.v32i1.6742
7. Aydin, S. (2023). Teachers’ Perceptions of the Use of the Metaverse in Foreign Language Teaching and Learning. In Shaping the future of online learning: Education in the metaverse (pp. 201-219). IGI Global.S. DOI: 10.4018/978-1-6684-6513-4.ch011
8. Toraman, Y. (2022). User acceptance of metaverse: Insights from technology acceptance model (TAM) and planned behavior theory (PBT). EMAJ: Emerging Markets Journal, 12(1), 67-75. https://doi.org/10.5195/emaj.2022.258

9. Wang, Y., Liu, C., & Tu, Y. F. (2021). Factors affecting the adoption of AI-based applications in higher education. Educational Technology & Society, 24(3), 116-129. https://www.jstor.org/stable/27032860
10. Amron, A., Mursid, A., & Suhartono, E. (2024). Exploring the Impact of Perceived Usefulness and Enjoyment to Enhance Intention to Use and Satisfaction among Metaverse Travellers. Journal of Ecohumanism, 3(8), 5857-5866. https://doi.org/10.62754/joe.v3i8.5195
11. Wu, R., & Yu, Z. (2024). Investigating users’ acceptance of the metaverse with an extended technology acceptance model. International Journal of Human–Computer Interaction, 40(19), 5810-5826. https://doi.org/10.1080/10447318.2023.2241295
12. Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS quarterly, 189-211. https://doi.org/10.2307/249688
13. Bai, B., Wang, J., & Chai, C. S. (2021). Understanding Hong Kong primary school English teachers’ continuance intention to teach with ICT. Computer Assisted Language Learning, 34(4), 528-551. DOI:10.1080/09588221.2019.1627459
14. Yeşilyurt, E., Ulaş, A. H., & Akan, D. (2016). Teacher self-efficacy, academic self-efficacy, and computer self-efficacy as predictors of attitude toward applying computer-supported education. Computers in human Behavior, 64, 591-601. https://doi.org/10.1016/j.chb.2016.07.038
15. Chatterjee, S., & Bhattacharjee, K. K. (2020). Adoption of artificial intelligence in higher education: A quantitative analysis using structural equation modelling. Education and Information Technologies, 25, 3443-3463. https://doi.org/10.1007/s10639-020-10159-7
16. Al-Adwan, A. S., Li, N., Al-Adwan, A., Abbasi, G. A., Albelbisi, N. A., & Habibi, A. (2023). Extending the technology acceptance model (TAM) to Predict University Students’ intentions to use metaverse-based learning platforms. Education and Information Technologies, 28(11), 15381-15413. https://doi.org/10.1007/s10639-023-11816-3
17. Çengel, M., & Yildiz, E. P. (2022). Teachers’ attitude scale towards Metaverse use: a scale development study. Education Quarterly Reviews, 5(4). https://ssrn.com/abstract=4314357
18. Salloum, S., Shaalan, K., Alfaisal, R., Salloum, A., & Gaber, T. (2024). Integrating ChatGPT into Medical Education: A Combined SEM-ML Approach. In 2024 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC) (pp. 1-5). IEEE. https://doi.org/10.1109/ASSIC60049.2024.10507994
19. Akour, I. A., Al-Maroof, R. S., Alfaisal, R., & Salloum, S. A. (2022). A conceptual framework for determining metaverse adoption in higher institutions of gulf area: An empirical study using hybrid SEM-ANN approach. Computers and education: artificial intelligence, 3, 100052. https://doi.org/10.1016/j.caeai.2022.100052
20. Teo, T. (2019). Students and teachers’ intention to use technology: Assessing their measurement equivalence and structural invariance. Journal of Educational Computing Research, 57(1), 201-225. https://doi.org/10.1177/0735633117749430
21. Prakash, A., Haque, A., Islam, F., & Sonal, D. (2023). Exploring the potential of metaverse for higher education: Opportunities, challenges, and implications. Metaverse Basic and Applied Research, 2, 40-40. https://doi.org/10.56294/mr202340
22. Stanoevska-Slabeva, K. (2022). Opportunities and challenges of metaverse for education: a literature review. Edulearn22 Proceedings, 10401-10410. https://doi.org/10.21125/edulearn.2022.2527
23. Kye, B., Han, N., Kim, E., Park, Y., & Jo, S. (2021). Educational applications of metaverse: possibilities and limitations. Journal of educational evaluation for health professions, 18. https://doi.org/10.3352/jeehp.2021.18.32
24. Khalil, A., Saher, U., & Haqdad, A. (2023). Prospects and challenges of educational metaverse in higher education. Journal of Positive School Psychology, 1648-1663. http://journalppw.com
25. Ahuja, A. S., Polascik, B. W., Doddapaneni, D., Byrnes, E. S., & Sridhar, J. (2023). The digital metaverse: Applications in artificial intelligence, medical education, and integrative health. Integrative Medicine Research, 12(1), 100917. https://doi.org/10.1016/j.imr.2022.100917
26. Martínez, E., Montoya, E., Flórez, M., Carbonell, V., & del Fresno, L. (2024). Designing an Immersive Virtual Physics Laboratory Environment. In ICERI2024 Proceedings (pp. 268-272). IATED. https://doi.org/10.21125/iceri.2024.0145
27. Won, J. H., Choi, Y., & Kim, Y. S. (2021). A metaverse platform for engineering education: Case of South Korea. International Journal of Internet, Broadcasting and Communication, 13, 129-134.
28. Sidhu, M. S., Mousakhani, S., Lee, C. K., & Sidhu, K. K. (2024). Educational impact of Metaverse learning environment for engineering mechanics dynamics. Computer Applications in Engineering Education, 32(5), e22772. https://doi.org/10.1002/cae.22772
29. Smith, P. (2022). Black immigrants in the United States: Transraciolinguistic justice for imagined futures in a global metaverse. Annual Review of Applied Linguistics, 42, 109-118. https://doi.org/10.1017/S0267190522000046
30. Kanematsu, H., Fukumura, Y., Barry, D. M., Sohn, S. Y., & Taguchi, R. (2010). Multilingual discussion in metaverse among students from the USA, Korea and Japan. In Knowledge-Based and Intelligent Information and Engineering Systems: 14th International Conference, KES 2010, Cardiff, UK, September 8-10, 2010, Proceedings, Part IV 14 (pp. 200-209). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-15384-6_22
31. Singh, C. (2024). Algorithmic Decision Making: Can Artificial Intelligence and the Metaverse Provide Technological Solutions to Modernise the United Kingdom’s Legal Services and Criminal Justice?. Frontiers in Law, 3, 28-39. https://doi.org/10.6000/2817-2302.2024.03.05
32. Tamai, M., Inaba, M., Hosoi, K., Thawonmas, R., Uemura, M., & Nakamura, A. (2011, October). Constructing situated learning platform for Japanese language and culture in 3D metaverse. In 2011 second international conference on culture and computing (pp. 189-190). IEEE. https://doi.org/10.1109/Culture-Computing.2011.59
33. Kanematsu, H., Fukumura, Y., Ogawa, N., Okuda, A., Taguchi, R., & Nagai, H. (2009, June). Practice and evaluation of problem based learning in Metaverse. In EdMedia+ Innovate Learning (pp. 2862-2870). Association for the Advancement of Computing in Education (AACE). https://www.learntechlib.org/p/31886
34. Gayevska, O., & Soroko, N. (2022). Pedagogical strategies with immersive technologies for teaching and learning the Japanese language. Information Technologies and Learning Tools, 6(92), 99-110. https://lib.iitta.gov.ua/id/eprint/734238
35. Nasir, N. B., Moon, J., & Kim, S. B. (2023). Metaverse in Education: Insights from South Korea and Potentials for Malaysia. In 2023 IEEE 8th International Conference On Software Engineering and Computer Systems (ICSECS) (pp. 84-88). IEEE. https://doi.org/10.1109/ICSECS58457.2023.10256325
36. Ji, M., Xi, X., Kim, H., Zhou, Y., Kim, S., & Park, C. (2022). A comparative analysis of English language learning trends in Korea and China using the metaverse. Asia-pacific Journal of Convergent Research Interchange (APJCRI), 8(7), 127-136. doi: 10.47116/apjcri.2022.07.12
37. Junior, A. U. M., & Silveira, I. F. (2023, June). The State-of-the-Art of Research in the Production of Brazilian PhD and MSc Theses that Address the Use of Virtual Reality in Education. In 2023 18th Iberian Conference on Information Systems and Technologies (CISTI) (pp. 1-6). IEEE. https://doi.org/10.23919/CISTI58278.2023.10211934
38. López-Belmonte, J., Pozo-Sánchez, S., Lampropoulos, G., & Moreno-Guerrero, A. J. (2022). Design and validation of a questionnaire for the evaluation of educational experiences in the metaverse in Spanish students (METAEDU). Heliyon, 8(11). https://doi.org/10.1016/j.heliyon.2022.e11364
39. Qiu, Y., Isusi-Fagoaga, R., & García-Aracil, A. (2023). Perceptions and use of metaverse in higher education: A descriptive study in China and Spain. Computers and Education: Artificial Intelligence, 5, 100185. https://doi.org/10.1016/j.caeai.2023.100185
40. Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications.
41. Cochran, W.G. (1963) Sampling Technique (2nd Ed.), John Wiley and Sons.
42. Nanjundeswaraswamy, T. S., & Divakar, S. (2021). Determination of sample size and sampling methods in applied research. Proceedings on engineering sciences, 3(1), 25-32. doi: 10.24874/PES03.01.003
43. Fokides, E. (2023). Development and testing of a scale for examining factors affecting the learning experience in the Metaverse. Computers & Education: X Reality, 2, 100025. https://doi.org/10.1016/j.cexr.2023.100025
44. Bartlett, M. S. (1950). Tests of significance in factor analysis. British journal of psychology, 3, 77-85.
45. Pett, M. A., Lackey, N. R., & Sullivan, J. J. (2003). Making sense of factor analysis. SAGE Publications, Inc., https://doi.org/10.4135/9781412984898
46. Kaiser, H. F. (1974). An index of factorial simplicity. psychometrika, 39(1), 31-36. https://doi.org/10.1007/BF02291575
47. Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1995). Multivariate data analysis. Englewood Cliffs, NJ: Prentice-Hall.
48. Tabachnick, B. G., Fidell, L. S., & Ullman, J. B. (2013). Using multivariate statistics (Vol. 6, pp. 497-516). Boston, MA: pearson.
49. Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. psychometrika, 16(3), 297-334. https://doi.org/10.1007/BF02310555
50. Gürkan, G., & Bayer, H. (2023). A Research on Teachers’ Views about the Metaverse Platform and Its Usage in Education. Journal of Science Learning, 6(1), 59-68. https://doi.org/10.17509/jsl.v6i1.50313
51. Qiu, Y., Isusi-Fagoaga, R., & García-Aracil, A. (2023). Perceptions and use of metaverse in higher education: A descriptive study in China and Spain. Computers and Education: Artificial Intelligence, 5, 100185. https://doi.org/10.1016/j.caeai.2023.100185
52. Eşin, Ş., & Özdemir, E. (2022). The Metaverse in mathematics education: The opinions of secondary school mathematics teachers. Journal of Educational Technology and Online Learning, 5(4), 1041-1060. https://doi.org/10.31681/jetol.1149802