"Reading groups" in an undergraduate biology course: A peer-based model to help students develop skills to evaluate primary literature
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
Undergraduates who learn to evaluate primary literature demonstrate an enhanced ability to understand the process of science, weigh scientific evidence, and think critically (e.g. 6,8). Studies show that students who learn how to dissect primary literature through active learning practices, demonstrate mastery of the deeper levels of cognitive processing (9-11). Inspired by that evidence, we designed peer-led active learning sessions called "Reading Groups" (RGs) to supplement in-class learning in an advanced biology course. RGs are moderated by peers who are not content experts. They support novice learners in organizing new information and provide focusing questions to structure discussions. Thus, RGs allow students to discuss research articles in the absence of a content expert, helping them develop the skills and confidence to pose questions during subsequent in-class discussions. This paper describes the design of RGs and reports responses from student surveys. Findings indicate self-reported gains in both evaluating primary literature and self-efficacy. Specifically, students report development of critical thinking, data interpretation, communication skills, and greater confidence in their ability to critique papers. These data are encouraging and we hope that other instructors will consider implementing RGs as a tool to assist students in reading and evaluating primary literature.
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 | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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