Working with mental models to learn and visualize a new reaction mechanism
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".