Metaverse Enhancing Vocational Skills of Digital Media Education in Chinese Higher Vocational Institutions
Bibliographic record
Abstract
This study explores the impact of metaverse on vocational skills training in higher vocational education in my country, focusing on practical experience, professional knowledge, innovation ability, and technical proficiency and assessing the level of students using metaverse to improve technical and vocational skills. The research subjects are students in the audio-visual language course of the digital media art design major at Fuzhou Software Vocational and Technical College. A quantitative research method was adopted, using teaching plans, pre-test and post-tests, questionnaires, and other tools, and a total of 54 valid questionnaires were collected. The results showed a high level: (1) The impact of metaverseon vocational skills training in higher vocational education, focusing on practical experience, professional knowledge, innovation ability, and technical proficiency. (2) Vocation skill after learning by metaversehigher than before learning by metaverse technology. (3) Students who learn metaverse had to qualify the student’s feedback level high. Metaversefosters an interactive learning approach, enhancing vocational skills, transforming educational models, and supporting teaching reform while laying the groundwork for its broader application in vocational education through curriculum innovation.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".