Dialogic pedagogy in graduate teacher education research advisement: A narrative account of three teacher educators
Bibliographic record
Abstract
Research methods courses often tend to focus on transferring technical information to students rather than offer a more dialogical approach to learning (Barraket, 2005; Kilburn et al., 2014). By drawing on the concept of self-study (Bullough & Pinnegar, 2001), through personal journals and retrospective reflections, this paper explores learning activities introduced in three teacher education graduate research methods courses to support student learning beyond the mastering of research skills or techniques. Narratives of three teacher educators illustrate how teacher candidates can dialogically reflect on research-related topics with peers, bring questions forward for discussion in class and online, apply their emerging technical research skills through collective analysis of a situation, and grow collective knowledge. Teacher candidates recognize the importance of research in their work, although their passion for conducting research is influenced by varied constraints, including research design, programmatic and personal limitations.
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How this classification was reachedexpand
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".