Visualising the Development of a Teacher-In-Training into a Beginning Expert
Bibliographic record
Abstract
Teachers use situated knowledge to deal with the complex and diffuse educational contexts they operate in. To be able to take deliberated action, based on the situated knowledge, reflection is necessary during the teacher training. Video cases with common, real world situations are suitable for reflection because of their holistic and diffuse character. Reflection concerns learning experiences with increasing complexity: single-loop (reflecting on a current action), double-loop (reflecting to gain new insights) and triple-loop (reflecting in order to adjust individual identity) learning. The knowledge gained from loop learning is of a situated nature. The current article operationalizes situated knowledge as educational purposes and design patterns; educational purposes determine which course of action (design pattern), is the best option. Using this distinction, we investigate whether the reflection done by fourth-year teachers in training corresponds to what can be expected of a starting expert, namely, reflection on all three levels. The results indicate that three out of four teachers in training can be characterized as starting experts, based on their responses to a video case. They experience learning on all three loop levels, and these experiences contribute to a variety of educational purposes and design patterns. It is the teacher trainers’ challenge to have their students reflects using video cases, so they can use loop-learning to build their situated knowledge. This knowledge will allow them to adequately respond to the complex and diffuse situations in their educational practice.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".