Teacher Candidates and Math Content Knowledge: An Institution’s Response to Promote Proficiency
Bibliographic record
Abstract
This mixed methods study explored the math content knowledge (MCK) of teacher candidates (TCs) in an elementary education program over a four-year period. Prior to commencing the faculty of education program, 972 TCs participated in a numeracy assessment during their orientation. Test items were aligned to provincial curriculum expectations involving basic numeration skills, primarily at the grade 6 level. Overall findings indicated that approximately one third of TCs scored below the minimum standard of 70%. Interviews with TCs who scored lower than 70% revealed the following themes: 1) avoidance of math, 2) belief in ‘math people’, and 3) desire to improve. Our research identifies the need to offer equitable approaches to support the development of TCs in ways that do not make them feel stigmatized due to past experiences with math. The outcomes of this research have led to the development of a compulsory math content course for all elementary TCs called “MathPlus” that focuses on MCK (grades 6-9). MathPlus is meant to become an integral part of the teacher education program, whereby MCK development is the normative culture in which all TCs feel supported as math learners.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".