Deep learning approach in undergraduate nursing students and their relationship with learning outcomes: A latent profile analysis

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Detalles Bibliográficos
Publicado en:	Nurse Education in Practice vol. 85 (May 2025), p. 104379
Autor principal:	Zhang Jiayuan
Otros Autores:	Ji Xiangzi, Yang, Li, Zhang, Hui, Li-Na, Meng
Publicado:	Elsevier Limited
Materias:	China Problem solving Questionnaires Likert scale Cognitive ability Thinking skills Cognition & reasoning Medical education Innovations Knowledge College students Critical thinking Clinical assessment Nursing education Deep learning Competency based learning Clinical medicine Coping Teaching methods Skills Educational objectives Classrooms Clinical nursing Medical students Elementary school students Measures Flipped classroom Nurses Learning Variance analysis Teaching Attitudes Nursing Learning outcomes Profiles Learning Activities Educational Practices Likert Scales Medical Evaluation Medical Services Competency Based Education Knowledge Level Individual Differences Evidence Based Practice Educational Environment Competence Learning Strategies Nursing Students Meta Analysis Instructional Materials Communication Skills Educational Change Career Development Achievement Gains Individual Needs Class Activities
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Descripción
Resumen:	Background Deep learning approach plays a pivotal role in nursing education, equipping students with the critical thinking skills and knowledge necessary to address complex clinical challenges. However, nursing students exhibit diverse approaches to deep learning, affected by individual characteristics, academic environments and teaching methods. Objective This study aims to identify latent profiles of deep learning approach among undergraduate nursing students and analyze the factors influencing these profiles and their association with learning outcomes. Design A descriptive cross-sectional survey. Methods A total of 891 undergraduate nursing students from two medical universities in China participated in this study between May and July 2024. Data were collected using the Deep Learning Scale and the Learning Outcomes Scale. Latent profile analysis was employed to identify deep learning profiles. One-way analysis of variance and multinomial logistic regression were used to explore influencing factors of different profiles. The Bolck-Croon-Hagenaars (BCH) method was applied to examine differences in learning outcomes across profiles. Results Four latent profiles of deep learning were identified: "Comprehensive Deep Learners" (27.0 %), "Ability-Oriented Learners" (25.4 %), "Attitude-Driven Learners" (21.7 %) and "Surface Coping Learners" (25.8 %). Gender, grade, preference for the nursing major and participation in flipped classrooms were significant factors influencing profile membership ( p < 0.05). "Comprehensive Deep Learners" had the highest learning outcome scores, while "Surface Coping Learners" scored the lowest. Conclusions Significant heterogeneity exists in deep learning approach among undergraduate nursing students. "Comprehensive Deep Learners" achieved the highest learning outcomes. Nursing education should adopt tailored interventions based on the characteristics of different deep learning profiles to improve students’ learning outcomes and comprehensive competencies.
ISSN:	1471-5953 1873-5223
DOI:	10.1016/j.nepr.2025.104379
Fuente:	Sociology Database