Supervisory Conversations: A Key to Reflective Experimentation
Bibliographic record
Abstract
Using a qualitative approach, in this article, the author explores the conversations which take place between preservice teachers and their university supervisor, analysing sequences which serve as support for experimentation of a ‘new’ teaching approach and situations of knowledge construction by preservice teachers. They were asked to use cooperative learning activities during student teaching although such strategies were not necessarily modelled by their cooperating teachers or familiar to the students. As their researcher/supervisor, the author provided support in planning conferences and coaching through post-observation conferences. It is suggested that there is more to supervisory conversations than simply providing moral support for the preservice teachers or evaluation of their performance. They are occasions for knowledge construction, notably, through problem-solving and solution finding, stimulation of reflection and discussion of theory.
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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 | 0.146 | 0.245 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.053 |
| Scholarly communication | 0.018 | 0.026 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".