Responding to the Challenges Posed by Summative Teacher Candidate Evaluation: A collaborative self-study of practicum supervision by faculty
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 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.
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.
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 | 0.004 | 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 | 0.001 | 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