Reworking the Recipe: Adding Inquiry and Reflection to College Science Labs
Bibliographic record
Abstract
Cookbook-style laboratories (labs), where students follow recipes and confirm known results are common, yet years of science teaching and learning research indicate they do not help college students develop the habits of mind and skills of a scientist. We describe the rationale, challenges, and initial changes made in our teaching and learning lab reform project in this article. The metaphor of cooking and following recipes aims to help readers visualize the fundamental changes we are making to college exercise physiology labs. Connections between changes to existing inquiry-based learning literature, four distinct levels of inquiry in science learning, student agency, and student reflection are made. Changes to teaching practices and supporting the instructional team in trying new strategies which support reformed lab learning activities is also described in this article. We invite readers to reflect on the changes we are making, and perhaps imagine ways they could translate our early reforms to their own college science lab teaching and learning context.
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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 | 0.017 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".