Marshmallows, Fun, and Constellations: A Mixed-Methods Evaluation of a STEAM Astronomy Workshop
Bibliographic record
Abstract
This study evaluates a STEAM-based astronomy workshop for children, delivered at two science engagement events in Malta: Science in the City 2023 and Unconventional Science Careers Days 2023. The workshop integrated storytelling, mythological narratives, creative making activities, and digital tools within the 5E instructional model and the creative pedagogy CREATIONS, with constellations as the central theme. Using a mixed-methods approach, we collected survey data from 122 participants (aged M=10, SD=2.4) and practitioner observations. Quantitative analysis showed that most children (80.3%) found the workshop easy to understand, though 91.8% reported not learning new content. Despite this, 51.6% expressed strong interest in learning more about astronomy, and 55.7% wanted similar school workshops. Significant differences emerged by setting: open-air festival participants reported higher levels of enjoyment and clarity than classroom-based participants. Qualitative analysis revealed children emphasized astronomy knowledge, enjoyment, and creative processes, often linking learning to personal contexts such as zodiac signs. Practitioner observations highlighted parental involvement as both supportive and potentially intrusive. These findings suggest STEAM workshops emphasising artistic processes, can stimulate curiosity, engagement, and cultural relevance in astronomy education, while underscoring the importance of facilitator training and careful scaffolding to balance creativity with conceptual accuracy. The study contributes to research on non-formal STEAM learning by demonstrating the potential and challenges of integrating arts, storytelling, and science in astronomy education.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.043 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".