Teaching in Active Learning Classrooms at a Canadian University
Bibliographic record
Abstract
This article describes an evaluation of a campus wide Active Learning initiative to examine instructors’ experiences teaching in Active Learning Classrooms (ALCs) at a Canadian University. ALCs at this university differ in size, layout, and audio-visual equipment. The participants were 21 instructors from different disciplines who had taught courses in various ALCs who were interviewed to explore their pedagogical decision-making, teaching experiences, and their access to ALC-specific, pedagogical and technological support. Instructors explained their classroom management strategies specific to ALCs, discussed how physical and technological features of different ALCs impacted the extent of necessary revisions to their courses, and highlighted logistical issues in being assigned to ALCs that may fit the requirements of their courses. Based on the findings, we pose a series of recommendations to offices responsible for classroom assignment, academic departments, and centres for faculty support and development. Overall, instructors would benefit from advance notice of ALC room assignment, just-in-time and self-directed opportunities regarding integrating Active Learning strategies in instruction, and access to orientation sessions and multimedia documentation developed for different types of ALCs.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".