Reframing Coding as “Mathematization” in the K–12 Classroom: Views from Teacher Professional Learning
Bibliographic record
Abstract
There is now a growing body of literature that argues for the use of computational programming and modelling in K–12 science classrooms. However, one of the common pedagogical challenges of using computational modelling in the classroom is the overhead of learning programming, which interrupts curricular flow because it requires specialized technical knowledge. In this article, our goal will be to illustrate a pathway for integrating computational modelling and programming in the science classroom for teachers with little or no background in programming. Drawing upon our findings from an ongoing series of design-based professional learning sessions with 56 teachers in K–12 public and charter schools in Alberta organized by the Galileo Educational Network, we will argue that (a) when teachers, with little or no background in programming, view programming as a way to “mathematize” the world, they can visualize and implement seamless integration of programming and modelling with their science curricula; and (b) the use of multiple and complementary forms of programming and modelling (e.g., physical, virtual and embodied) can facilitate such integration.
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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 | 0.017 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.055 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.005 | 0.010 |
| 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".