Computational Thinking in Mathematics Education Across Five Nations
Bibliographic record
Abstract
Computational Thinking (CT) has been declared as the basic literacy of the 21st century, as well as reading literacy and numeracy literacy. Awareness about the importance of ICT has been responded very well by various countries by including CT in the school curriculum. This study aimed to analyze the comparison of the integration of CT in mathematics education in 5 countries, namely China, Singapore, United Kingdom (UK), Canada and the United States of America (USA). This study used a systematic literature review method that was carried out with the PRISMA protocol. This study started from identifying the process, assessing, and interpreting all available research evidence. The design used is to summarize, review, and analyse 43 articles in the Scopus database that are very relevant to the research object. The results of the study found that, most research on the integration of CT in mathematics education had been conducted in the USA. The type of research conducted in China was dominated by quantitative research while in Canada and it tended to be qualitative. In China, UK, Canada and USA, most of the research was carried out at the Elementary School level, while in Singapore was carried out at the Junior High School level. This result implies valuable insights for policymakers and educators regarding effective strategies for integrating CT in mathematics learning.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".