Effect of Video-Cases on the Acquisition of Situated Knowledge of Teachers
Bibliographic record
Abstract
Video footage is frequently used at teacher education. According to Sherin and Dyer (2017), this is often done in a way that contradicts recent studies. According to them, video is suitable for observing and interpreting interactions in the classroom. This contributes to their situated knowledge, which allows expert teachers to act intuitively, immediately and effectively. Situated knowledge is used to give form (design patterns) and direction (educational purposes) to a teacher’s actions. Design patterns consist of solutions for recurring problems. In the current research, we investigated whether a course in classroom management either with or without video cases contributes more to the development of situated knowledge, design patterns and educational purposes. The pre- and posttest are based on a written advice, given out by 41 students of the Dutch hbo-teacher training with an average age of 22, to the main character of a video case, in addition to an interview and observation report. The results indicate that the use of video cases does not lead to an increase in the number of educational purposes. There is an increase, however, in the design pattern ‘classroom management’. By internalizing this design pattern, the divide between theory, practical experiences and the identity of the teacher is bridged. Although the classroom management theme dominated the video case and course, the results indicate that a targeted use of video cases in teacher education is effective in promoting the development of situated knowledge.
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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 | 0.004 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".