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.
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.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".