Impact of avatar-based metaverse learning on students’ self-expansion: a multi-group analysis of prior experience and educational levels
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".