Bibliographic record
Abstract
Extended reality (XR), which encompasses virtual reality (VR), augmented reality (AR), and mixed reality (MR), offers powerful affordances for improving teaching and learning experiences in a post-pandemic world. Increasingly, many governments and institutions around the world are making major investments in XR technologies to prepare education systems for the future. However, many of these investments remain isolated pilot projects which, while they attest to the potential of XR in education, are unlikely to be scaled up due to lack of sustainability and collaboration. Based on literature and empirical evidence, I have identified major barriers to the wider adoption of XR in education, including the lack of (a) open content, tools, and skills; (b) sound pedagogy and instructional design; and (c) scalability and sustainability. As a potential solution, I introduce the Open XR for Education Framework (OXREF), an empirical framework that proposes a holistic solution to XR object creation, implementation, and deployment, while covering pedagogical, technological, and policy perspectives. The contribution of the OXREF is its ability to build fit-for-purpose XR experiences in a scalable, sustainable, and collaborative manner while promoting openness, accessibility, equity, and reuse. The novelty of the proposed framework is its use of open educational resources (OER), open educational practices (OEP), as well as free and open-source software (FOSS) tools and platforms. Its cloud-based infrastructure and open licenses support viable operationalization strategies that can be implemented by educational institutions and governments.
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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.117 | 0.053 |
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".