Improving the Quality of Practicum Learning: Self-Study of a Faculty Member’s Role in Practicum Supervision
Bibliographic record
Abstract
This self-study of activities as a practicum supervisor in several secondary schools focuses on the supervisor’s contribution to the quality of a teacher candidate’s professional learning in the context of a familiar tension between on-campus courses and in-school practicum experiences. Data from both formal and informal supervisory experiences are taken from notes recorded in classes with those supervised formally, records of notes taken in practicum observations, and email messages exchanged with teacher candidates about their supervision experiences. Analysis of formal supervision experiences generated a series of insights into candidates’ experiences of the practicum, a modest innovation in supervisory practice, and important reminders arising from a significant error in personal practice as a supervisor. Informal supervision experiences involved visits arising from invitations to observe extended by candidates in the author’s physics methods course. These experiences generated opportunities to test an alternative supervisory strategy focused on candidates’ perceptions of their learning from experience. Related literature includes the topics of learning from experience, reflective practice, and the potential significance of an epistemology of practice. In closing, attention is given to trustworthiness, the personal significance of the study and connections to the teacher education literature.
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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 | 0.019 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".