Effects of mobile metaverse-based vocabulary learning on learners’ perception and performance: a case study of Chinese EFL learners

The emergence of metaverse technology has revolutionized the traditional learning methodologies, thereby paving the way for mobile metaverse-based learning. However, there is limited empirical research on effective methods of integrating metaverse technology into educational practices and a comparative analysis of different mobile metaverse-based learning methods. This study aimed to compare the effectiveness of different mobile metaverse-based learning methods in English vocabulary learning and students’ perceptions towards them. Ninety-six students were divided into a control group that used traditional mobile vocabulary learning (TMVL) and experimental groups that used mobile augmented reality-based vocabulary learning (MARVL) and mobile virtual reality-based learning (MVRVL) based on the SAMR model. The participants underwent language exams, questionnaires on effectiveness, usability, and satisfaction and interviews. The results revealed that the experimental groups outperformed the control group in post-tests and delayed post-tests, and different mobile learning methods significantly affected learners’ performance. Participants’ initial language proficiency level served as a covariate to their final scores. However, no significant differences were observed in learners’ performance in post-tests and delayed post-tests within the experimental groups. The questionnaire results showed that only the MARVL group scored significantly higher than the MVRVL group in the dimension of effectiveness. Although the interviewees praised mobile metaverse-based learning methods, no other statistically significant differences were found among the TMVL, MARVL, and MVRVL groups. Correlation analysis revealed that learners’ perceptions of the mobile learning technology they used had little correlation with their post-test performance. The study recommends further research in this field.