Learning about teaching through research and vice versa: Towards developing methods in graduate coursework
Bibliographic record
Abstract
The research on methods used in graduate mathematics education courses is limited, however, existing groundwork suggests that curriculum should provide students with experiences that align with the practices of mathematics education researchers. At the same time, calls to bridge mathematics education research and classroom practice have been clearly articulated both within and outside the literature on the preparation of mathematics education researchers. This study describes a process that we call learning about teaching through research and vice versa (LTR). Specifically, the process involves graduate students doing a mathematical task, reading a research paper about the same mathematical task, and finally completing an assignment that was based on viewing video data from a school classroom where the same task was enacted. Phenomenography was used to analyse written survey data and report that graduate students experienced the process as teachers, researchers and teacher-researchers. The results indicate that the implemented methodology 1) offered students an opportunity to experience practices similar to those mathematics education researchers engage in while pursuing scholarly inquiries, and 2) provided a setting where students learned about teaching and mathematics education research. Finally, the results support the claim that the LTR process acts as an example where research and practice enhanced one another and thus bridged the perceived gap between research and practice.
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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 | 0.142 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.003 | 0.007 |
| 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".