Introducing PBL in Engineering Education: Challenges Lecturers and Students Confront
Why this work is in the frame
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Bibliographic record
Abstract
Problem-based learning (PBL) is widely used across the professional education sector and is now emerging in engineeringeducation as both a viable and effective teaching and learning strategy. PBL originated some 45 years ago in medicaleducation at universities in McMaster (Canada), Maastricht (Netherlands) and Newcastle (Australia) and since then hasgained popularity worldwide in many professional disciplinary fields. The PBL approach, as presented in literature,supports a shift from teacher-directed, or centred, learning to facilitation of students’ learning, thus shifting the focus tostudents’ learning. Facilitation, as practiced in PBL, involves a different style of teaching compared to traditionallyaccepted styles, and from the experience of both students and lecturers, brings with its adoption challenges. Importantly, askilled PBL facilitator, who is secure in their role, can contribute significantly to the effectiveness of PBL groups’ work andthus to students’ learning. This paper reports on a qualitative study, and its findings, concerning the experiences ofacademic staff and students at one institution, the German Malaysian Institute (GMI), in Malaysia. During interviews andfocus groups, lecturers and students identified the challenges that lecturers face in effectively facilitating PBL. Analysesrevealed two major themes that inhibit success: lecturers’ and students’ adaptation to PBL. These findings provideinteresting insights into what is required to adapt to this mode of delivery.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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