Using Low-immersive Virtual Reality in Online Learning: Field Notes from Environmental Management Education
Bibliographic record
Abstract
Recent research in the field of virtual reality (VR) education is dominated by the application, experience, and effectiveness of high-immersive environments. However, high-immersive VR may not be accessible to all learners, with online distance learning students in particular unable to fully engage without being supplied with appropriate accessories. These field notes shed light on the role of low-immersive VR as a desktop tool for online distance learning students, exploring student experience of using 360° virtual spaces to undertake a summative assessment. Primary data collection in the form of an anonymous online survey was employed to gather feedback from postgraduate environmental management students who used low-immersive VR to undertake an environmental management system audit of a university campus. Quantitative results were analysed using descriptive statistics and qualitative responses using thematic analysis. Findings indicated that with guidance from the academic teaching staff and practice using the software, the majority of students felt both prepared and happy to undertake a summative assessment using VR spaces. Skills development and an appreciation of the effectiveness of the assessment approach were also highlighted as positive outcomes reinforcing findings from literature on the value of VR to improve learning outcomes particularly with practical tasks. Limitations of the assessment content and software were however noted by students, but both could be resolved with adaptations to the tool. It is hoped this research will be valuable to online education providers to demonstrate the value of using low-immersive VR within their programmes.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".