Creating a Participatory Classroom for All Levels of Undergraduate Students: A Co-operative Learning Workshop
Bibliographic record
Abstract
This workshop is based on the idea that participatory learning must be experienced, discussed, and reflected upon to be implemented to full effect in the classroom. As the facilitator, I guided those present in an active examination of some of the issues surrounding how best to create a participatory classroom (using “think-pair-share” and co-operative learning groups). The forty participants, after introductory comments, were posed four questions: 1) Why was participatory learning important? 2) What participatory learning techniques have you used in your classes? 3) How effective have these initiatives been? and 4) How were these initiatives subsequently modified to take into account differing abilities and backgrounds of students? Participants discussed these questions in small groups and then reported on their deliberations encompassing both the potential for and the problems encountered in trying to create a participatory classroom. The workshop provided a working example of participatory learning in action and an opportunity for focused peer discussion on the topic, which no doubt served as some inspiration to those considering the transition to implementing a more participatory style of teaching and learning in their own classrooms.
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 | 0.050 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.006 | 0.025 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".