The effectiveness of VR environment on primary and secondary school students’ learning performance in science courses
Bibliographic record
Abstract
VR technology is being widely used in education due to it’s a sense of immersion, real-time interaction, and ability to stimulate imagination. However, there is a lack of research comparing students at different stages. This study used a quasi-experimental design involving 73 fourth-grade and 86 eighth-grade students in two schools in northwest China minority gathering area. One class in each grade level used Virtual Reality (VR) headsets device to teach science content, while the other used interactive whiteboards. The study aimed to examine the differences in students’ learning performance, problem-solving ability, self-efficacy, and technology acceptance. The findings showed that VR environment: (a) enhanced the learning performance of primary school students but did not have a significant impact on secondary students; (b) significantly improved problem-solving ability for both primary and secondary school students; (c) significantly promoted self-efficacy for both primary and secondary students; and (d) had a significant influence on technology acceptance for both primary and secondary students. Therefore, we recommended that teachers could integrate VR technology into science labs to enhance secondary students’ problem-solving ability and self-efficacy, and improve primary students’ learning performance, problem-solving ability, and self-efficacy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".