Developing as Culturally Responsive Mathematics Teacher Educators: Reviewing and Framing Perspectives in the Research
Bibliographic record
Abstract
Culturally responsive pedagogies (CRP) are widely accepted as a critical component of teaching in ways that value and incorporate children's diverse cultural and community knowledge resources (Gay, 2010; Ladson-Billings, 1995a, 1995b).Ladson-Billings (2014) describes the need for a fluid and dynamic understanding of culture, as well as a fluidity to one's scholarship on CRP, suggesting that -if we ever get to a place of complete certainty and assuredness about our practice, we will stop growing‖ (p.77).She describes common hazards of becoming stuck in limited understandings of culture, or ignoring the socio political dimensions.As such, growing one's CRP is important ongoing work for mathematics teacher educators (MTEs).Growing our own CRP practices as mathematics teacher educators has been our goal for some time now (Keazer & Nolan, 2021;Nolan & Keazer, 2019, 2021).In the context of teaching mathematics education courses, we are committed to reflect on our efforts to enact a pedagogy that is responsive to the culture(s) and knowledge(s) of our students (i.e., practicing and prospective teachers).Our efforts respond to Averill et al.'s (2009) challenge for educators to -critically reflect on their own culturally responsive practices, ideally in discussion with other practitioners, teacher educators, and students‖ (p.181).To date, no specific tool has been proposed for supporting and guiding the professional growth of culturally responsive (mathematics) teacher educators.Thus, this review shares our process and outcomes of identifying, synthesizing, and analysing a collection of key scholarly texts in the field of teacher educator culturally responsive pedagogy, that provide framing around: a) how CRP has been defined (that is, what does CRP mean?) and b) how CRP has been described through the naming of dimensions, components, characteristics, or questions (that is, what does CRP look like?). BACKGROUND AND CONTEXTResearch on CRP in mathematics teacher education has primarily focused on the CRP of prospective and practicing teachers (PTs) (e.g., Willey & Drake, 2013) and/or the mathematics curriculum (e.g., Aguirre & Zavala, 2013), rather than that of MTEs.However, we support Han et al.'s (2014) claim that an essential element of teacher educators' efforts to support the development of PTs' CRP is to examine and model their own CRP.
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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 | 0.046 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.001 | 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".