Implementation of PBL: piecemeal or all the way?
Bibliographic record
Abstract
The transformation from traditional teaching to student-centred learning is a widespread global phenomenon. Since the introduction of Problem-Based Learning (PBL) as the main didactic method at the new medical school of McMaster University in Canada about 40 years ago, many institutes in higher education have followed. At first PBL was mostly applied in new schools, starting fresh with the development of a completely new curriculum, like the University of Maastricht in the Netherlands and the University of Aalborg in Demark. In the mean time PBL has become quite popular as a method to involve students and to promote active learning in Engineering Education. Different institutions apply the PBL didactic principles in many different ways. What is more, implementation of PBL in an existing institute with standing traditions constitutes an altogether different challenge than building a new curriculum from scratch. In particular in institutes with a strong academic tradition the question often arises whether it is necessary to go all the way at once. The paper discusses advantages and disadvantages of a range of implementation strategies, based on an example from practice at TU Delft.
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 | 0.021 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".