Students’ interpretations of mechanistic language in organic chemistry before learning reactions
Bibliographic record
Abstract
Research on mechanistic thinking in organic chemistry has shown that students attribute little meaning to the electron-pushing ( i.e. , curved arrow) formalism. At the University of Ottawa, a new curriculum has been developed in which students are taught the electron-pushing formalism prior to instruction on specific reactions—this formalism is part of organic chemistry's language. Students then learn reactions according to the pattern of their governing mechanism and in order of increasing complexity. If students are fluent in organic chemistry's language, they should have lower cognitive load demands when learning new reactions, and be better positioned to connect the three levels of chemistry's triplet ( i.e. , Johnstone's triangle). We developed a qualitative research protocol to explore how students use and interpret the mechanistic language. Twenty-nine first-semester organic chemistry students were interviewed, in which they were asked to (1) explain a mechanism, given all the starting materials, intermediates, products, and electron-pushing arrows, (2) draw in arrows for a reaction mechanism, given the starting materials and products of each step, and (3) predict the product of a reaction step, given the starting materials and electron-pushing arrows for that step. To investigate the students’ ideas about mechanistic language rather than their knowledge of specific reactions, we selected reactions for the interview guide that had not yet been taught. Following transcription, we analyzed the interviews using constant comparative analysis to explore how students used and interpreted the mechanistic language. Four categories of student thinking emerged with electron movement underlying students’ thinking throughout the interviews. Herein, we discuss these categories, students’ interpretation of the symbolism, connections to learning theory, and implications for teaching, learning, and research.
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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 | 0.016 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.024 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| 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".