Bibliographic record
Abstract
A great deal of research has shown that lectures with large class sizes struggle to promote active learning resulting in poor knowledge acquisition and retention as well as limited conceptual understanding. Based on the benefits observed for blending learning models and small group learning in the literature, Introductory Psychology (Psyc 100) at Queen’s has recently undergone a massive redesign with the goal of improving the student experience.The structure of Psyc 100 has been changed from 3 hours of traditional lecture a week to 1 hour of lecture, 1 hour of online learning, and 1 hour of learning lab per week. The goal of this redesign is to increase student engagement through learning labs, grant more freedom to pursue the course material via interactive online tasks, and delve deeper into exciting and relevant topics with more in-depth lectures.The labs are specially designed with a student-centered approach that helps learners to engage with fellow students and the material through group discussions, quizzes, games, and debates. Upper year students majoring in Psychology comprise approximately 2/3 of the tutorial facilitators for these labs, which provide undergraduate students with an important opportunity to take a more active role in the Psychology department and develop a love for teaching.We will present the research behind this redesign, demonstrate how it has been incorporated into the new Psyc 100 curriculum, and share our experiences as student facilitators through the ongoing refinement of the course.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".