The Mechanisms App: Electron-Pushing Formalism as a Software System
Bibliographic record
Abstract
The arrows depicting electron movement and the bond-making and breaking events are the maps that guide student instruction in organic chemistry curricula. For students, the pathways represented by electron pushing formalism (EPF) can be tough to navigate. For instructors, providing formative feedback to students to support their learning of the EPF arrow system is difficult to provide in a timely manner. The Mechanisms app (“Mechanisms”) was developed as a method for students to explore the electron movement of organic chemistry through a touch screen interface of a smart phone or tablet and do so within a game-like experience. In this paper the pedagogical content of the Mechanisms app (“Mechanisms”) is described along with studies of students’ use of the app to understand whether the open-ended experience to construct understanding of EPF is valuable as a formative assessment method. Presented in this paper are the results of Mechanisms use by analysis of a multi-institution anonymous student survey, with a usability study of organic chemistry students, and with three case studies detailing the use of the app in college classrooms.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.009 |
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".