Exploring elementary students’ learning of astronomy through constructing visual representations
Bibliographic record
Abstract
This study explores how the construction and refinement of visual representations support elementary students’ learning of astronomical concepts, particularly in topics that are challenging to teach and understand. Drawing on qualitative data—including student interviews, classroom recordings, student-produced visual representations, and field notes—from a Canadian Grade 5/6 classroom, the findings show that representational practices serve both cognitive and social functions. On the one hand, visual representations act as generative tools for students to reason, interpret, and make sense of complex phenomena. On the other hand, they function as epistemic resources for communicating scientific claims in public settings. Students demonstrated the ability to coordinate and synthesize information from various sources—such as online research, reading materials, and peer feedback—into coherent representations, even negotiating between scientific accuracy and audience engagement. The study highlights the potential of visual representation tasks to foster active, inquiry-based learning in astronomy, allowing students to integrate knowledge, communicate effectively, and take ownership of their learning. Educational implications are discussed, suggesting that task design is crucial in leveraging visual representations to support both individual reasoning and collective knowledge-building in science education.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".