Problem‐based learning in a new Canadian curriculum
Bibliographic record
Abstract
Problem-based learning (PBL) is a method of group learning that uses true-to-life problems as a stimulus for students to learn problem-solving skills and acquire knowledge about the basic and clinical sciences. This article documents the design and implementation of PBL in a second year course in the new curriculum of the University of Ottawa School of Nursing's Generic Program. The learning and teaching experiences of students and facilitators in this PBL course are described. As a way to determine students' perception of their learning using PBL, they were asked to respond to four questions. The most frequently described thinking processes were problem solving, nursing process and group process. When asked to describe the learning they derived from PBL, as differentiated from other instructional methods, students identified group process and problem solving most often. The most frequently identified factors that influenced performance and learning in PBL were positive attitude and group effort. The factors that affected the facilitators' performance of their role were large group size, insufficient practice of facilitator skills and PBL preparation. To enhance group process, facilitators modelled and shared roles. They fostered student motivation and development through formative evaluation. PBL produced clear benefits for students, such as increased autonomous learning, critical thinking, problem solving and communication. For facilitators, PBL was a liberation from the traditional role of 'content expert and super consultant'.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".