Straddling Teacher Candidates’ Two Worlds to Link Practice and Theory: A Self-Study of Successful and Unsuccessful Efforts
Bibliographic record
Abstract
This self-study examines a teacher educator’s quest to provide an integrated practice-and-theory approach to learning to teach that places the experiences of the teacher candidates at the centre of the curriculum by focussing on their developing questions, challenges and issues through socio-constructivist discussions of their emerging practices. Constraints of university structures with regards to offering such an approach are discussed, including the impact that offering seminars on the university campus rather than in schools has on teacher candidates’ ability to integrate theories and practices of learning to teach. The research questions guiding this study include: How can I understand what process candidates actually undergo during practicum in the development of their ability as teachers vis-à-vis my course?, and How can I re-structure and re-organize my course to enhance the development of candidates’ practice as teachers? Data include interviews with volunteer student participants before and after observations made during classroom visits as well as discussions with groups of students. Analysis is based on reading and rereading the data as well as discussing it with the larger research team. The findings include the realization of how little of my course is integrated into my teacher candidates’ teaching, how much greater the influence of their mentor teacher is, and the extent to which teacher candidates need explicit help and guidance to transform or reframe their teaching. The article concludes with specific suggestions for adjusting a seminar associated with the practicum to enhance the development of a practice-and-theory approach to teaching.
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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 | 0.024 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.022 | 0.023 |
| Scholarly communication | 0.022 | 0.008 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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".