Bibliographic record
Abstract
Problem-Based Learning (PBL), a student-centred learning approach that focuses on reallife problems in higher education, has been around for more than fifty years (Servant-Miklos, Schmidt & Norman, 2019). It originated in 1969 at McMaster University's medical school in Canada and spread to other academic disciplines including engineering This wide-ranging diversity of applications has yielded, on the one hand, a rich body of theory and practice, with different PBL models emerging to meet diverging curricular requirements and learning objectives (Savin-Baden, 2003). On the other hand, it has also created some confusion, wherein the differences in philosophical understanding, didactic basis, and concrete practice between the academic disciplines have not been discussed thoroughly. At the same time, PBL is facing a host of new challenges from emerging global threats and opportunities, such as climate change, biodiversity loss, socioeconomic inequality, and technological progress, including artificial intelligence, with a commensurate rise in ethical challenges. Faced with the rapidly evolving environmental emergency, some PBL scholars have recently called for PBL to "change or risk irrelevance" (Servant-Miklos, Dolmans & Ryberg, 2023), advocating for the development of more socially engaged, transdisciplinary, and sustainable approaches to PBL.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.737 | 0.614 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".