What's the Matter?: Educators' Understandings of Teacher Content Knowledge in Primary Mathematics
Bibliographic record
Abstract
A significant number of Ontario teachers lack a background in mathematics; how this lack impacts student learning in Ontario is unclear. Prior studies indicate that high teacher self-efficacy developed through mathematics pedagogical content knowledge (MPCK) improves student learning. Therefore, this thesis examines how primary school math teachers and administrators conceptualize the value of mathematical content knowledge for teaching. Towards this goal, I have used a case-study model, interviewing three teachers and a principal from one Ontario school, along with an instructor in initial teacher math education. From the existing literature on MPCK, I assessed how participants were aware of their MPCK and the ways they attempted to improve their math teaching. A qualitative method was used to acquire contextualized data that is often absent from large scale psychological and survey-based studies. The findings indicate that teachers' understandings of the value of content knowledge in math are linked to their fundamental conceptualizations of the role of a teacher. However, the participants’ understandings of the nature of math and the nature of teaching were inconsistent. This inconsistency points to a systemic misunderstanding of the role of a teacher. Moving forward, results from teachers' perspectives provide a valuable addition to a framework for larger scale MPCK research in the Canadian context.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.018 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".