[WP4] D4.1 PAX Guideline for Developers
Bibliographic record
Abstract
This deliverable presents the first draft of the PAX Guidelines aimed at supporting developers in creating didactically meaningful extended reality (XR) applications for educational purposes. Developed as part of the PAX Project, the guidelines combine insights from pedagogical theory, XR development practices, and collaborative research across academia and industry. The structured framework consists of four steps - capture, plan, realize, and apply - guiding developers through the process of integrating XR technology into educational contexts effectively. Drawing on foundational elements such as FH Münster’s EduAR Workbook and Enversed’s XR Canvas, the PAX Guidelines also incorporate findings from interdisciplinary workshops, surveys, and analyses conducted within the project’s work packages. These findings highlight the need for a practical, user-friendly resource for developers and underscore the challenges of aligning pedagogical principles with technical design.This document outlines the methodology behind the PAX Guidelines, details their conceptual structure, and discusses the collaborative processes involved in their creation. While the first draft remains conceptual, analogue to the PAX Framework for teachers (WP3), the next steps involve translating it into an interactive website prototype, testing it in real-world development scenarios, and refining it based on feedback. The guidelines aim to bridge the gap between education and technology, providing a tool that not only supports developers but also fosters the meaningful use of XR in classrooms. This deliverable lays the groundwork for future testing, iteration, anddissemination, with the ultimate goal of promoting innovation in educational XR applications.
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 | 0.020 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.050 | 0.048 |
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".