Problem-Based Learning: Facilitating Multiple Small Teams in a Large Group Setting
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Problem-based learning (PBL) is often described as resource demanding due to the high staff-to-student ratio required in a traditional PBL tutorial class where there is commonly one facilitator to every 5-16 students. The veterinary science program at Charles Sturt University, Australia, has developed a method of group facilitation which readily allows one or two staff members to facilitate up to 30 students at any one time while maintaining the benefits of a small PBL team of six students. Multi-team facilitation affords obvious financial and logistic advantages, but there are also important pedagogical benefits derived from uniform facilitation across multiple groups, enhanced discussion and debate between groups, and the development of self-facilitation skills in students. There are few disadvantages to the roaming facilitator model, provided that several requirements are addressed. These requirements include a suitable venue, large whiteboards, a structured approach to support student engagement with each disclosure, a detailed facilitator guide, and an open, collaborative, and communicative environment.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it