Reflective and Active Learning on Education in Canada Today
Bibliographic record
Abstract
During my SAPP project, “Reflective and Active Learning on Education in Canada Today” with Dr. Patricia Kmiec , I used my previous experience as a student to redesign eight in-class activities. The activities included individual, small group (3-4 students), and large group (4-7 students) activities. I had completed the course in Winter of 2022 and knew my student perspective would be valuable when completing this project. My three goals for each activity were to develop: 1) clear instructions for students before beginning, 2) questions to consider during the activity, and 3) self-assessment questions to promote reflection and active learning. Thinking back to when I completed the course, I wanted to encourage students to speak up with their thoughts, opinions, and ideas about the course material. Helping students interact with their peers helps to build relationships, critical thinking skills, and the ability to collaborate with groups to reach a similar goal. Throughout my academic career, I have realized the value of smaller group class discussions and how these discussions can positively impact self-confidence in students. It is with our hope that each student that takes SOCI 3300 with Dr. Kmiec leaves with the belief that they can actively contribute, listen, and think about many different topics in all areas of life.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".