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Record W4401120398 · doi:10.1080/0047231x.2024.2381418

Reworking the Recipe: Adding Inquiry and Reflection to College Science Labs

2024· article· en· W4401120398 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of College Science Teaching · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methods
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsContext (archaeology)Agency (philosophy)Reflection (computer programming)Mathematics educationMetaphorLearning sciencesRecipeScience educationPedagogyPsychologyExperiential learningComputer scienceSociologyChemistry

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.091
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Scholarly communication
Consensus categoriesMetaresearch, Science and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.688
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0910.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.008
Science and technology studies0.0080.003
Scholarly communication0.0010.003
Open science0.0020.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.090
GPT teacher head0.471
Teacher spread0.381 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it