Comparing the Views on Problem-Based Learning from Medical Education Students in Zhengzhou University in China and the University of Bristol in the UK
Bibliographic record
Abstract
Problem-based learning (PBL), as a student-centred learning method, refers to students actively participating in a group scenario to solve open-end problems. This study aims to compare the students’ attitudes on PBL in Zhengzhou University and the University of Bristol. This study adopts qualitative methods. By conducting semi-structured interviews with eight participants, four from Zhengzhou University and the others from the University of Bristol. Overall, the results of the study indicated that students from both two universities are overall satisfied with PBL because of its contribution to deeper understanding of medical knowledge and skill development and they all think that the quality of group discussion and the efficiency of PBL classes need to be improved. In terms of the different views from two universities, when it comes to the biggest benefit of PBL, students from Zhengzhou University are more likely to choose clinical thinking, while students from the University of Bristol are more satisfied with the deep understanding on medical knowledge. Unexpectedly, although Zhengzhou University has implemented PBL for fewer years than the University of Bristol, students are more satisfied with and motivated in PBL classes than those of the University of Bristol.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".