Doing Research in a Mathematics Education Course: One Experience, Two Points of View
Bibliographic record
Abstract
In initial teacher training, is there a bridge between theory and practice and, more importantly, does this bridge hold up when students find themselves in the labour market? For several years, this question has been at the heart of the work of authors who have studied the integration of research in initial training and more precisely in initial teacher training. At the Universite de Moncton , undergraduate students at the faculty of education take one research course during their studies. However, that course alone doesn’t seem to be enough to get students to really incorporate research both in their other courses and in their practicum. Therefore, in order to integrate research in initial teacher training, some changes were made to the mathematics education course. Our goal was both to have students develop a positive attitude towards research and to carry out their own research. In order to do that, the work that they had to do was organized to achieve all the learning outcomes through the implementation of a didactic engineering. Such an approach not only allowed students to make connections between theory and practice by working with pupils in the school system, but also to publish an article in which they shared their experience with other teachers.
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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 | 0.044 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.030 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.006 | 0.019 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".