Primary school students’ problem-solving strategies in creating artworks with GeoGebra
Bibliographic record
Abstract
Computational thinking (CT) as a problem-solving skill has been argued to be an essential skill for all learners. Accordingly, there have been efforts to formalize and operationalize CT within school curricula in various countries. In primary schools, students often develop CT through unplugged activities and visual programming activities. However, in this study, we investigated the use of mathematical software with which students typed in commands (codes) to construct artistic artifacts. Educational Design Research (EDR) has guided the development of our task. We attempted to utilize technology to support students’ problem-solving skills and creativity by developing a GeoGebra-based Math+CT task infusing arts. Fifteen Grade 5 primary school students worked on a task to construct a mandala (Hinduism-Buddhism sacred geometrical figures) involving mathematical concepts. Data, in the form of students’ GeoGebra (i.e., “ggb”) files and screen video recordings, were collected and then analyzed using a content analysis method. Findings revealed that our designed task had promoted students’ different problem-solving strategies while working with technology. Additionally, most students did not encounter serious problems in working with GeoGebra commands, and students’ computational thinking skills were supported as a result of engagement with our activities.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".