Exploring climate-friendly cities: a case study of elementary students’ systems thinking
Bibliographic record
Abstract
This qualitative case study explores how elementary students’ systems thinking emerges and evolves through engagement in climate change-focused design tasks. Despite growing interest in systems thinking within science education, research at the elementary level, especially concerning complex socioscientific issues like climate change, remains limited. Guided by the Components – Mechanisms – Phenomena (CMP) framework, we analyzed interviews, student artefacts, and field notes collected over 12 weeks involving Grade 6 students in Western Canada. Findings reveal that students effectively identified key system components (e.g. vegetation, oceans, atmosphere, renewable energy sources) and recognised emergent phenomena such as global warming and climate-friendly urban design. However, they often struggled to articulate mechanisms of how these components dynamically interact to produce broader outcomes. It was evident that design tasks and structured scaffolds, such as CMP-informed prompts, provided immediate opportunities for component identification and phenomenon recognition, while perturbation activities prompted dynamic reasoning about system interdependencies and trade-offs between efficiency and equity. Findings also supported that students’ systems thinking skills were multifaceted and context-dependent rather than strictly hierarchical. The study contributes conceptually (reframing development as context-sensitive), methodologically (CMP as scaffold and analytic lens), and practically (a classroom-ready toolkit, including CMP-informed prompts and design-plus-perturbation tasks).
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".