How virtual reality, augmented reality and mixed reality facilitate teacher education: A systematic review
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
Abstract Background Virtual reality (VR), augmented reality (AR) and mixed reality (MR) have sparked recently in improving the effectiveness of teacher education. However, there is a lack of review regarding the utilisation of these technologies in this field. These three technologies, namely VR, AR and MR, can be collectively referred to as extended reality (XR) (as mentioned in reference Tang et al., 2022). Objectives Remarkably, the utilisation of XR‐based technologies in teacher education needs to be explored. Moreover, research questions related to the training objectives, methodological features, and the effects of XR‐based teacher education remain unanswered. Methods To this end, the present study conducted a systematic review to analyse 52 articles from six databases (including Web of Science, Scopus, IEEE Xplore, ERIC, ScienceDirect, and ACM Digital Library). Results The results indicate that XR technologies have been primarily used to train teachers' procedural knowledge, for instance, classroom management. Furthermore, most studies have primarily focused on pre‐service teachers (PSTs) rather than in‐service teachers and utilised small sample sizes, with VR emerging as the most frequently employed tool. Finally, the majority of the studies reported that XR‐based training affected teachers positively. Conclusions It urges researchers and developers to consider theory‐driven training design, which increases the potential to better understand what features of XR promote in‐service teachers' and PSTs' learning and how they do so. This article additionally conducts a SWOT (strengths, weaknesses, opportunities, and threats) analysis of XR‐based teacher education to offer more insightful recommendations and foster further discussion.
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.
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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