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Volume 10,Issue 7

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30 June 2026
Construction of an Evaluation Indicator System for Medical MOOCs Quality in China Using Delphi and Analytic Hierarchy Process
He Jiang ,  Yuan Li ,  Hui Chen ,  Chunyan He ,  Samdrup Phuntsok ,  Langtsu Tsering ,  Yinghui Liu
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1 School of Nursing, Tianjin Medical University, Tianjin 300070, China,
2 Department of Medical and Nursing, Changdu Vocational and Technical School, Qamdo 854000, Xizang Autonomous Region, China,
3 Department of Nursing, Children’s Hospital, Tianjin University /Tianjin Children’s Hospital, Tianjin 300134, China,
JCNR 2026 , 10(6), 178–192; https://doi.org/10.26689/jcnr.v10i6.15561
© 2026 by the Authors. Licensee Whioce Publishing, Singapore. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Objective: This study addresses the extensive utilization of Massive Open Online Courses (MOOCs) in medical education and acknowledges the multitude of factors affecting their quality. Given the absence of a scientific and comprehensive assessment system, we aimed to formulate an evaluation indicator system for assessing the quality of medical MOOC construction in China and to determine the weight of each indicator. Methods: Using the Donabedian Structure-Process-Outcome model as theoretical guidance and based on relevant literature retrieval and analysis, we first constructed an evaluation indicator system for medical MOOC construction quality. Then, after two rounds of expert consultation, a final indicator content was built, and the weight of the indicators at each level was determined using the Analytic Hierarchy Process (AHP). Results: The effective recovery rate of the questionnaires in the two rounds of expert consultation was 68.75% (11/16) and 93.75% (15/16), respectively, and the expert authority coefficient (Cr) was 0.955 and 0.921, respectively. Kendall’s W coefficients for indicators at the second and third levels were 0.174 and 0.238, respectively (p < 0.01), indicating substantial expert consensus, and the weighted values of the indicators were determined with a consistency ratio (C.R.) < 0.1. Consequently, the final evaluation index system comprises three first-level indicators, six secondary indicators, and 71 tertiary indicators. Conclusion: This study successfully established a comprehensive evaluation indicator system for assessing the quality of medical MOOCs, which is characterized by scientific rigor and reliability, and could serve as a valuable reference for evaluating the quality of Medical MOOCs in China.

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