Heterogeneity Study on Learning Strategies of Undergraduate Nursing Students Based on Latent Profile Analysis and Exploration of Influencing Factors

Background Learning strategies are critical for undergraduate nursing students to acquire professional knowledge, develop clinical competencies, and adapt to the demands of future nursing practice. However, existing research often treats learning strategies as a homogeneous construct, overlooking potential heterogeneity in how nursing students employ these strategies. Identifying latent profiles of learning strategies and their influencing factors is essential to develop targeted educational interventions, optimize learning outcomes, and promote the professional development of nursing students. Methods A cross-sectional study was conducted between January and March 2025. Convenience sampling was used to recruit 2,279 undergraduate nursing students from three universities in Sichuan Province, China. Data were collected using four validated instruments: a general information questionnaire, the College Students’ Learning Strategy Usage Scale, the College Students’ General Academic Emotion Scale, and the Academic Self-Efficacy Scale. Latent Profile Analysis (LPA) was performed to identify distinct latent profiles of learning strategies, with model selection based on fit indices and theoretical plausibility. Multivariate logistic regression analysis was then used to explore the associations between academic emotion, academic self-efficacy, and latent profile membership, adjusting for potential confounding variables. Results LPA revealed three distinct latent profiles of learning strategies among undergraduate nursing students: (1) Poor Learning Strategy Class (n = 107, 4.69%), characterized by low scores across all learning strategy dimensions; (2) Good Learning Strategy Class (n = 1,301, 57.08%), with moderate scores in most strategy dimensions and adequate metacognitive awareness; and (3) Excellent Learning Strategy Class (n = 870, 38.17%), defined by high scores in all learning strategy domains, particularly in deep learning and self-regulated learning. Logistic regression analysis showed that academic emotion (OR = 0.906, 95% CI: 0.876–0.937, P  < 0.001) and academic self-efficacy (OR = 1.018, 95% CI: 1.012–1.024, P  < 0.001) were significant predictors of latent profile membership. Specifically, more positive academic emotions and higher academic self-efficacy were associated with a greater likelihood of belonging to the Excellent Learning Strategy Class (vs. the Poor or Good classes). Conclusions Undergraduate nursing students exhibit significant heterogeneity in learning strategies, which can be classified into three distinct latent profiles. Academic emotion and academic self-efficacy are key factors influencing these profiles. Nursing educators should prioritize targeted interventions for students in the Poor Learning Strategy Class (to build foundational learning skills) and Good Learning Strategy Class (to enhance deep learning and self-regulation). Such interventions could include academic emotion management workshops (e.g., stress reduction techniques, fostering positive learning attitudes) and self-efficacy enhancement programs. By addressing these influencing factors, nursing education can optimize students’ learning strategies, ultimately improving their academic performance and supporting the development of competent, well-rounded nursing professionals.