Factors Affecting the Implementation of Extended Reality Technologies to Support Technical Education in Two-Year Colleges
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it