Integrate coding into Ontario elementary mathematics teaching and learning: A curriculum analysis
Bibliographic record
Abstract
Background: In a digital society, coding has emerged as a critical skill, essential for equipping citizens with the competencies needed for the 21st century (Tuomi et al., 2018). Recognizing its value, scholars have promoted the integration of coding into education to foster students’ 21st century skills (Gretter & Yadav, 2016). In Canada, coding has also been regarded as an important skill (Francis et al., 2017). Much work has been done to promote coding integration in K–12 education, especially in Ontario, where coding has been integrated into the elementary mathematics education curriculum (Ontario, 2022; Ministry of Education of Ontario, 2020). Therefore, this research aims to investigate (1) how Ontario ministry of education conceptualized coding within their curricula, and (2) how Ontario Math Support integrated coding into their lesson plans.Conceptual Framework: In this study, I created a conceptual framework, named Coding Content Knowledge for Mathematics Teaching and Learning (CCKMTL) model. This model combined curriculum analysis (Posner, 2004), mathematics content knowledge (Ball et al., 2008), and a pedagogical framework for integrating computational thinking (Kotsopoulos et al., 2017), offering a multifaceted lens through which to investigate coding integration into the Ontario elementary mathematics curricula.Methodology: This research employed a qualitative study methodology. Data were collected from Ontario curriculum documents and Ontario Math Support’s lesson plans.Results: The findings revealed that Ontario’s educational framework systematically incorporated coding at various educational stages, which enabled students to apply coding to mathematical challenges as well as other transdisciplinary problems (e.g., music, finance).Implications: The study has practical and theoretical implications. Practically, it provides valuable reference for multiple communities, including in-service, pre-service teachers, school administrators, policymakers, and educational researchers on how to integrate coding into mathematics teaching and learning. Theoretically, this study has expanded the computational thinking model developed by Kotsopoulos et al. (2017) by identifying related themes and subthemes. It potentially broadens its scope and applicability within the context of elementary mathematics. Therefore, future research plans to use the CCKMTL framework developed in this study to investigate the design of curriculum policies that have effectively integrated coding into various educational programs, such as mathematics, science, music, etc
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".