International journal of nursing sciences · 2026
Development and validation of the intelligent digital learning student engagement influencing factor scale for medical students.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод в очереди
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Hu Rong, Zhang Xiao, Zhang Xinyao, Luo Yixin
Аннотация
OBJECTIVES: This study aimed to develop and psychometrically evaluate the Intelligent Digital Learning Student Engagement Influencing Factor Scale for nursing and other medical students.
METHODS: This instrumental development study was conducted in two phases. Phase 1 involved scale development through theoretical modeling, synthesizing the Pedagogy-Social Interaction-Technology Model, the Model of Student Motivation and Self-Regulated Learning, and the Three-Dimensional Model of Student Engagement, followed by qualitative interviews with 25 medical students guided by this integrated framework, a two-round Delphi expert consultation with eight experts, and pilot testing with 30 nursing students. Phase 2 involved psychometric evaluation through a cross-sectional survey of 1,499 medical students, with nursing students constituting the largest subgroup (n = 673, 44.90 %), followed by clinical medicine (n = 616, 41.09 %), medical laboratory science (n = 132, 8.81 %), and preventive medicine (n = 78, 5.20 %). The sample was randomly divided into two subsamples for exploratory (n = 750) and confirmatory (n = 749) factor analyses. Construct validity, reliability, and item discrimination were assessed.
RESULTS: The final scale comprised 20 items across four factors: personal digital literacy, perceived school support, perceived technological environment, and perceived interpersonal interaction and support. These factors validated the integrated theoretical framework, reflecting learner-level psychological mechanisms, macro-level institutional support, and micro-level classroom factors encompassing technological and social interaction dimensions. Exploratory factor analysis explained 66.85 % of the total variance, with factor loadings ranging from 0.488 to 0.751. Confirmatory factor analysis demonstrated acceptable model fit (χ2/df = 4.587, CFI = 0.928, TLI = 0.968, RMSEA = 0.049, SRMR = 0.054). Average variance extracted values ranged from 0.542 to 0.608, and composite reliability ranged from 0.846 to 0.890. The scale demonstrated excellent internal consistency (Cronbach's α = 0.920) and split-half reliability (0.918). Item discrimination analysis confirmed significant differences between the upper and lower 27 % of the sample (P < 0.01).
CONCLUSION: This scale enables nurse educators to diagnose engagement deficits across institutional, technological, and interpersonal dimensions, guiding curricular improvements and providing a basis for precise interventions amid digital teaching transformation. Future studies should longitudinally examine its utility in nursing populations and its predictive validity for clinical performance.
METHODS: This instrumental development study was conducted in two phases. Phase 1 involved scale development through theoretical modeling, synthesizing the Pedagogy-Social Interaction-Technology Model, the Model of Student Motivation and Self-Regulated Learning, and the Three-Dimensional Model of Student Engagement, followed by qualitative interviews with 25 medical students guided by this integrated framework, a two-round Delphi expert consultation with eight experts, and pilot testing with 30 nursing students. Phase 2 involved psychometric evaluation through a cross-sectional survey of 1,499 medical students, with nursing students constituting the largest subgroup (n = 673, 44.90 %), followed by clinical medicine (n = 616, 41.09 %), medical laboratory science (n = 132, 8.81 %), and preventive medicine (n = 78, 5.20 %). The sample was randomly divided into two subsamples for exploratory (n = 750) and confirmatory (n = 749) factor analyses. Construct validity, reliability, and item discrimination were assessed.
RESULTS: The final scale comprised 20 items across four factors: personal digital literacy, perceived school support, perceived technological environment, and perceived interpersonal interaction and support. These factors validated the integrated theoretical framework, reflecting learner-level psychological mechanisms, macro-level institutional support, and micro-level classroom factors encompassing technological and social interaction dimensions. Exploratory factor analysis explained 66.85 % of the total variance, with factor loadings ranging from 0.488 to 0.751. Confirmatory factor analysis demonstrated acceptable model fit (χ2/df = 4.587, CFI = 0.928, TLI = 0.968, RMSEA = 0.049, SRMR = 0.054). Average variance extracted values ranged from 0.542 to 0.608, and composite reliability ranged from 0.846 to 0.890. The scale demonstrated excellent internal consistency (Cronbach's α = 0.920) and split-half reliability (0.918). Item discrimination analysis confirmed significant differences between the upper and lower 27 % of the sample (P < 0.01).
CONCLUSION: This scale enables nurse educators to diagnose engagement deficits across institutional, technological, and interpersonal dimensions, guiding curricular improvements and providing a basis for precise interventions amid digital teaching transformation. Future studies should longitudinally examine its utility in nursing populations and its predictive validity for clinical performance.