Defining Computational Thinking as an Evident Tool in Problem-Solving: Comparative Research on Chinese and Canadian Mathematics Textbooks
Bibliographic record
Abstract
Purpose To analyze mathematics problem-solving (PS) procedures in Chinese (CH) and Canadian (CA) elementary mathematics textbooks that leverage computational thinking (CT) as a cognitive tool, which have evidently existed and been implemented. Design/Approach/Methods In this study, an analysis framework was developed to investigate the characteristics of CT tools for three PS steps—understand the problem, devise and conduct plans, and look back into textbooks—in four contexts: data practices, modeling and simulation practices, computational tools practices, and systemic thinking practices. Findings Our results demonstrate the tools (CT) employed in the PS process in CH and CA mathematics textbooks. The strong connections between the “look back” stage and CT tools were explored. During the “look back” stage, both countries required students to transfer their knowledge and perform generalization. In addition, CT is regarded as a basic skill analysis for students in mathematics education and has received significant attention at every stage of the PS process. Originality/Value This study brings a new perspective to CT research in education by regarding CT as a cognitive tool for students in mathematics PS.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".