Impact of avatar-based metaverse learning on students’ self-expansion: a multi-group analysis of prior experience and educational levels
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In recent years, advancements in virtual learning tools have significantly transformed the field of education. Among these innovations, the development of metaverse learning environments has gained increasing importance within the educational sector. Immersive school scenes, interactive features, and customizable avatars are key elements that enhance student learning performance. However, the effects of these environments on students’ self-expansion remain largely unexplored. Therefore, we proposed a research model that measures student learning outcomes and conducts a multi-group comparison based on prior experience in the metaverse and educational levels. Data were collected from 254 students in Hong Kong. Our findings indicate that Avatar-Based Learning Experience (ALE), Immersive Engagement (IE), Interactive Simulation (IS), and Sense of Presence (SP) are critical factors contributing to students’ self-expansion within metaverse education. Moreover, students with prior experience in the metaverse exhibited higher levels of self-expansion. Notably, male students in higher education reported higher levels of ALE and SP than those with school-level education. However, no statistically significant differences were found among female students across different educational levels. This study provides valuable insights for educators and metaverse developers in designing customized teaching materials and creating more engaging virtual environments to enhance student motivation and learning outcomes in the future.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it