When preservice and inservice teachers join forces: A collaborative way to support the enactment of new coding curricula in mathematics classrooms
Bibliographic record
Abstract
The importance of computational thinking skills in mathematics has been recognized in educational research for a long time. More recently, this recognition has materialized in formal international recommendations (e.g., by PISA’s 2022 Mathematics Framework) and in national or provincial curricular reforms (e.g., in France, Sweden, and Canada) that promote the incorporation of coding in mathematics classrooms. This has led to opportunities as well as challenges for mathematics teachers, and a pressing need for work on teacher training. To contribute to this emerging area, we report on a professional development experience in which 25 inservice teachers collaborated with 36 preservice teachers to plan, implement, and reflect on the implementation of coding-based mathematics activities (using Scratch or Python) with Gr. 5–9 school students. Teachers’ reflections are shared as insights gained through the experience, which may be of interest to other teachers or policy makers engaged in the implementation of coding in school subjects such as mathematics. With other researchers and teacher educators in mind, participating teachers’ reflections are also used as a springboard to evaluate the reported training approach, discuss the approach in the context of existing literature, and provide some perspectives for the future.
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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 | 0.024 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".