Using teacher-generated tags of classroom situations to elicit mentor and pre-service teachers' practical knowledge: Title Part of the symposium 'International Perspectives on Mentoring in Practicum Settings'
Bibliographic record
Abstract
In this AERA Division K symposium, 18 researchers from 7 different countries (China, New Zealand, France, Australia, Netherlands, Spain, and Canada) are brought together. The symposium provides the opportunity to engage and interact with international research efforts focussing on 'practicum pedagogies,' and in particular, mentoring in practicum settings. You will learn about the similarities and differences that motivate and challenge teacher educators from across the world for whom the principal concern is the quality of the field experience for both the student teachers and their practicum mentors.\nAs one of the contributions, the tagging study has a twofold objective. First, elicitation of mentor and pre-service teachers' conceptualizations of videotaped classroom situations to clarify similarities and differences between practical knowledge of experienced and novice teachers. Second, exploration of 'collaborative tagging' as a new method to access mentors and pre-service teachers' practical 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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".