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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".