The use of virtual reality scenarios in science: Results of a design-based research experiment
Bibliographic record
Abstract
Conference paper for the EARLI 2023 conference We are witnessing a decline in student interest in science and technology, which is often attributed to the abstraction of science concepts, the scope of the content and teacher-centred approaches. The use of VR simulations appeared promising in the COVID-19 period, when access to laboratories was scarce. For VR simulations, the pedagogical scenarios seem very important. This design-based research aims to explore the pedagogical and didactic potential of computer-based VR scenarios for postsecondary science courses. A mixed methodology relying on individual questionnaires and group interviews was deployed on seven sites with 39 teachers and 5,759 students, grounded in Pintrich’s expectancy-value model of motivation and engagement, the TAM3 technology acceptance model, the theory of interest, and Mahlke’s (2008) model of user experience. After using the simulations, both teachers and students reported advantages, but these varied quite a bit depending on who was responding. While teachers focus on the pedagogical advantages, such as diversifying teaching methods, students focus first on the affective aspects of the experience (fun, perceived enjoyment, etc.) and also on some advantages for learning (e.g., visualization). Multilevel regression models show that the scenario scores, associated with quality and complexity, are an important and significant variable at the teacher level.
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 | 0.036 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".