The language of organic chemistry: is fluency the key to success?
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».