School and Community Practices of Computational Thinking in Mathematics Education through Diverse Perspectives
Bibliographic record
Abstract
In the 21st century, computational thinking (CT) has emerged as a fundamental skill. Building on this momentum and recognizing the importance of exploring the use of computational thinking (CT) concepts and tools in teaching and learning, this study conducted a qualitative content analysis to investigate online resources for school and community outreach practices related to integrating CT and coding into mathematics education. The data set was selected from sample websites hosting a community of practice and interpreted through Kafai et al.’s (2020) framings of CT and a combination of three theories of learning and teaching (i.e., constructionism, social constructivism, and critical literacy). The study found that in mathematics, more attention is given to the cognitive approach of CT, which focuses on acquiring CT skills and concepts, rather than the situated approach that emphasizes participation during learning. Additionally, there is not enough emphasis on the critical framing of CT, which examines how learning reflects values and power structures. The study’s significance is grounded in enhancing the perspectives of researchers, educators, and policymakers by providing insights into the wide affordances of CT which meet and exceed the expectations of curriculum content and skills. In light of the recent attention paid to adding coding to the new mathematics curriculum, this study contributes to the literature, practice, and curriculum development on the integration of CT into school mathematics and serves as a basis for future research in the field.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.010 | 0.022 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".