Method for Integrating Components of a CURE into an Introductory Biology Traditional Laboratory <sup />
Bibliographic record
Abstract
Undergraduate biology instructors face the challenge of balancing critical thinking procedures with biology content (1). Evidence has revealed that directed “cookbook” laboratory exercises alone severely limit opportunities to interpret data and practice higher-order thinking (2, 3). However, concurrently learning complex biological concepts and practicing higher-order thinking, such as inquiry laboratory activities, is thought to enhance comprehension of biological concepts (4, 5). Course-based undergraduate research experiences, or CUREs, have become a popular method of instruction because they provide access to research experience for all students (6–8). However, financial barriers, increased time investment, lack of institutional support, and the narrow scope of topics and laboratory skills gained in CUREs relative to traditional laboratory activities can present challenges for laboratory instructors who desire to provide a robust curriculum (5, 9). Ideally, laboratory curricula include learning outcomes for students to gain a diversity of laboratory skills, reinforce biology concepts, practice higher-order thinking, and develop an understanding of the research process.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.011 |
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".