Essential learning outcomes for delocalization (resonance) concepts: How are they taught, practiced, and assessed in organic chemistry?
Bibliographic record
Abstract
The concept of delocalization ( i.e. , resonance) is fundamental concept in organic chemistry but essential learning outcomes (LOs) have not previously been proposed nor has there been an analysis of how resonance is taught, despite indications in the literature that students have many non-canonical ideas about the concepts. To address this deficit, we first developed a set of ten learning outcomes believed to be essential to the concept of delocalization in organic chemistry, especially for students’ later success. Next, we analyzed how these learning outcomes are being taught, practiced and assessed in common textbooks and in a sample of exams. Five themes emerged from the analysis: (1) several of the essential intended LOs we identified are not represented in the textbooks’ teaching explanations, practice questions, or professors’ assessments; (2) the concepts related to delocalization are often taught, practiced, and assessed without associated justifications; (3) there is a large gap between when delocalization is taught and when it is used in context; (4) the link between delocalization and other concepts ( e.g. , reactivity) is not explicitly explained in most teaching materials; and (5) the language used around delocalization may be misleading ( e.g. , resonance, stability). Our analysis identified areas in which delocalization education could be improved, including with respect to teaching, practice opportunities, and assessing the concepts.
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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 | 0.017 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".