The language of organic chemistry: is fluency the key to success?
Bibliographic record
Abstract
In chemistry, like other sciences, we rely on symbols and other representations to communicate abstract, small, and dynamic phenomena. These representations become part of that discipline’s language; the electron-pushing formalism (EPF) is a central example in organic chemistry. Students can spend a large amount of time decoding chemistry’s symbolic language, increasing their working memory load. We believe this increased cognitive load limits their abilities to learn new concepts and engage in scientific reasoning. We hypothesized that greater fluency would reduce cognitive load, thus freeing cognitive resources for more advanced scientific reasoning. Our previous work has demonstrated that students can quickly gain fluency using a learning module dedicated to that purpose (OrgMech101). In this study, we explored how EPF fluency relates to cognitive load and reasoning ability, in a sample of second-year organic chemistry student participants (N = 36). Using an experimental design, participants in two groups completed identical pre- and post-tests, with different learning phases: group 1 (treatment) focused on EPF skills, while group 2 (control) focused on acid–base concepts. We assessed EPF fluency and reasoning ability while measuring cognitive load using eye-tracking technology to capture changes in participants’ average and maximum pupil diameters. Eye-tracking data were analysed using a custom-made Python script, and participants’ reasoning ability was analysed by categorizing their arguments based on complexity. The findings revealed a significant relationship between EPF fluency and cognitive load (ρ(31) = 0.322, p = 0.039), regardless of the assigned group—the higher the fluency, the lower the participant’s cognitive load. The treatment group participants also had significantly decreased cognitive load from the pre- to the post-test (Z = 2.098, p = 0.036). However, there was no significant difference in cognitive load between the groups when accounting for their initial cognitive load levels. Participants in the treatment group exhibited greater increases in reasoning ability compared to the control group, which may be due to more available cognitive resources upon becoming more fluent with the EPF (lower cognitive load), as seen by the increase in their post-test scores. The findings highlight the importance of helping students become fluent with respect to representations to facilitate effective information processing. Reducing cognitive load can be achieved by ensuring students develop a foundational understanding of the EPF and incorporating causal mechanistic reasoning questions to encourage chemical reasoning and improve organic chemistry 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 | 0.024 |
| 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 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".