Evaluating students’ learning gains, strategies, and errors using OrgChem101's module: organic mechanisms—mastering the arrows
Bibliographic record
Abstract
We developed an online learning module called “Organic Mechanisms: Mastering the Arrows” to help students learn part of organic chemistry's language—the electron-pushing formalism. The module guides students to learn and practice the electron-pushing formalism using a combination of interactive videos, questions with instant feedback, and metacognitive skill-building opportunities. This module is part of OrgChem101.com, an open educational resource (OER) that houses a series of learning modules. To evaluate the mechanism module's effects on students’ learning and experiences, we offered a workshop during which undergraduate students used the module. We investigated their learning gains via a pre-test and post-test format and their experiences using a survey. Analysis of responses revealed significant learning gains between the pre- and post-test, especially with questions that asked students to draw the products of a reaction. After using the learning tool, students used more analysis strategies, such as mapping, attempted more questions, and made fewer errors. The students reported positive experiences and a belief that the module would help them in their organic chemistry courses. Previous work also identified greater metacognitive skills after using the module, related to the module's intended learning outcomes. Herein, we describe the module, evaluation study, findings, and implications for research and practice.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".