Evidence map›Paper›PMID 41437036›Full record

ArticleBMC medical education2025

Assessing and optimizing Team-Based Learning in undergraduate cosmetic dermatology education: an empirical study using interpretable machine learning.

Pingxiang Ouyang, Lu Zhou, Lihua Gao, Jianyun Lu, Jinrong Zeng

Abstract read
In one paragraph

Article in BMC medical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Pingxiang OuyangDepartment of Dermatology, The Third Xiangya Hospital, Central South University, 138 Tongzipo Road, Changsha, China.
Lu ZhouDepartment of Dermatology, The Third Xiangya Hospital, Central South University, 138 Tongzipo Road, Changsha, China.
Lihua GaoDepartment of Dermatology, The Third Xiangya Hospital, Central South University, 138 Tongzipo Road, Changsha, China.
Jianyun LuDepartment of Dermatology, The Third Xiangya Hospital, Central South University, 138 Tongzipo Road, Changsha, China.
Jinrong ZengDepartment of Dermatology, The Third Xiangya Hospital, Central South University, 138 Tongzipo Road, Changsha, China. zengjinrong1989@csu.edu.cn.

Funding

Central South University 2025JGB026
6 · The paper itself

Abstract

backgroundThe research aimed to explore the application effectiveness, advantages, and disadvantages of Team-Based Learning (TBL) in undergraduate cosmetic dermatology education and to analyze directions for pedagogical improvement using interpretable machine learning (IML).

methodsA total of 154 undergraduate clinical medicine students from Xiangya School of Medicine, Central South University were included as study subjects. Using the cosmetic dermatology chapter (two teaching units) from the Dermatology and Venereology course, a comparative design was implemented between traditional Lecture-Based Learning (LBL) and interest-oriented TBL. Teaching feedback was collected through questionnaires, and IML and correlation analysis were applied to identify key nodes for pedagogical optimization.

resultsSurveys showed that 83.8% of students reported increased learning interest, 84.4% were willing to engage in post-class self-directed learning, 81.2% were satisfied with the course, and 86.4% desired more TBL courses. Recognition of teaching components revealed that 87.0% preferred the presentation part, 89.0% endorsed the teamwork part, 80.5% agreed with the questioning part, and 95.5% valued the discussion part. Compared to LBL, TBL significantly improved self-directed learning capability (P < 0.001) and knowledge mastery capability (P = 0.004), but showed weaker improvement in independent thinking capability (P = 0.028). IML revealed that the most important feature determining the enthusiasm for cosmetic dermatology and TBL was the enhancement of independent thinking capability. Course satisfaction was primarily influenced by improved self-directed learning capability. Correlation analysis indicated that the recognition of most teaching components positively correlated with learning interest in cosmetic dermatology and satisfaction with TBL, except that students with higher recognition of the Teamwork Part (r = -0.432) and Questioning Part (r = -0.900) paradoxically exhibited resistance to the implementation of additional TBL courses.

conclusionTBL effectively enhances students' learning interest and comprehensive competencies in cosmetic dermatology education, demonstrating strong potential for broader application. However, significant opportunities for refinement persist in instructional design.

Indexed as

DermatologyEducation, Medical, UndergraduateMachine LearningCurriculumEducational MeasurementFemaleHumansMaleProblem-Based LearningStudents, MedicalSurveys and QuestionnairesArtificial intelligenceCosmetic dermatologyMachine learningSHapley additive explanationsTeaching reformTeam-Based learning

Identifiers

PMID41437036
PMCPMC12836865

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.