An online categorization task to investigate changes in students' interpretations of organic chemistry reactions
Bibliographic record
Abstract
Abstract In this study, we investigated how students organized their knowledge about organic chemistry reactions in a transformed curriculum, including their choices, abilities, and changes over time. This transformed curriculum focuses on interpreting the underlying mechanistic patterns of chemical reactions and emphasizes the principles of reactivity in organic chemistry. Data from this study were collected at beginning and end of an Organic Chemistry II course using an open and closed online categorization task with a set of organic chemistry reactions. In the open task, participants organized the set of reactions as they chose, giving us insight into how the participants preferred to organize their knowledge. In the closed task, participants were asked to organize the set of reactions in a specific way—by each reaction's governing mechanism—which would provide a measure of the students' ability to categorize the reactions in that way. We investigated the similarities and differences of the open and closed categorizations at each time of administration and analyzed the changes over time. Findings from this study emphasized the efficacy of the transformed curriculum for: (a) promoting a focus on process‐oriented features of reactions over static features of a reaction and (b) increasing the students' abilities to categorize a set of reactions according to the mechanism governing the reaction. Findings revealed implications for the transformed curriculum, which addresses key areas for improvements, potential implications for research, and also limitations of the current study. We further describe possible extensions of this study to how the open and closed categorization tasks may be used for research and instruction in other science, technology, engineering, and math disciplines.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".