Bibliographic record
Abstract
This study critically examined the math content knowledge (MCK) of teacher candidates (TCs) enrolled in a two-year Master of Teaching (MT) degree. Teachers require a solid math knowledge base in order to support students’ achievement. Provincial and international math assessments have been of major concern in Ontario, Canada, due to declining scores. Research aimed to investigate the development of TCs’ math capacities for effective teaching is important to teachers, school districts, universities, professional learning associations, and policy makers. The researchers of this study analyzed the basic numeracy skills of 151 TCs through pre- and post-tests. In addition, eight TCs took part in semi-structured interviews and shared their experiences in the MT math program. Test results indicated improvements in many areas, however, not all numeracy skills improved significantly. Interviews revealed TCs’ perceptions of the math test, courses, and instructors, as well as the importance of teaching math during their practicum placements. The researchers made recommendations to teacher education programs in areas such as: establishing minimum math competency standards, enhancing coherence between MT math courses and practicum placements, and providing additional support for TCs with low math proficiency.
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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 | 0.015 |
| 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 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".