Children’s Visualization and Collaboration in a STEM Makerspace: Opportunities for Fostering Sustainability Awareness
Bibliographic record
Abstract
With growing concerns about climate change and environmental degradation, students’ understanding of sustainability and climate change has become an increasingly prominent topic in science curricula. This has increased the need for learning experiences that meaningfully address these topics in the classroom. In this study, we investigated how grade 6 students explored sustainability concepts through visualization, specifically by creating 3D models of a sustainable place in a STEM makerspace classroom. By analyzing students’ visualization and their collaborative problem solving, we examined how students conceptualize and navigate diverse perspectives related to sustainability. Our findings indicate that visualization supported students’ epistemic agency and engagement with sustainability concepts. In their 3D models, students highlighted the importance of greenery, renewable energy, local food sources, low-emission transportation, and waste management. They displayed diverse problem-solving approaches when encountering differing ideas in group work. and the visualization activities provided opportunities for students to reason, understand, and negotiate ideas on sustainability and climate change.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".