Acceptance of problem-based learning by preclinical students from a public university and its impact on their learning
Bibliographic record
Abstract
Introduction: In modern medical education, with its transformational changes, teacher-centered learning is transformed into student-centered learning. This shift has escalated so fast with momentum, and its value in teaching and learning has been evaluated in many ways. In the current scenario, problem-based learning (PBL) is a well-recognized effective method of teaching and learning. The credit goes to McMaster University in Hamilton, Ontario, Canada, for the establishment of PBL. In Malaysia, many medical schools have applied this method in their curriculum, one of which is University Malaysia, Sarawak (UNIMAS), which has also adopted PBL in its undergraduate curriculum since 1996. Purpose: The aim of this study is to determine the students’ acceptance of PBL and its positive and negative impact on their learning. Methodology: It was a cross-sectional study conducted to determine the acceptance of the students of PBL. The study population is selected using convenience sampling of 140 out of 148 pre-clinical year-2 students who were exposed to the PBL method, the focus group discussion (FGD) was conducted based on pre-framed questions to know the impact of PBL on the students’ learning. Results: Results showed that the respondents had the satisfaction and accepted PBL. Sixty students from 6 PBL groups were involved in FGD. Feedback from FGD revealed their difficulties with the conduction of 1st session of PBL, for instance, problems in searching resources, new learning environments and peers from different regions. There were positive responses spelled by the subjects that the PBL has improved their communication skills, critical thinking, and self-esteem. Conclusion: Overall the PBL has proved beneficial evidenced a positive impact on the learning process of medical students.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".