Textbook Treatments of Electrostatic Potential Maps in General and Organic Chemistry
Bibliographic record
Abstract
Electrostatic potential maps (EPMs) allow for representation of key molecular-level information in a relatively simple and inexpensive format. As these visualizations become more prevalent in instruction, it is important to determine how students are exposed to them and supported in their use. A systematic review of current general and organic chemistry textbooks ( N = 45) determined how frequently EPMs were presented in texts, how well distributed EPMs were across chapters, whether EPMs were included in end-of-chapter problems, and the types of conceptual instructional support provided to students when first exposed to them. Analysis demonstrated great variance in the use of EPMs. Most, but not all, textbooks presented at least one image, yet the prevalence and integration across texts varied greatly, owing in part to content differences between general and organic texts. Many texts provided minimal conceptual support and did not include EPMs in end-of-chapter problem sets. Overall, little consensus emerged as to how often EPMs should be used, and the sorts of instructional supports or student practice offered to scaffold the use of EPMs. These findings suggest a need for examining the supports that foster effective comprehension and use of EPMs, and more generally, obtaining data that inform the design and implementation of emerging instructional supports.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".