Video-Stimulated Recall as a Facilitator of a Pre-Service Teacher’s Reflection on Teaching and Post-Teaching Supervision Discussion—A Case Study from Finland
Bibliographic record
Abstract
The use of video in learning to teach is not new. The vast body of research shows that both pre-service and in-service teachers benefit from analyzing video lessons conducted by experienced teachers, their peers, or themselves. In this narrative case study, we analyze one post-teaching supervision discussion about a mathematics lesson. The study provides an insight into a unique setting where teaching practice took place, i.e. one teacher training school in Finland. We aim to demonstrate one pre-service teacher’s learning process in the post-teaching discussion supported by the recursive use of video-stimulated recall (VSR). VSR was used first, as a tool for encouraging reflection on the lesson during the supervision discussion, after which the pre-service teacher was interviewed while watching a video of the supervision discussion. We argue that the recursive reflection on different kinds of videos may help pre-service teachers better learn from their own teaching experiences and from the advice of the experienced supervising teacher. In addition, arguably, the recursive use of VSR may be a fruitful method for educational researchers studying teacher education.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".