Working with mental models to learn and visualize a new reaction mechanism
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Creating and using models are essential skills in chemistry. Novices and experts alike rely on conceptual models to build their own personal mental models for predicting and explaining molecular processes. There is evidence that chemistry students lack rich mental models of the molecular level; their mental models of reaction mechanisms have often been described as static and not process-oriented. Our goal in this study was to characterize the various mental models students may have when learning a new reaction mechanism and to explore how they use them in different situations. We explored the characteristics of first year organic chemistry students’ ( N = 7) mental models of epoxide-opening reaction mechanisms by qualitative analysis of transcripts and written answers following an audio-recorded interview discussion. We discovered that individual learners relied on a combination of both static (with a focus on symbolism and patterns) and dynamic (reactivity as process or as particles in motion) working mental models, and that different working mental models were used depending on the task. Static working mental models were typically used to reason generally about the reaction mechanism and products that the participants provided. Dynamic working mental models were commonly used when participants were prompted to describe how they pictured the reaction happening, and in attempting to describe the structure of a transition state. Implications for research, teaching, and learning from these findings are described herein.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it