The Effect of Team-Based Learning on Conventional Pathology Education to Improve Students’ Mastery of Pathology
Bibliographic record
Abstract
In recent decades, traditional pathology education methodologies have been noticeably affected by new teaching approaches, including problem-based learning (PBL) and team-based learning (TBL). However, lack of outcome-based studies has hindered the extensive application of the TBL approach in the teaching of pathology in Chinese medical schools. In this study, a pilot TBL format on four topics in pathology was implemented in one session with medical students at Jinan University Medical School and the previous sessions of medical students were able to function as controls. The final exam scores of TBL participants were significantly higher than the scores for non-participants, indicating that the students demonstrated better academic performance at the end of the TBL class. In addition, the follow-up questionnaires revealed that the majority of the TBL participants spent more time studying and were actively and enthusiastically involved in TBL activities. The new teaching format also inspired teachers’ desire to lead discussions and administer quizzes instead of repeating rote didactics. Overall, this pilot study reveals that a combination of the TBL approach and traditional pathology theory can improve pathology education.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".