Mechanisms before Reactions: A Mechanistic Approach to the Organic Chemistry Curriculum Based on Patterns of Electron Flow
Bibliographic record
Abstract
A significant redesign of the introductory organic chemistry curriculum at the authors’ institution is described. There are two aspects that differ greatly from a typical functional group approach. First, organic reaction mechanisms and the electron-pushing formalism are taught before students have learned a single reaction. The conservation of electrons, atoms, and formal charges, how the use of curved arrows helps describe the mechanism, and how to predict reaction mechanisms are emphasized. Second, the reactions taught in the first two semesters of organic chemistry are arranged by their governing mechanism, rather than by functional group. The reactions are taught in order of increasing difficulty, beginning with acid–base reactions, followed by simple additions to π electrophiles, and ending the first semester with addition to π nucleophiles, including aromatic chemistry. The reactions in the second organic semester begin with elimination reactions, then substitutions, and finally more complex π nucleophile mechanisms (e.g., aldol reaction) and π electrophile reactions (e.g., acetals). Ultimately, the goal is for students to learn and interpret reactions based on their patterns of reactivity, allowing them to analyze, predict, and explain new reactions. In principle, a mechanistic method is more general, easier to understand, and provides a better way to achieve a deep understanding of chemical reactivity. Chemical reactions follow patterns, and these patterns can allow a chemist to predict how a chemical will behave, even if they have never seen a particular reaction before. Visualizing reactivity as a collection of patterns in electron movement is a more powerful and systematic way to approach learning in organic chemistry. It still requires some memorization, but because the course organization is directly linked to reaction patterns, deeper learning in the discipline is possible.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.031 | 0.013 |
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".