Learner-Centered Analysis in Educational Metaverse Environments: Exploring Value Exchange Systems through Natural Interaction and Text Mining

Abstract

Amid Education 4.0 and the Fourth Industrial Revolution, we explore the integration of self-directed learning within the metaverse. This study envisions empowered learners, merging the metaverse’s immersive potential with self-directed learning. Using text mining and co-occurrence analysis for student responses prompts questions about their preference over traditional methods. Text mining extracts nuanced insights from open-ended responses, surpassing structured data. Co-occurrence analysis reveals hidden concept relationships, enhancing student engagement and understanding. Beyond XR, the metaverse encompasses avatars, virtual experiences, and value systems. Educators navigate this landscape with text mining, shaping value exchange through engaging content. Integrating real-world experiences in the metaverse merges immersion and personalized learning. Challenges include data precision and semantic intricacies in co-occurrence graphs. Future solutions involve real- time adaptability and sentiment analysis for holistic insights into learner emotions. This study envisions a synergy of self- directed learning and the metaverse, bridging digital and physical realms. Learners navigate interconnected experiences, fostering autonomy. Uncovering the metaverse’s potential contributes to education for digitally adept learners.

Keywords

• Education 4.0
• learner-centered
• educational metaverse
• blockchain
• value exchange
• text-mining

Project Number

112-2410-H-003-006

References

1. S. Mystakidis, “Metaverse,” Encyclopedia, vol. 2, no. 1, pp. 486–497, 2022.
2. Y.-C. Tsai, “The value chain of education metaverse,” arXiv preprint arXiv:2211.05833, 2022.
3. K. D. Setiawan, A. Anthony et al., “The essential factor of metaverse for business based on 7 layers of metaverse–systematic literature review,” in 2022 International Conference on Information Management and Technology (ICIMTech). IEEE, 2022, pp. 687–692.
4. R. Ferreira-Mello, M. Andre´, A. Pinheiro, E. Costa, and C. Romero, “Text mining in education,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 9, no. 6, p. e1332, 2019.
5. M. Hernandez-de Menendez, C. A. Escobar D´ıaz, and R. Morales- Menendez, “Engineering education for smart 4.0 technology: a review,” International Journal on Interactive Design and Manufacturing (IJI- DeM), vol. 14, pp. 789–803, 2020.
6. D. A. Norman and J. C. Spohrer, “Learner-centered education,” Com- munications of the ACM, vol. 39, no. 4, pp. 24–27, 1996.
7. P. Caratozzolo, A. Friesel, P. J. Randewijk, and D. Navarro-Duran, “Virtual globalization: An experience for engineering students in the education 4.0 framework,” in 2021 ASEE Virtual Annual Conference Content Access, 2021.
8. R. M. Moate and J. A. Cox, “Learner-centered pedagogy: Considerations for application in a didactic course.” Professional Counselor, vol. 5, no. 3, pp. 379–389, 2015.

9. E. Oliveira, P. G. de Barba, and L. Corrin, “Enabling adaptive, person- alised and context-aware interaction in a smart learning environment: Piloting the icollab system,” Australasian Journal of Educational Tech- nology, vol. 37, no. 2, pp. 1–23, 2021.
10. M. Cukurova, M. Giannakos, and R. Martinez-Maldonado, “The promise and challenges of multimodal learning analytics,” British Journal of Educational Technology, vol. 51, no. 5, pp. 1441–1449, 2020.
11. Y.-C. Tsai, J.-Y. Huang, and D.-R. Chiou, “Empowering young learners to explore blockchain with user-friendly tools: A method using google blockly and nfts,” arXiv preprint arXiv:2303.09847, 2023.
12. S. Periyasami and A. P. Periyasamy, “Metaverse as future promising plat- form business model: Case study on fashion value chain,” Businesses, vol. 2, no. 4, pp. 527–545, 2022.
13. E. AbuKhousa, M. S. El-Tahawy, and Y. Atif, “Envisioning architecture of metaverse intensive learning experience (milex): Career readiness in the 21st century and collective intelligence development scenario,” Future Internet, vol. 15, no. 2, p. 53, 2023.
14. J. Han, G. Liu, and Y. Gao, “Learners in the metaverse: A systematic review on the use of roblox in learning,” Education Sciences, vol. 13, no. 3, p. 296, 2023.
15. M. Ersoy and R. Gu¨rfidan, “Blockchain-based asset storage and service mechanism to metaverse universe: Metarepo,” Transactions on Emerging Telecommunications Technologies, vol. 34, no. 1, p. e4658, 2023.
16. L. I. Gonza´lez-Pe´rez and M. S. Ram´ırez-Montoya, “Components of education 4.0 in 21st century skills frameworks: systematic review,” Sustainability, vol. 14, no. 3, p. 1493, 2022.
17. W. Suh and S. Ahn, “Utilizing the metaverse for learner-centered con- structivist education in the post-pandemic era: an analysis of elementary school students,” Journal of Intelligence, vol. 10, no. 1, p. 17, 2022.
18. T. Huk, “From education 1.0 to education 4.0-challenges for the con- temporary school,” The New Educational Review, vol. 66, pp. 36–46, 2021.
19. M. Zhu, C. J. Bonk, and M. Y. Doo, “Self-directed learning in moocs: Exploring the relationships among motivation, self-monitoring, and self-management,” Educational Technology Research and Development, vol. 68, pp. 2073–2093, 2020.
20. E. Rabin, Y. M. Kalman, and M. Kalz, “An empirical investigation of the antecedents of learner-centered outcome measures in moocs,” International Journal of Educational Technology in Higher Education, vol. 16, no. 1, pp. 1–20, 2019.
21. C. Romero and S. Ventura, “Educational data mining and learning analytics: An updated survey,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 10, no. 3, p. e1355, 2020.
22. P. Blikstein and M. Worsley, “Multimodal learning analytics and educa- tion data mining: Using computational technologies to measure complex learning tasks,” Journal of Learning Analytics, vol. 3, no. 2, pp. 220– 238, 2016.
23. Y.-C. Tsai, “Empowering learner-centered instruction: Integrating chat- gpt python api and tinker learning for enhanced creativity and problem- solving skills,” arXiv preprint arXiv:2305.00821, 2023.
24. E. Johnston, G. Olivas, P. Steele, C. Smith, and L. Bailey, “Exploring pedagogical foundations of existing virtual reality educational applica- tions: A content analysis study,” Journal of Educational Technology Systems, vol. 46, no. 4, pp. 414–439, 2018.
25. H. Lohman, Y. Griffiths, B. M. Coppard, and L. Cota, “The power of book discussion groups in intergenerational learning,” Educational Gerontology, vol. 29, no. 2, pp. 103–115, 2003.
26. L. S. Davis, S. A. Johns, and J. Aggarwal, “Texture analysis using generalized co-occurrence matrices,” IEEE Transactions on pattern analysis and machine intelligence, no. 3, pp. 251–259, 1979.