More Students Working Together with Less Rote Learning: Fostering Academic Success in the Sciences with Peer-Led Study Groups for High-Risk Courses
Bibliographic record
Abstract
The University of Guelph’s award-winning Supported Learning Groups (SLG) Program offers students weekly, collaborative, out-of-class review sessions for challenging courses on our campus, including numerous 1st and 2nd year science courses. Based on a well established model of co-curricular academic support used around the world known as Supplemental Instruction (SI), the SLG Program encourages learner-centredness, enhances student engagement, and helps retain students. Come hear about our collaborative approach in which professional staff, instructors, and some of the brightest and most engaged upper-year undergraduates work together to build student competence and confidence in identified courses. These student Peer Helpers are trained to guide students through activities designed to get students working together to review the course content and come to understand it themselves, as well as develop successful study habits and prepare for midterms and finals. The effective use of group facilitation and collaborative learning strategies are our bread and butter – we will interactively involve participants as we describe how the SLG Program engages students with the course content they must master, with more active learning and less rote memorization, and helps them develop transferable learning skills.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".