Development and evaluation of a Lewis acid–base tutorial for use in postsecondary organic chemistry courses
Bibliographic record
Abstract
A well-developed understanding of the Lewis acid–base model is highly important for the understanding of organic chemistry. As such, students should receive instruction and be assessed on use of the model. Online tutorials and constructed-response items provide a means for confirming that students have a well-developed conceptualization of the Lewis acid–base model. In a prior study, a predictive logistic regression model was presented that can be used with constructed-response assessment items to determine use of a Lewis acid–base model in written responses. In this study, we use that predictive model to evaluate the effectiveness of a tutorial designed to promote meaningful understanding of the Lewis acid–base model in three different instructional contexts: first-semester organic chemistry students before summative assessment, first-semester organic chemistry students after summative assessment, and second-semester organic chemistry students. Additionally, we evaluated the learning gains of one set of first-semester students after a 3-week time delay. McNemar’s test results suggest that the tutorial had a net positive impact in all three instructional contexts, with the most significant impact observed with the second-semester students. This work has implications for further development of literature-based tutorials to promote meaningful understanding of organic chemistry reaction mechanisms assessed by constructed-response items.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".