Chemistry simulations and embodied cognition: Exploring design, model generation, and collaboration
Bibliographic record
Abstract
The GEM cycle is a teaching approach with the objective of fostering understanding of unobservable phenomena in science, such as electricity, cellular respiration, or molecular structures. Simulations have enabled science students to experience and support the development of mental models. Traditionally, computer simulations have been used to support individual learning and inquiry, the cognitive advantages of paired and small group collaboration is becoming more apparent. Embodied cognition is a theoretical approach of relevance to learning contexts which defines cognition as embodied within one’s interaction with their environment. We have identified several objectives for enriched understanding in the research field of science education and student dyad collaboration. Our overall goal is to further understand how students develop conceptual understanding in the context of these interactions using a novel chemistry simulation designed by our research team. We have developed a number of research questions that study student collaboration and the possible emergence of shared understanding and mental model construction. Our preliminary research is designed to empirically evaluate our questions and facilitate the design of educational simulations for collaboration. Author Keywords Simulations, embodied cognition, Discourse, common
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".