Building mental models of a reaction mechanism: the influence of static and animated representations, prior knowledge, and spatial ability
Bibliographic record
Abstract
In chemistry, novices and experts use mental models to simulate and reason about sub-microscopic processes. Animations are thus important tools for learning in chemistry to convey reaction dynamics and molecular motion. While there are many animations available and studies showing the benefit of learning from animations, there are also limitations to their design and effectiveness. Moreover, there are few experimental studies into learning chemistry from animations, especially organic reaction mechanisms. We conducted a mixed-methods study into how students learn and develop mental models of a reaction mechanism from animations. The study ( N = 45) used a pre-/post-test experimental design and counterbalanced static and animated computerized learning activities (15 min each), plus short think-aloud interviews for some participants ( n = 20). We developed the tests and learning activities in a pilot study; these contained versions of an epoxide opening reaction mechanism either as static (using the electron-pushing formalism) or animated representations. Participants’ test accuracy, response times, and self-reported confidence were analyzed quantitatively ( α = 0.05) and we found that, while participants showed a learning effect, there were no significant differences between the static and animated learning conditions. Participants’ spatial abilities were correlated to their test accuracy and influenced their learning gains for both conditions. Qualitative framework analysis of think-aloud interviews revealed changes in participants’ reasoning about the test questions, moving toward using rule- and case-based reasoning over model-based reasoning. This analysis also revealed that dynamic and transitional features were incorporated into participants’ working mental models of the reaction mechanism after learning from animations. The divergence of participants’ mental models for reasoning and visualization could suggest a gap in their mental model consolidation.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".