Lowering the activation barrier to create a constructively aligned undergraduate chemistry laboratory experience – a review of innovations in assessments and course design
Bibliographic record
Abstract
Reimagining laboratory education in chemistry can help address demands to revitalize the undergraduate chemistry curriculum. In doing so, we can help students think like scientists and connect chemistry to other disciplines. Historically, undergraduate laboratories were taught through expository experiments coupled with traditional lab reports. However, these practices do not allow for constructive alignment of the curriculum, because the assessments target the cognitive domain of learning while the learning outcomes and class activities target the psychomotor domain. This lack of alignment also limits meaningful learning in the laboratory, at the heart of the cognitive, affective, and psychomotor domains. This review summarises some recent innovations in course design and assessments for undergraduate level laboratory courses. Overall, we aspire to lower the activation energy barrier for educators to find and implement curricular reforms in laboratory education that are constructively aligned within their course. We structure this review under the major learning outcomes of laboratory instruction, defined by Reid and Shah: (1) linking cognitive and psychomotor domains; (2) developing practical skills; (3) designing experiments; and (4) improving transferable skills, which are further separated into scientific writing, oral communication, and peer learning.
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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 | 0.024 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".