A Bibliometric Analysis of Metaverse-Integrated Learning in Language Acquisition
Keywords:
Metaverse-integrated Learning; Self-efficacy; Collaborative Learning; Multi-modality; Bibliometric AnalysisAbstract
Metaverse-integrated learning (MIL) has garnered increasing attention in educational fields in recent years. While prior research has predominantly focused on scientific fields, only a few scholars have delved into its application in language acquisition. This study aimed to delineate the intellectual landscape of MIL in language acquisition, identifying key regions/countries, authors and journals, and to scrutinize the roles of self-efficacy, collaborative learning, and multi-modality within this context. Based on a bibliometric analysis, this study utilized both quantitative and qualitative methods to examine 248 articles from the Web of Science, spanning from 2010 to 2025, with the aid of VOSviewer and CitNetExplorer. The findings indicate that the primary research hubs are located in Taiwan, the United States and China. Among the authors, Lan Yuju is the most influential, while Computer Assisted Language Learning emerges as the most frequently cited journal in the field. With a significant upward trend in the academic domain, the findings further reveal that self-efficacy, collaborative learning, and multi-modality are not only crucial but also interdependent factors in MIL in language acquisition. These findings may provide implications for relevant educators and policymakers to enhance the MIL in language acquisition within the educational world.
https://doi.org/10.26803/ijlter.25.1.13
References
Adhami, N., & Taghizadeh, M. (2024). Integrating inquiry-based learning and computer supported collaborative learning into flipped classroom: Effects on academic writing performance and perceptions of students of railway engineering. Computer Assisted Language Learning, 37(3), 521–557. https://doi.org/10.1080/09588221.2022.2046107
Bandura, A. (1982). Self-efficacy mechanism in human agency. American psychologist, 37(2), 122-147. https://doi.org/10.1037//0003-066X.37.2.122
Beaver, D., & Rosen, R. (1979). Studies in scientific collaboration: Part III. Professionalization and the natural history of modern scientific co-authorship. Scientometrics, 1(3), 231–245. https://doi.org/10.1007/BF02016308
Camiciottoli, B. C., & Campoy-Cubillo, M. C. (2018). Introduction: The nexus of multimodality, multimodal literacy, and English language teaching in research and practice in higher education settings. System, 77, 1–9. https://doi.org/10.1016/j.system.2018.03.005
Çelik, F., & Baturay, M. H. (2024). The effect of metaverse on L2 vocabulary learning, retention, student engagement, presence, and community feeling. BMC Psychology, 12(1), 58. https://doi.org/10.1186/s40359-024-01549-4
Chen, C. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57(3), 359–377. https://doi.org/10.1002/asi.20317
Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). Science mapping software tools: Review, analysis, and cooperative study among tools. Journal of the American Society for Information Science and Technology, 62(7), 1382–1402. https://doi.org/10.1002/asi.21525
Diao, Y., & Su, Y. S. (2025). Exploring the impact of the metaverse on promoting students’ access to quality education: A meta-analysis. IEEE Transactions on Learning Technologies, 18, 321–334. https://doi.org/10.1109/TLT.2025.3550714
Divekar*, R. R., Drozdal*, J., Chabot*, S., Zhou, Y., Su, H., Chen, Y., Zhu, H., Hendler, J. A., & Braasch, J. (2022). Foreign language acquisition via artificial intelligence and extended reality: Design and evaluation. Computer Assisted Language Learning, 35(9), 2332–2360. https://doi.org/10.1080/09588221.2021.1879162
Early, M., Kendrick, M., & Potts, D. (2015). Multimodality: Out from the margins of English language teaching. TESOL Quarterly, 49(3), 447–460. https://doi.org/10.1002/tesq.246
Ellegaard, O., & Wallin, J. A. (2015). The bibliometric analysis of scholarly production: How great is the impact? Scientometrics, 105(3), 1809–1831. https://doi.org/10.1007/s11192-015-1645-z
Fan, S., Yecies, B., Zhou, Z. I., & Shen, J. (2024). Challenges and opportunities for the Web 3.0 Metaverse turn in education. IEEE Transactions on Learning Technologies, 17, 1935–1950. https://doi.org/10.1109/TLT.2024.3385505
Garfield, E. (1979). Is citation analysis a legitimate evaluation tool? Scientometrics, 1(4), 359–375. https://doi.org/10.1007/BF02019306
Getenet, S., Cantle, R., Redmond, P., & Albion, P. (2024). Students’ digital technology attitude, literacy and self-efficacy and their effect on online learning engagement. International Journal of Educational Technology in Higher Education, 21(1), 3. https://doi.org/10.1186/s41239-023-00437-y
Godwin-Jones, R. (2023). Emerging spaces for language learning: AI bots, ambient intelligence, and the metaverse. Language Learning & Technology, 27(2), 6-27. https://doi.org/10.64152/10125/73501
Gu, X., Yu, T., Huang, J., Wang, F., Zheng, X., Sun, M., Ye, Z., & Li, Q. (2023). Virtual-agent-based language learning: A scoping review of journal publications from 2012 to 2022. Sustainability, 15(18), 13479. https://doi.org/10.3390/su151813479
Guichon, N., & McLornan, S. (2008). The effects of multimodality on L2 learners: Implications for CALL resource design. System, 36(1), 85–93. https://doi.org/10.1016/j.system.2007.11.005
Guo, X. (2023). Multimodality in language education: Implications of a multimodal affective perspective in foreign language teaching. Frontiers in Psychology, 14, 1283625. https://doi.org/10.3389/fpsyg.2023.1283625
Huang, Y., Xu, W., Sukjairungwattana, P., & Yu, Z. (2024). Learners’ continuance intention in multimodal language learning education: An innovative multiple linear regression model. Heliyon, 10(6), e28104. https://doi.org/10.1016/j.heliyon.2024.e28104
Huynh-The, T., Pham, Q. V., Pham, X. Q., Nguyen, T. T., Han, Z., & Kim, D. S. (2023). Artificial intelligence for the metaverse: A survey. Engineering Applications of Artificial Intelligence, 117, 105581. https://doi.org/10.1016/j.engappai.2023.105581
Jabeen, S., Li, X., Amin, M. S., Bourahla, O., Li, S., & Jabbar, A. (2023). A review on methods and applications in multimodal deep learning. ACM Transactions on Multimedia Computing, Communications, and Applications, 19(2s), 1–41. https://doi.org/10.1145/3545572
Kessler, M. (2022). Multimodality. ELT Journal, 76(4), 551–554. https://doi.org/10.1093/elt/ccac028
Ketchen, D. J., & Shook, C. L. (1996). The application of cluster analysis in strategic management research: an analysis and critique. Strategic management journal, 17(6), 441-458. https://doi.org/10.1002/(SICI)1097-0266(199606)17:6%253C441::AID-SMJ819%253E3.0.CO;2-G
Koch, M. J., Kersten, K., & Greve, W. (2024). An emotional advantage of multilingualism. Bilingualism: Language and Cognition, 27(5), 950–963. https://doi.org/10.1017/S1366728923000937
Lampropoulos, G., & Evangelidis, G. (2025). Learning analytics and educational data mining in augmented reality, virtual reality, and the metaverse: A systematic literature review, content analysis, and bibliometric analysis. Applied Sciences, 15(2), 971. https://doi.org/10.3390/app15020971
Lee, S. M., Yang, Z., & Wu, J. G. (2024). Live, play, and learn: Language learner engagement in the immersive VR environment. Education and Information Technologies, 29(9), 10529–10550. https://doi.org/10.1007/s10639-023-12215-4
Leydesdorff, L., Bornmann, L., Comins, J. A., & Milojevi?, S. (2016). Citations: Indicators of quality? The impact fallacy. Frontiers in Research Metrics and Analytics, 1, 1. https://doi.org/10.3389/frma.2016.00001
Li, M., & Yu, Z. (2023). A systematic review on the metaverse-based blended English learning. Frontiers in Psychology, 13, 1087508. https://doi.org/10.3389/fpsyg.2022.1087508
Li, X., Lan, Y., Pi, Z., Qi, G. Y., Grant, S., & Sun, J. (2025). Pedagogical agent positioning in external videos improves English academic presentation proficiency in desktop virtual reality settings. British Journal of Educational Technology, 56(4), 1507–1529. https://doi.org/10.1111/bjet.13531
Liou, H. C. (2012). The roles of Second Life in a college computer-assisted language learning (CALL) course in Taiwan, ROC. Computer Assisted Language Learning, 25(4), 365–382. https://doi.org/10.1080/09588221.2011.597766
Lucia, B., Vetter, M. A., & Adubofour, I. K. (2025). Behold the metaverse: Facebook’s Meta imaginary and the circulation of elite discourse. New Media & Society, 27(2), 790–807. https://doi.org/10.1177/14614448231184249
Makransky, G., & Petersen, G. B. (2023). The theory of immersive collaborative learning (TICOL). Educational Psychology Review, 35(4), 103. https://doi.org/10.1007/s10648-023-09822-5
Min, W., & Yu, Z. (2023). A bibliometric analysis of augmented reality in language learning. Sustainability, 15(9), 7235. https://doi.org/10.3390/su15097235
Mohsen, M. A., Althebi, S., Alsagour, R., Alsalem, A., Almudawi, A., & Alshahrani, A. (2024). Forty-two years of computer-assisted language learning research: A scientometric study of hotspot research and trending issues. ReCALL, 36(2), 230–249. https://doi.org/10.1017/S0958344023000253
Nagao, K. (2023). Virtual reality campuses as new educational metaverses. IEICE Transactions on Information and Systems, E106.D(2), 93–100. https://doi.org/10.1587/transinf.2022ETI0001
Onu, P., Pradhan, A., & Mbohwa, C. (2024). Potential to use metaverse for future teaching and learning. Education and Information Technologies, 29(7), 8893–8924. https://doi.org/10.1007/s10639-023-12167-9
Orakc?, ?., Yüre?illi Göksu, D., & Karagöz, S. (2023). A mixed methods study of the teachers’ self-efficacy views and their ability to improve self-efficacy beliefs during teaching. Frontiers in Psychology, 13, 1035829. https://doi.org/10.3389/fpsyg.2022.1035829
Parmaxi, A. (2023). Virtual reality in language learning: A systematic review and implications for research and practice. Interactive Learning Environments, 31(1), 172–184. https://doi.org/10.1080/10494820.2020.1765392
Qureshi, M. A., Khaskheli, A., Qureshi, J. A., Raza, S. A., & Yousufi, S. Q. (2023). Factors affecting students’ learning performance through collaborative learning and engagement. Interactive Learning Environments, 31(4), 2371–2391. https://doi.org/10.1080/10494820.2021.1884886
Ratten, V. (2023). The post COVID-19 pandemic era: Changes in teaching and learning methods for management educators. The International Journal of Management Education, 21(2), 100777. https://doi.org/10.1016/j.ijme.2023.100777
Rodríguez-Ruiz, J., Marín-López, I., & Espejo-Siles, R. (2025). Is artificial intelligence use related to self-control, self-esteem and self-efficacy among university students? Education and Information Technologies, 30(2), 2507–2524. https://doi.org/10.1007/s10639-024-12906-6
Rosendahl, P., & Wagner, I. (2024). 360° videos in education – A systematic literature review on application areas and future potentials. Education and Information Technologies, 29(2), 1319–1355. https://doi.org/10.1007/s10639-022-11549-9
Roy, R., Babakerkhell, M. D., Mukherjee, S., Pal, D., & Funilkul, S. (2023). Development of a framework for metaverse in education: A systematic literature review approach. IEEE Access, 11, 57717–57734. https://doi.org/10.1109/ACCESS.2023.3283273
Sarkis-Onofre, R., Catalá-López, F., Aromataris, E., & Lockwood, C. (2021). How to properly use the PRISMA statement. Systematic Reviews, 10(1), 117. https://doi.org/10.1186/s13643-021-01671-z
Shu, X., & Gu, X. (2023). An empirical study of a smart education model enabled by the Edu-Metaverse to enhance better learning outcomes for students. Systems, 11(2), 75. https://doi.org/10.3390/systems11020075
Shukla, A., Mishra, A., Rana, N. P., & Banerjee, S. (2024). The future of metaverse adoption: A behavioral reasoning perspective with a text?mining approach. Journal of Consumer Behaviour, 23(5), 2217–2233. https://doi.org/10.1002/cb.2336
Suthers, D. D. (2012). Computer-supported collaborative learning. In N. M. Seel (Ed.), Encyclopedia of the Sciences of Learning (pp. 613–615). Springer. https://doi.org/10.1007/978-1-4419-1428-6_389
Van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3
Van Eck, N. J., & Waltman, L. (2014). CitNetExplorer: A new software tool for analyzing and visualizing citation networks. Journal of Informetrics, 8(4), 802–823. https://doi.org/10.1016/j.joi.2014.07.006
Villalonga-Gómez, C., Ortega-Fernández, E., & Borau-Boira, E. (2023). Fifteen years of metaverse in higher education: A systematic literature review. IEEE Transactions on Learning Technologies, 16(6), 1057–1070. https://doi.org/10.1109/TLT.2023.3302382
Vygotskij, L. S., & Vygotskij, L. S. (2012). Thought and language. Mit Press.
Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes (M. Cole, V. John-Steiner, S. Scribner, & E. Souberman, Eds. & Trans.). Harvard University Press.
Waddington, J. (2023). Self-efficacy. ELT Journal, 77(2), 237–240. https://doi.org/10.1093/elt/ccac046
Wang, C., & Li, X. (2025). Software review: Empowering language education with D-ID Creative Reality Studio’s multimodal capabilities. International Journal of Computer-Assisted Language Learning and Teaching, 15(1), 1–11. https://doi.org/10.4018/IJCALLT.368218
Wang, C. P., Lan, Y. J., Tseng, W. T., Lin, Y. T. R., & Gupta, K. C. L. (2020). On the effects of 3D virtual worlds in language learning: A meta-analysis. Computer Assisted Language Learning, 33(8), 891-915. https://doi.org/10.1080/09588221.2019.1598444
Wehner, A. K., Gump, A. W., & Downey, S. (2011). The effects of Second Life on the motivation of undergraduate students learning a foreign language. Computer Assisted Language Learning, 24(3), 277–289. https://doi.org/10.1080/09588221.2010.551757
Wu, J. G., Zhang, D., & Lee, S. M. (2023). Into the brave new metaverse: envisaging future language teaching and learning. IEEE Transactions on Learning Technologies, 17, 44–53. https://doi.org/10.1109/TLT.2023.3259470
Yu, Z., & Li, M. (2022). A bibliometric analysis of Community of Inquiry in online learning contexts over twenty-five years. Education and Information Technologies, 27(8), 11669–11688. https://doi.org/10.1007/s10639-022-11081-w
Zheng, D., Young, M. F., Brewer, R. A., & Wagner, M. (2010). Attitude and self-efficacy change. CALICO Journal, 27(1), 205–231. https://doi.org/10.11139/cj.27.1.205-231
Zhi, R., Wang, Y., & Wang, Y. (2024). The role of emotional intelligence and self-efficacy in EFL teachers’ technology adoption. The Asia-Pacific Education Researcher, 33(4), 845–856. https://doi.org/10.1007/s40299-023-00782-6
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Yuanling HU, Hazrati Binti Husnin, Aidah Binti Abdul Karim, Mengyao Wu

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
All articles published by IJLTER are licensed under a Creative Commons Attribution Non-Commercial No-Derivatives 4.0 International License (CCBY-NC-ND4.0).