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A latent profile analysis of self-regulation associated with engagement in online learning among nursing students
Ying Chen1, Wei Wei1, Yanqing Yang2
1Xiamen Medical College, Xiamen, Fujian, China.
Aim:
To identify different online self-regulation learning (SRL) profiles among nursing students and to explore differences in demographic variables and online learning engagement among the profiles.
Background:
Many students study via online classes and their self-regulation during learning had an impact on their engagement and learning quality. Identifying online SRL profiles could aid in planning and executing online learning activities.
Design:
A cross-sectional survey study.
Methods:
The study involved 676 nursing students from a higher vocational college in Fuzhou, China, between March 22 and 28, 2021. The Online Self-regulated Learning Questionnaire and the Student Engagement in Distance Education Questionnaire were used to assess SRL and engagement. Latent profile analysis and multinomial logistic regression were performed.
Results:
The patterns of self-regulation in online learning among nursing students were categorized as "limited online SRL ability" (n = 234), "moderate online SRL ability" (n = 408) and "superior online SRL ability" (n = 34). A regression analysis indicated statistically significant inter-category differences in grade, main reasons for choosing nursing as a major and degree of professional identity (P < 0.05) and no significant inter-category differences in sex, place of origin and family economic status (P > 0.05). The stronger the online SRL ability, the higher the level of online learning engagement (P < 0.05).
Conclusion:
The distribution of some demographic variables showed differences among different categories of nursing students and intervention measures corresponding to the different characteristics of each category may help improve online SRL ability and promote the level of online learning engagement.
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