Ten Essential Delocalization Learning Outcomes: How Well Are They Achieved?
Bibliographic record
Abstract
Delocalization (resonance) is a concept in organic chemistry that influences the chemical reactivity, structure, and physical properties of molecules. However, the concept has proven challenging for students and the related learning outcomes had previously been only vaguely defined. We recently defined ten essential learning outcomes about delocalization that a student should be able to demonstrate by the end of a two-course organic chemistry sequence. The goal of the present study was to investigate to what extent the ten LOs were achieved by students, as well as the connections between the LOs. We analyzed three exam questions related to seven of the ten LOs for the degree of achievement, common errors, and scientific reasoning. We found that students sometimes struggled to identify when delocalization could occur, that some of the LOs built on one another, and that students were more successful in drawing resonance structures when explicitly asked, but less successful when the requirement was implicit or embedded within a mechanism. Our analysis of student reasoning showed that the dominant modes of reasoning were aligned with the related expectations and explanations in the course. When asked to justify the contribution of resonance structures to the resonance hybrid, most answers used a descriptive mode of reasoning; when asked to explain why a given proton was more acidic than another, most answers contained relational and linear causal reasoning. Implications for research and practice are discussed.
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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 | 0.020 | 0.096 |
| 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 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".