Implementation of PBL in Engineering Education: Conceptualization and Management of Tensions
Bibliographic record
Abstract
Engineering educators are facing high demands as they are being challenged to create learning environments that not only better teach technical skills, but also incorporate process skills and foster other desirable attributes. Problem-based learning, known as PBL, and its variants have been deemed effective as an instructional strategy in a variety of different disciplines including engineering. With pedagogical innovations like PBL, however, comfortable routines related to the structure and flow of classroom activity is disrupted for both educators and students. In addition to having to manage changes within their classroom processes and routines, engineering educators must also interact and operate within the larger systems in which their classrooms are embedded, the university. The structure and culture of the university system may facilitate or hinder the teaching intentions and goals of educators, as this larger system can impose its own set of tensions. In this paper, we report findings of a research study which investigated conceptualizations of PBL, tensions as experienced when implementing PBL and strategies to manage the tensions.
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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 | 0.019 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".