Challenges of introducing PBL in engineering: lecturers' and students' perspectives
Bibliographic record
Abstract
Problem-based learning (PBL) has become widely used across the professional education sector and is now emerging in engineering education as a viable teaching and learning strategy. PBL originated some 45 years ago in medical education at universities in McMaster (Canada), Maastricht (Netherlands) and Newcastle (Australia), and since then has gain popularity worldwide in many fields. The PBL approach as presented in literature supports a shift from teacher-directed learning to facilitation of students’ learning. Facilitation involves a different style of teaching compared to traditionally accepted styles, and from the experience of both students and lecturers, brings several challenges. A skilled PBL facilitator who is secure in his/her role can contribute significantly to the effectiveness of PBL groups’ work and thus to students’ learning. This paper reports on a qualitative study of the experiences of academic staff and students at one institution, the German Malaysian Institute (GMI), in Malaysia. During interviews and focus groups, lecturers and students identified the challenges that lecturers face in effectively facilitating PBL. Analyses revealed two major themes that inhibit success: lecturers’ and students’ adaptation to PBL. These findings provide interesting insights into what is required to adapt to this mode of delivery.
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 | 0.026 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".