Advancing Extended Reality Teaching and Learning Opportunities Across the Disciplines in Higher Education
Bibliographic record
Abstract
The emergence of Extended Reality (XR) technologies in diverse fields, along with research demonstrating opportunities for innovation in teaching and learning, warrants further exploration. However, investigation into the supports required by instructors for experimentation, design, and deployment of XR technologies is needed. This study explores the benefits and challenges of XR technologies for teaching and learning, and reviews XR pedagogical initiatives at three universities. The research aims to discern strategies to promote and support XR initiatives in higher education, develop best practices for using and integrating XR technologies into university courses, and guide future research into XR technologies in higher education. To develop an empirical understanding of the opportunities and challenges faced by instructors using XR technologies, we performed a thematic analysis of sixteen semi-structured interviews to capture insights from instructors’ experiences. We found that instructors were cognizant of the many benefits associated with XR use in terms of experiential learning, making learning content more accessible, and the potential for its application. However, the findings also highlight the lack of guidance and support available to instructors in the implementation of these technologies in educational contexts. Our results provide recommendations for future institutional supports that could advance XR and expand the current teaching and learning opportunities available to instructors and students.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".