Why doesn't it add up? A narrative inquiry into teachers' experiences with math
Bibliographic record
Abstract
The success rates in mathematics education in Canada has been the cause of concern and some controversy.Although Canada has recently scored relatively high on the mathematics section on Programme for International Student Assessment (PISA), a disproportionately low number of Canadian students go on to enroll in math programs in post-secondary education.The aim of this study was to try to determine whether or not there is a relationship between the experiences elementary math teachers had with math when they were students, and the way they teach math in their classrooms.In order to achieve this overall objective, the following research questions framed this study: (1) how does a teacher's personal experience with math (as a student/in life) contribute to his/her instructional practices, and (2) what training/education/experiences lead to a successful math teacher?Six elementary, non-specialist math teachers were interviewed, and through a narrative inquiry approach, were encouraged to share their personal stories about their experiences with mathematics as learners and teachers.The themes that emerged from the interviews include: (1) many elementary teachers had negative experiences with math as students, (2) many elementary teachers received ineffective teacher training, (3) elementary teachers often carry those negative experiences into their practice, and (4) change will need to come in the form of fun and understanding.Based on its findings, this study suggests the following considerations: (1) preservice elementary teachers complete math competencies as a prerequisite for their teacher training programs, (2) teacher training programs offer math curriculum courses with opportunities to increase pedagogical content knowledge, (3) school districts offer professional development opportunities in math that include local and recognized math "experts" to work with small groups of teachers over a prolonged period of time.v Running Head: WHY DOESN'T IT ADD UP? Chapter Four.Research Findings………….…………………………..…………………………49 A. Introduction…………………………………………………………………………..49 B. Study Findings……………………………………………………………………….49 C. Research Question 1…………………………………………………………………50 1. Theme 1………………………………………………………………………….50 2. Theme 2………………………………………………………………………….52 3. Theme 3………………………………………………………………………….54
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.015 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".