Secondary School Mathematics Teacher Candidates' Research Pedagogical and Content Knowledge
Bibliographic record
Abstract
University-based initial teacher education aims at instilling in teacher candidates the idea of the interconnectedness of content, pedagogical and educational research knowledge by allowing meaningful interaction between teacher candidates and teacher educators. The theory-practice divide is presented in the literature as barrier to achieving this goal. This mixed methods research study re-conceptualizes the theory-practice divide from a problem into an opportunity. Secondary school teacher candidates can use contradictions and tensions, surrounding the theory-practice divide, for synthesizing diverse perspectives on content, pedagogical and educational research knowledge. They can integrate this perspective in their practice teaching. The study examined secondary school teacher candidates’ perspectives on the interaction of their content, pedagogical and educational research knowledge in practice teaching as well as factors contributing to these perspectives. The study found that participants’ different perspectives on their research pedagogical and content knowledge (RPACK) were associated with the different levels of their reform-mindedness in mathematics education as measured by a survey. The low, medium and high reform minded participants placed as the first priority pedagogical knowledge, content knowledge and educational research knowledge, respectively.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".