Computational Thinking Workshop: A New Way to Learn and Teach Mathematics
Bibliographic record
Abstract
In this digital era, technology has entered every aspect of our life, including educational system. Computational thinking (CT) and programming are a relatively recent part of certain school curricula. The idea of CT was originated in 1950s, and the first usage of the term CT was by Papert in 1980; the notion/concept was refreshed by Wing in 2006. CT is the focus of attention for many researchers, such as Gadanidis , Namukasa,  Kotsopoulos, Curzon, diSessa, Farris,  Sengupta and so on ; they argued that using CT tools, ideas and activities in mathematics pedagogies and curricula contributes to learning in creative and imaginative ways. In this paper, the ways that students interact with their peers during CT and mathematical thinking activities are investigated in the context of an instrumental case study of 10 elementary students. Observational, interview, and reflection data collected during two workshops were analyzed to determine the ways in which the activities impacted students’ interacting and understanding. Students engaged in three CT activities: symmetry app, Scratch program, and Sphero robot. As a result, CT activities allow students to learn mathematical concepts better, when they are working with CT ideas and activities. This study was limited in its sampling as it only focused on primary grades 3 - 6 in a private school. For future studies, the researchers suggest conducting a study that will include public schools and involve tools for teaching mathematics concepts.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.039 | 0.015 |
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".