Exploring the Mediating Role of Student Engagement in the Relationship Between Virtual Reality Interactivity and Creativity in a Project-Based Learning Environment
Bibliographic record
Abstract
Virtual reality (VR) environment, due to its immersive and interactive characteristics, can effectively enhance learners' on-site experience, and gradually become a new educational scene. Existing studies have shown that compared with traditional teaching environments, learning methods using VR technology can significantly improve the teaching effect, but the internal mechanism of promoting the development of creativity is not clear. Therefore, this study carried out a quasi-experimental study under the framework of project-based learning (PBL), and set two sets of control conditions of VR-PBL and traditional PBL to systematically collect learners' participation and creativity performance data. A standardized scale was used to measure learners' perception of VR interactivity and their multi-dimensional learning input (emotional and cognitive dimensions), and an expert scoring method was used to evaluate the creativity test works of painting. The results show that: (1) VR technology significantly improves the level of learning engagement by enhancing interactivity; (2) Learning engagement is positively correlated with creativity performance; (3) Learning engagement had a partial mediating effect between VR interactivity and creativity performance. This study provides a new perspective for the design of VR-based educational intervention programs and provides a practical reference for fostering creativity through optimizing interactive learning environments.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".