A Critical Analysis of Immersive Environments: A Methodology for Museum Education
Bibliographic record
Abstract
Abstract Due to the COVID‐19 pandemic, museum professionals have adopted various technological resources that have expanded museums into new virtual spaces. These virtual spaces do much more than simply communicate information to visitors and attract them to visit the museum physically: they offer new teaching and learning contexts. The emergence of these new learning contexts calls for the development of new methodologies to analyse them. This article focuses on developing a coding rubric to analyse complex interactive, immersive museum environments systematically. To achieve this, I adapted and tested Gillian Rose's (2022, Visual methodologies: an introduction to researching with visual materials ) critical analysis approach to visual content to explore the museum educational possibilities of virtual reality, using immersive environments by the Louvre and the Victoria and Albert Museum as case studies. I developed a comprehensive coding grid, adjusting Rose's (2022, Visual methodologies: an introduction to researching with visual materials ) principles and modalities to the unique characteristics of virtual reality experiences as identified by Fegely and Cherner (2021, Journal of Information Technology Education: Research ) and Lee and Cherner (2015, Journal of Information Technology Education: Research ). The findings reveal numerous opportunities to leverage virtual reality's distinct features, depending on the subject matter, the virtual teaching environment, and the pedagogical strategies employed. The research and analytical grid promise to make a valuable contribution to the fields of museum education and museology because they provide a structured means to scrutinise and evaluate the educational possibilities inherent in virtual reality experiences while also deepening our understanding of the intricate dynamics between users and museum artworks as well as objects within immersive environments. [Correction added on 8 October 2025, after first online publication: Abstract has been updated in this version].
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".