Method for Integrating Components of a CURE into an Introductory Biology Traditional Laboratory <sup />
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.
Bibliographic record
Abstract
\nTraditional laboratories with simple experiments and a lack of inquiry do not always lead to learning gains, but Course-based Undergraduate Experience (CURE) has increased student engagement, understanding of the research process, and confidence in research skills. Although CUREs offer multiple benefits, they also come with greater financial, logistic, and time-commitment challenges relative to more traditionally structured laboratories, and they can also narrow the scope of the laboratory sessions. We propose a framework that integrates some components of a CURE into a traditionally structured laboratory. Specifically, the laboratory curriculum is set up for students to complete weekly laboratory activities, reinforcing the concepts introduced in lecture, as well as a semester-long research project within the guidelines of an experiment or observational study that can be completed, at least in part, outside of the laboratory. In this framework, students have abundant freedom to drive their research projects to follow their own interests. Additionally, instructor equipment costs and time commitments necessary from the instructor are low. This laboratory framework is best designed for faculty who would like to implement a CURE but lack the necessary resources and/or place a high value on weekly laboratory activities to reinforce concepts introduced in the lecture portion of the course. Survey data indicated that integrating some elements of a CURE without transforming a course into a full CURE resulted in students benefitting from many of the same gains, such as better understanding the realities of the research process and enjoying conducting their research projects.\n
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it