USING COMPUTATIONAL TOOLS TO ENHANCE LEARNING IN AN UNDERGRADUATE MOLECULAR SPECTROSCOPY COURSE
Bibliographic record
Abstract
In molecular spectroscopy, our models of the molecular world are built on rigorous spectroscopic experimentation and rich interplay between theory and experiment. To instill such appreciation to undergraduate students, who have little experience in either spectroscopic experiments and theory, is challenging. We have developed a new computational laboratory component to complement the material covered in a senior undergraduate course on molecular spectroscopy. Specifically, we focus on illustrating molecular spectroscopic concepts (some of which can be quite abstract and complicated) taught in class with electronic structure calculations. This talk will describe our implementation and the learning outcome. Two particular examples will be discussed. One is related to the misconception that electron density is the main factor responsible for NMR chemical shifts and how we utilize both experimental data and calculations to help students overcome this common misconception. The other deals with differences in geometries, for example, those obtained using rotational constants directly, isotopic substitution procedures, and electronic structure calculations. This talk will also discuss how the above activities worked in practice and the improvements we plan to implement next time.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".