The student hat in professional development: Building epistemic empathy to support teacher learning
Bibliographic record
Abstract
Abstract Professional development (PD) can support science teachers to learn about instructional reforms, but more work needs to be done on broadening our understanding of how specific PD activities support teacher learning. One understudied PD activity is the student hat: when teachers engage in student learning activities while considering ideas, language, and feelings students might have to build their empathy for how student experience reform instruction. Little is known about if and how student hat activities support teacher learning. I conducted a single embedded case study of a 2.5‐day PD for middle school science teachers using the OpenSciEd curriculum. I interviewed 12 participants to understand how they perceived the student hat activities and analyzed 36 hours of PD video, focusing specifically on moments in which participants struggled to act in student hat, to gain insights on how it helped them to learn. Teachers found student hat difficult, but it helped them better understand science ideas, their students, and the instructional approach. These learning outcomes likely occurred because of two mechanisms: creating a safe environment to be wrong and building epistemic empathy with students. By allowing teachers to feel safe expressing confusion with content ideas, the student hat helped teachers to build their science understanding. Developing teachers' epistemic empathy for students helped them to understand how students might think and feel while engaging in reform instruction.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".