Visual skills in the arts and their potential in chemistry education
Bibliographic record
Abstract
The necessity of visuals in chemistry underscores the importance of representational competence in chemistry education, which encompasses the ability to interpret and generate visual representations. Yet, chemistry does not have a tradition of training students directly in the general skills of visual analysis or drawing. The challenges faced by students in understanding complex visuals could be addressed by looking for parallels in other disciplines. In this study, we used the parallel processes framework to explore the shared cognitive skills between chemistry and two arts disciplines: fine arts and art history. By exploring how observation, analysis, modelling, and interpretation function in both fields using an action-research approach, we propose three parallel processes: visual analysis, visuospatial reasoning, and drawing. We then designed activities based on these skills for a focus group with science instructors to provide insight into their potential and feasibility for post-secondary classrooms. We show the need for diverse teaching approaches, particularly in interpreting three-dimensional representations, and the importance of scaffolding visual analysis activities. Overall, this research contributes to the ongoing discourse on representational competencies 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 | 0.003 | 0.008 |
| 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 | 0.009 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".