Sharing our journey as teacher educators: The challenges and joys of teaching with cases
Bibliographic record
Abstract
Two teacher educators reflect on their 20 years of using dilemma cases in their teacher education courses on inclusive education. In so doing, they plumb their critical friendship and work to answer three questions that drove their study: (1) What have we learned about ourselves and each other as teachers? Why does this matter? (2) What have we learned about teaching with cases and about the importance of sharing these experiences with a colleague who is on the same path? (3) What have we learned that can help to meet the challenges of a major program re-design just as one collaborator retires? Qualitative data analysis yielded four emergent themes: self-doubt, uncertainty, identity as a teacher educator, and why we teach with cases. Teaching with dilemma cases proved invigorating but also unsettling, as vulnerabilities were exposed to enable teacher candidates to adopt a critical stance and to recognize that inclusive teaching is a vulnerable undertaking.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.039 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.043 | 0.050 |
| Scholarly communication | 0.029 | 0.029 |
| Open science | 0.005 | 0.027 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 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".