In Search of Ways to Improve Practicum Learning: Self-Study of the Teacher Educator/Researcher as Responsive Listener
Bibliographic record
Abstract
Teacher education programs that appear to be more successful work to thread practicum experiences and on-campus courses with an eye to achieving overall program coherence. As part of a funded research project centred on understanding how teacher candidates perceive quality in their practicum experiences and, by extension, in their professional learning, focus groups were recruited for a series of discussions that extended over an academic year. I undertook this self-study in an attempt to examine the conditions for learning that made these focus groups so successful by virtue of participants’ commitment, engagement, focus and drive to become the best teachers they could possibly be. Self-study was an avenue for me to develop insights into my practice and to identify ways to move forward to become a more effective teacher educator who could model and scaffold responsive listening and relationship-building for future teachers. The two questions driving this self-study were “How does adopting and promoting a listening perspective improve participants learning?” And “What is transformative about responsive listening?” Identifying and challenging my assumptions were initial steps in understanding what a listening perspective entails, the importance of authorizing student perspectives and developing their pedagogical voices. Responsive listening became a means to interrogate my practice, to reframe my experience, to work in and from action, and to become more comfortable with the uncertain spaces where deep learning can occur – for myself and for those whom I teach. In so doing, I came closer to appreciating the possibilities for transformation.
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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 | 0.055 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.016 | 0.020 |
| Scholarly communication | 0.021 | 0.011 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.006 | 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".