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

Year 2024, Volume: 4 Issue: 2, 165 - 176, 31.12.2024

Wu Xiaolan Hasan Tınmaz

https://doi.org/10.57019/jmv.1582429

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

Ethical Statement

In this article, the principles of scientific research and publication ethics were followed. This study did not involve human or animal subjects and did not require additional ethics committee approval.

Supporting Institution

This research was supported by Woosong University Academic Research in 2024.

References

• 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
• 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
• 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
• 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
• 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.
• Mustafa, B. (2022). Analyzing education based on metaverse technology. Technium Soc. Sci. J., 32, 278. https://doi.org/10.47577/tssj.v32i1.6742
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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.
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications.
• Cochran, W.G. (1963) Sampling Technique (2nd Ed.), John Wiley and Sons.
• 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
• 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
• Bartlett, M. S. (1950). Tests of significance in factor analysis. British journal of psychology, 3, 77-85.
• Pett, M. A., Lackey, N. R., & Sullivan, J. J. (2003). Making sense of factor analysis. SAGE Publications, Inc., https://doi.org/10.4135/9781412984898
• Kaiser, H. F. (1974). An index of factorial simplicity. psychometrika, 39(1), 31-36. https://doi.org/10.1007/BF02291575
• Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1995). Multivariate data analysis. Englewood Cliffs, NJ: Prentice-Hall.
• Tabachnick, B. G., Fidell, L. S., & Ullman, J. B. (2013). Using multivariate statistics (Vol. 6, pp. 497-516). Boston, MA: pearson.
• Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. psychometrika, 16(3), 297-334. https://doi.org/10.1007/BF02310555
• 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
• 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
• 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