Factors Affecting the Implementation of Extended Reality Technologies to Support Technical Education in Two-Year Colleges
Bibliographic record
Abstract
Advancements in computer technology have revolutionized extended reality (XR) experiences, including augmented reality (AR), virtual reality (VR), mixed reality (MR), and 360° photography and videography. These technologies have found widespread adoption in various educational contexts, from K-12 schools to universities. However, community and technical colleges in the United States have been slower to adopt these innovative instructional modalities. This study was conducted in two phases to investigate factors influencing the adoption of XR technologies at two-year institutions. In the first phase, Advanced Technician Education (ATE) program participants were surveyed (n = 44) on barriers to adoption of XR at two-year institutions. In the second phase, participants from two-year colleges (n = 18) were interviewed guided by the Consolidated Framework for Implementation Research (CFIR) to identify their perceptions and the challenges faced in implementing XR-enabled instruction. Most survey respondents (20.5%) reported a lack of XR knowledge as a reason for not integrating XR into their curricula, followed by the cost of XR hardware and content (10.3%). Lack of knowledge about XR was rated as a “moderate” barrier and hardware and content costs were both rated as “significant” barriers for XR implementation. The qualitative findings identified enhanced visualization, experiential learning, high student engagement, and institutional support for technology implementation as facilitators to XR adoption. In contrast, limited availability of XR educational content, restricted development opportunities of XR content, integration challenges of XR technologies with existing learning management systems, resource constraints, and training needs of educators were reported as hindering the implementation of XR technologies at two-year colleges.
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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 | 0.007 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".