The Efficacy of Virtual Reality in Climate Change Education Increases with Amount of Body Movement and Message Specificity
Bibliographic record
Abstract
Climate change impacts are felt globally, and the impacts are increasing in severity and intensity. Developing new interventions to encourage behaviors that address climate change is crucial. This pre-registered field study investigated how the design of a virtual reality (VR) experience about ocean acidification could impact participants’ learning, behavior, and perceptions about climate change through the manipulation of the experience message framing, the sex of voice-over and the pace of the experience, and the amount of participants’ body movement. The study was run in 17 locations such as museums, aquariums, and arcades in the U.S., Canada, the U.K., and Denmark. The amount of body movement was a causal mechanism, eliciting higher feelings of self-efficacy while hindering learning. Moreover, linking the VR narrative about ocean acidification linguistically to climate change impaired learning compared to a message framing that did not make the connection. As participants learned more about the experience, they perceived the risks associated with ocean acidification as higher, and they were more likely to engage in pro-climate behavior. The results shed light on the mechanisms behind how VR can teach about ocean acidification and influence climate change behavior.
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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".