Revolutions & re-iterations : An intellectual history of problem-based learning
Bibliographic record
Abstract
textabstractThe same year as the opening of the Woodstock music festival, a small medical school in Hamilton, Ontario, launched a daring new medical education programme in which lectures were replaced by small-group, interdisciplinary problem-based tutorials. Problem-based learning, as it became known, took the world of higher education by storm, such that today over 500 institutions in the World claim to use this method in almost every field of study, from engineering to liberal arts. Through the in-depth historical analysis of archive materials, oral history interviews and contemporary publications, this thesis proposes a rigorous account of the intellectual history of PBL from its birth place at McMaster University, to its evolution in Maastricht University, closing on a comparison with the Danish problem-oriented, project-based model of higher education. The author delivers a narrative that stands at the cross-roads between history, philosophy of education and cognitive psychology. “Revolutions and Re-iterations” retraces not only the key historical events that shaped PBL but also the sources of inspiration for many of PBL’s key features and the central theoretical debates that defined the practice of PBL since the 1970s.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.064 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".