Teachers’ Emotions during Video-based Video-based Teacher Professional Development
Bibliographic record
Abstract
These files contain the anonymized data used to create the tables and figures found in "Exploring Teachers’ Emotions via Nonverbal Behavior during Video-based Teacher Professional Development". Please refer to the abstract for the paper below: Increasing research on teacher professional development (TPD) has found teachers’ self-reflection to be key for improving teaching effectiveness. Although video methodology, as often used in TPD, provides crucial insight concerning situated learning, teachers are often reticent to participate in TPD protocols due to discomfort over being videotaped. This longitudinal study explored emotion-related behaviors by assessing the nonverbal expressions exhibited by teachers during a 1-year video-based TPD program highlighting salient contributors to productive classroom dialogue. Six teachers were observed regarding bodily motion, facial expression, and eye contact, with results obtained across four workshops coded according to five types of emotions. The emotions of shame, defensiveness, and distraction appeared more often than did laughter and surprise, with the negative emotions found to decrease over time. This study highlights the importance of longitudinally evaluating teachers’ emotional expressions during video-based TPD activities and continued efforts to encourage teacher participation in these pedagogical training opportunities.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.017 |
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".