Active learning spaces in a nutshell- Design and practice considerations
Bibliographic record
Abstract
Gone are the days of the rigid, auditorium-style classroom in Canadian universities. More and more institutions are beginning to invest in active learning spaces. These learning spaces often include moveable furniture, numerous writing spaces, which creates a more interactive learning environment for students. They may range in size and set-up, but one key feature that these spaces share is a rich opportunity for student collaboration. One such space is the Western Active Learning Space (WALS) at Western University. The WALS offers a unique experience for both instructors and students. The room is set-up with 7 D-shaped fixed tables and movable chairs; creating a “student-centred” learning environment. With the development of more active learning spaces occurring in higher education, it is worthwhile to examine practices that can help and/or hinder participation in this new educational environment. During this session, participants will learn about simple teaching strategies that can be implemented during the first day of class and throughout the semester to facilitate constructive and respectful discussions, develop a culture of collegiality, and allow students who are less inclined to participate an opportunity to contribute. The presentation will conclude with the discussion of an in-progress research project that will evaluate the capacity of active learning spaces to facilitate student development of transferable skills such as effective communication.
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 | 0.042 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.023 | 0.022 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".