Game-Based Personalized Learning in the Metaverse Computer Virtual Laboratory: Improving User Experience with a Learning Material Recommender System.
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| Title: | Game-Based Personalized Learning in the Metaverse Computer Virtual Laboratory: Improving User Experience with a Learning Material Recommender System. |
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| Authors: | Arif, Yunifa Miftachul1,2 (AUTHOR) yunif4@ti.uin-malang.ac.id, Nurhayati, Hani1 (AUTHOR), Karami, Ahmad Fahmi1 (AUTHOR), Ayunda, Nisa3 (AUTHOR), Subiyakto, A'ang4 (AUTHOR), Samala, Agariadne Dwinggo5 (AUTHOR), Criollo-C, Santiago6 (AUTHOR) |
| Source: | International Journal of Human-Computer Interaction. Oct2025, Vol. 41 Issue 19, p12095-12112. 18p. |
| Subjects: | Recommender systems, Computer science education, User experience, Gamification, Educational outcomes, Laboratories, Individualized instruction, Shared virtual environments |
| Abstract: | Assembly learning is an important component in computer education in terms of emphasizing the understanding of computer components and their functions. This study explores the evolving learning methodologies and media to convey this knowledge, especially in today's era of advanced technology characterized by online interactions. However, the real difficulty in the learning process is guiding students to relevant learning resources. Therefore, this study proposes the integration of Multi-Criteria Recommender System (MCRS) in metaverse, so that we can use the pretest as a reference and make appropriate learning material recommendations. The purpose of integrating the recommendation system into the virtual learning environment is to improve the computer assembly learning process so that students get the right materials and the best learning outcomes. The test results show that the recommender system can generate appropriate learning material recommendations for players, and the Metaverse-based Computer Virtual Laboratory (MCVL) also received a good usability rating. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Human-Computer Interaction is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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