Responding to the Challenges Posed by Summative Teacher Candidate Evaluation: A collaborative self-study of practicum supervision by faculty
Bibliographic record
Abstract
In our pre-service department, university practicum supervisors are faculty members who offer academic, social, and personal support to teacher candidates during their year-long program. Their role is described as one designed primarily to provide formative assessment and feedback to improve classroom practice and reflection on practice. This collaborative self-study describes how two new faculty members responded to the challenges posed by the teacher candidate evaluation process. Methods used included formal tape-recorded discussions during meetings of the self-study group of newly hired faculty, email correspondence, field notes, feedback from public forums about our work, and teacher candidate insights concerning the practicum evaluation process conducted by faculty. New strategies were developed to address the tensions associated with using summative evaluations in a formative framework and to improve practice during faculty practicum supervision.
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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 | 0.116 | 0.274 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.017 | 0.012 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 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".