Conceptualising the expertise of the mathematics teacher educator
Bibliographic record
Abstract
The aim for this working group is to further the development of research within the domain of the mathematics teacher educator (MTE), specifically to move beyond descriptions of MTE knowledge towards ways of conceptualising and researching the expertise of the MTE. The phenomenon of expertise of the MTE is not easily defined. We follow Beswick & Goos (2018) by using the label MTE as “anyone engaged in the education or development of teachers of mathematics” (p. 418) and recognise that MTE, as a role, encompasses a diverse set of practices within mathematics teacher education. Recently, within mathematics teacher education, there has been increasing interest in the development of theories that can account for what and how MTEs learn; for example, the publication of a recent special issue of the Journal of Mathematics Teacher Education where some scholars have extended existing models of mathematics teacher knowledge as a way of describing the knowledge of the MTE (e.g., Leikin, Zazkis & Meller, 2018). This working group builds on foundations from previous PME working sessions that have been centred around MTEs (Goos, Chapman, Brown, & Novotna, 2011; Beswick, Goos, & Chapman, 2014) and looks to extend existing conceptualisations of the expertise of MTEs beyond descriptions of MTE knowledge (e.g., Appova & Taylor, 2017).
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.030 |
| Scholarly communication | 0.012 | 0.031 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".