Developing skills of problem-based learning: what about specialist knowledge
Bibliographic record
Abstract
Problem-based learning (PBL) is an educational approach that uses problems or 'triggers' to initiate students' learning. Typically, students work in small groups (between eight and ten people), facilitated by a tutor, where they are required to identify, source, and contextualize knowledge to solve a given problem. The origins of PBL can be traced to the McMaster Medical School in Canada in 1965 but it has since become a popular means of delivering other disciplines, especially, but not exclusively, other healthcare courses such as nursing, occupational therapy, or physiotherapy. With its focus on group work, independent learning and knowledge application, PBL seemingly equips students with the capital to adapt to modern day, flexible economies. Previous research on PBL has focused on students' learning styles or their approaches to group work but students' understanding of knowledge and PBL has received little attention in the literature. This qualitative study explored undergraduate occupational therapy students' perceptions of knowledge from one PBL course. The data were collected through the use of twenty semi-structured interviews and the findings were analyzed thematically and in relation to theoretical constructs derived from the sociologists of education Basil Bernstein and Karl Maton. The findings suggest that whilst PBL offered students the opportunity to develop and enhance skills such as team working, their understanding of professional specific knowledge was limited. In a climate where healthcare provision is becoming increasingly pluralized and inter-professional working is common, practitioners still require an understanding of the esoteric knowledge that differentiates their practices from each other. This research highlights the need for PBL educators to consider the types of knowledge that students' acquire in addition to knowledge application and PBL skills.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.015 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".