Comparing Teacher Educator and Novice Teachers’ Frames in Making Sense of Feedback During Rehearsals
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this study, we compared the frames that a teacher educator (TE) and preservice novice teachers (NTs) within a math teaching course used to interpret feedback that the TE provided during rehearsals.We conducted video-elicited interviews with the TE and two NTs in which they watched excerpts of TE feedback from a rehearsal video to understand: a) how the TE and the NTs described the problems of practice (PoP) that the TE feedback focused on and b) the frames the TE and the NTs used to interpret those PoP.We present two major differences in the types of frames that participants drew upon to describe the PoP.Our results highlight the importance of considering the frames that NTs may bring to rehearsals.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it