Preliminary research in the application of integrated learning and teacher-centredness in undergraduate education in China
Bibliographic record
Abstract
BACKGROUND: The General Practice Department of Fudan University recognised that a traditional didactic educational approach will not achieve expected learning outcomes. Therefore, the Department adopts an integrated learning and teacher-centred approach. AIM: To evaluate the effect of introducing integrated learning and a teacher-centred approach to undergraduate medical education in China. METHODS: The concept of integrated learning and the use of a teacher-centred approach was introduced to the General Practice Department of Fudan University's 'Doctor-Patient Communication Skills' undergraduate course. A self-designed questionnaire and a questionnaire used by Fudan University to evaluate the students' satisfaction with their tutors were used to survey 58 medical students. RESULTS: The self-designed questionnaire gave good reliability and validity results. Based on the survey, 88% of the students both enjoyed the course and rated it highly. The students also showed a high degree of satisfaction with their tutors. CONCLUSION: Although the student numbers were low, their comments have indicated that the newly introduced, but optional course has achieved a highly desirable effect amongst the students. We believe that this effect has been created through a change from a didactic approach to teaching to a more student-centred approach and by giving meaning and purpose to the students learning; in effect making teaching and learning an enjoyable and satisfying experience for all.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".