Cross-Platform Conversion Framework for XR in Technician Education
Bibliographic record
Abstract
The Cross-Platform Conversion Framework for XR in Technician Education presents a structured, replicable process for adapting immersive learning content across multiple platforms and devices. Developed through the Resource Collaborative for Immersive Technologies (RECITE), an NSF Advanced Technological Education (ATE) project, this framework addresses one of the most persistent challenges in extended reality (XR): ensuring accessibility, interoperability, and sustainability of educational content across heterogeneous XR ecosystems. The framework introduces a four-layer software architecture: Content Abstraction, Interaction Logic, Platform Translation, and Deployment Packaging, supported by a five-stage development process that integrates pedagogical design with technical implementation. All development and testing were conducted in Unity to maintain WebGL compatibility and ensure browser-based accessibility. To make the workflow actionable, RECITE developed an accompanying online XR Conversion Tool that guides users through each stage of the process, generating tailored conversion guides for developers, educators, and instructional designers. This white paper provides both conceptual and practical guidance for implementing cross-platform XR experiences in technician education. It is published under a Creative Commons Attribution-ShareAlike 4.0 International license (CC BY-SA 4.0) and aligns with Web Content Accessibility Guidelines (WCAG) 2.1 and Section 508 compliance standards.
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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".