Acid–Base Learning Outcomes for Students in an Introductory Organic Chemistry Course
Bibliographic record
Abstract
An outcome-based approach to teaching and learning focuses on what the student demonstrably knows and can do after instruction, rather than on what the instructor teaches. This outcome-focused approach can then guide the alignment of teaching strategies, learning activities, and assessment. In organic chemistry, mastery of organic acid–base knowledge and skills are particularly essential for success. For example, Brønsted acid–base knowledge and skills are required in greater than 85% of the more complex organic and biochemical reactions we analyzed in this study. Despite the importance of mastering acid–base concepts and skills, the literature describes many related student difficulties. We identified essential learning outcomes (LOs) in organic acid–base chemistry by (1) analyzing more complex organic reactions to identify the acid–base-related skills and knowledge that students would need to successfully analyze those reactions and (2) analyzing textbooks’ explanations and coverage of acid–base chemistry. We constructed the learning outcomes using the Structure of Observed Learning Outcomes (SOLO) and modified Bloom taxonomies, as well as SMART (specific, measurable, achievable, relevant, and time-bounded) goal-setting principles. We explicitly aligned our courses’ learning activities and assessments with those intended learning outcomes, both in the initial introduction of acid–base chemistry and as we analyze more complex reactions. To clearly communicate these LOs to students and other educators, we described them in an educational graphic.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".