Mathematics and Coding in Teacher Education
Bibliographic record
Abstract
Coding has been introduced into K through 12 curricula worldwide, yet most teachers lack firsthand experience with it, either as students or in their adult lives. For teachers to support student learning with coding, they must first gain experience and develop effective teaching approaches. Teacher education programs can play a role in this. This study investigates how preservice teachers’ perspectives on coding and teaching and learning mathematics evolved as a result of: (a) course experiences (activities, assignments, discussions, and reflections); and (b) mathematics and coding teaching experiences. The study was conducted over six years, with six cohorts of preservice teachers enrolled in a 36-hour course called “Computational Modelling in Mathematics and Science Education” in a Bachelor of Education program at an Ontario university. The 53 participants selected for this study opted to teach a mathematics and coding lesson as part of a final assignment in the course. The study employed a constructivist theoretical framework, qualitative research methods, and reflexive thematic analysis. Four preservice turning points were identified, which involved shifts in perspectives about teaching and learning mathematics and coding. These turning points were analyzed using the lenses of (a) Seymour Papert’s theory of constructionism, and (b) Yasmin Kafai and Quinn Burke’s computational participation framework. By translating the findings as recommendations for practice, the study presents a model for teacher education that provides insights for researchers and teacher educators.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.035 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".