Critical Social Pedagogy in Mathematics Teacher Education
Bibliographic record
Abstract
There is no how-to recipe for implementing pedagogical approaches, as each school, learner and teacher has a unique perception of the nature of critical mathematics education. It is therefore the duty of educators and school administrators to cultivate critical teaching and learning experiences that can connect the standardised school curriculum to the reality of learners’ everyday lives. As such, this study investigated the pedagogical approaches which mathematics teacher educators employed in the development of democratic citizens in South African universities. Underpinned by the constructivist paradigm, the study employed a qualitative research approach and a case study design. Data were generated from a total of six mathematics teacher educators and 75 second- to fourth-year student teachers majoring in mathematics education across three different universities. The findings from the study revealed that there are contradictions between pedagogical philosophies and the mathematics teacher educators’ ideal image of their practice in the classroom. The nature of mathematics teaching, and the fear that learners come to class with different knowledges from their personal experience and have disparate opinions, hinder critical and social engagement within mathematics education classrooms. Based on the findings, it is recommended that mathematics teacher educators employ a problem-posing pedagogical approach which allows for the appropriation of knowledge in the form of self-reflection, a synergy of care, and self-determination which seeks to foster democratic values and critical consciousness in the development of democratic citizens.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.011 | 0.047 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".