A Problem-Based Introduction to Machine Learning in the Undergraduate Organic Chemistry Laboratory: Prediction of Diels–Alder Reaction Rates
Bibliographic record
Abstract
To engage students in higher-order thinking skills, an inquiry-based dry laboratory experience was developed for upper-year undergraduate students, where students were introduced to machine learning approaches to solve chemical problems. Students constructed their own data set of diene and dienophile features and performed a multivariate linear regression in a Python environment to predict the energy barriers (Δ G ‡ ) of a Diels–Alder system. They applied their models to a simulated drug development problem. Likert-scale surveys and qualitative interviews were utilized to collect data on student experiences. Students expressed that they felt strongly engaged in critical and creative thinking, collaboration, and metacognition. Subsequently, students felt that computational tools were more approachable, and had a stronger appreciation of how computational tools could be utilized in chemistry contexts. Students also expressed that they felt the freedom to make mistakes, reflect, and improve, embracing a growth mindset in this laboratory.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 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".