Patterns of reactions: a card sort task to investigate students’ organization of organic chemistry reactions
Bibliographic record
Abstract
Research has shown that within a traditional organic chemistry curriculum, organic chemistry students struggle to develop deep conceptual understanding of reactions and attribute little meaning to the electron-pushing formalism. At the University of Ottawa, a new curriculum was developed for organic chemistry in which students are taught the language of the electron-pushing formalism prior to learning about specific reactions. Reactions are then organized by governing pattern of mechanism rather than by functional group and are taught in a gradient of complexity. To investigate how students are making connections across reactions within the new curriculum, a card sort task was developed. The card sort task consisted of 25 cards, each depicting the reactants and solvent for a reaction taught during the two-semester organic chemistry sequence. The first part of the task asked participants to sort 15 of 25 cards into categories. Then, participants were given the 10 remaining cards to incorporate into categories with the previous 15. Participants were asked to explain the characteristics of each category and their sorting process. Students ( N = 16) in an organic chemistry course were interviewed while enrolled in the second semester course. We analyzed the students’ sorts based on which cards were sorted frequently together, the underlying characteristics used to form the categories, and the participants’ sorting processes. Participants created categories based on different levels of interpreting the reactions on the cards, with levels ranging from recognizing identical structural features to identifying similar types of mechanisms. Based on this study, if we want students to develop mechanistic thinking, we think students need to be more explicitly directed to the patterns present in organic reaction mechanisms and given opportunities to uncover and identify patterns on their own, during both summative and formative assessments.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".