Immersive Learning Through 360° Classroom Videos: Enhancing Teacher Education and Professional Development
Bibliographic record
Abstract
Conventional classroom videos have long been recognized as valuable tools for teacher training, enabling educators to analyze pedagogy, classroom management, and assessment strategies. However, recent advancements in 360° video recording and virtual reality (VR) technologies present exciting opportunities to further enhance teacher preparation by providing immersive, interactive experiences of classroom lessons. Accordingly, this study explores the use of 360° classroom videos in Lesson Study to enhance teacher education through immersive technologies. A modified four-stage model was implemented: (1) a 360° video lesson was recorded; (2) participants observed the lesson using VR headsets and reflected in real-time; (3) group discussions produced annotated videos with instructional insights; and (4) educators reviewed annotations to refine teaching practices. Using qualitative methods, data were collected from 48 VR think-aloud sessions, 16 interviews, and 2 group discussions. Key findings show that participants found the immersive experience highly engaging, promoting reflection on pedagogy, classroom setup, and student behaviour. Group dialogue supported collaborative learning and consensus-building, although various technical challenges were also noted. As such, this research highlights the potential of immersive video to support critical reflection and pedagogical development, while also addressing the need for infrastructure, accessibility, and digital fluency to fully integrate these tools into teacher training programs.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".