AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
PDF (3.4 MB)
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Publishing Language: Chinese

Quality Assessment and Teaching Optimization Strategies for the Clinical Surgery Theoretical Examination Based on Knowledge-Point Analysis

Ru YAO1, Zenan XIA2, Changjun WANG1( ), Yidong ZHOU1 ( ), Qiang SUN1
Department of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
Department of Plastic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
Show Author Information

Abstract

Objective

To conduct a systematic analysis of a clinical surgery theoretical examination based on knowledge points, thereby providing a reference for establishing a scientific and comprehensive test paper analysis system.

Methods

Using the difficulty coefficient (P-value) and discrimination index (D-value) as indicators, a detailed evaluation was performed on 6 question types and 40 knowledge points from the Clinical Comprehensive Course: Surgery examination for the 2023 clinical medicine pilot class at Peking Union Medical College. Data were visualized using frequency distribution charts, radar charts, and scatter plots.

Results

A total of 23 examination papers were collected. The students' average score was 78.63±7.58, with a pass rate of 100%. The overall examination difficulty was relatively low (P=0.79), and the overall discrimination was poor (D=0.15). Among the 40 knowledge points, 2 (5.0%) were difficult, 9 (22.5%) were moderately difficult, and 29 (72.5%) were relatively easy. Regarding discrimination, 3 (7.5%) knowledge points showed excellent discrimination, 8 (20.0%) demonstrated good or acceptable discrimination, and 29 (72.5%) had relatively poor discrimination. Some knowledge points [e.g., "Benign Prostatic Hyperplasia" (P=0.26, D=-0.17)] presented issues of high difficulty coupled with poor discrimination. A considerable number of knowledge points require corresponding adjustments.

Conclusion

Knowledge-point-based test paper analysis can provide references for optimizing question type design, thereby enhancing the scientific nature of assessments and offering a basis for teaching improvements.

CLC number: R6; G643 Document code: A Article ID: 1674-9081(2026)05-1477-06

References

【1】
【1】
 
 
Medical Journal of Peking Union Medical College Hospital
Pages 1477-1482

{{item.num}}

Comments on this article

Go to comment

< Back to all reports

Review Status: {{reviewData.commendedNum}} Commended , {{reviewData.revisionRequiredNum}} Revision Required , {{reviewData.notCommendedNum}} Not Commended Under Peer Review

Review Comment

Close
Close
Cite this article:
YAO R, XIA Z, WANG C, et al. Quality Assessment and Teaching Optimization Strategies for the Clinical Surgery Theoretical Examination Based on Knowledge-Point Analysis. Medical Journal of Peking Union Medical College Hospital, 2026, 17(5): 1477-1482. https://doi.org/10.12290/xhyxzz.2025-0499

4

Views

0

Downloads

0

Crossref

0

Scopus

0

CSCD

Received: 25 May 2025
Accepted: 25 July 2025
Published: 16 October 2025
© 2026 Medical Journal of Peking Union Medical College Hospital