LATENT PROFILE MODELING OF SELF-REGULATED LEARNING, MOTIVATIONAL STRATEGIES, DEEP LEARNING TECHNIQUES, AND PERCEIVED ACADEMIC PERFORMANCE AMONG UNIVERSITY STUDENTS
DOI:
https://doi.org/10.69980/1aa5cd15Keywords:
self-regulated learning, motivational strategies, deep learning, perceived academic performance, latent profile analysisAbstract
University students differ substantially in how they regulate learning, maintain motivation, use deep learning techniques, and perceive their academic performance. Identifying these differences can support more targeted academic interventions. This study aimed to identify distinct latent learning profiles based on self-regulated learning, motivational strategies, deep learning techniques, and perceived academic performance, and to examine demographic differences associated with profile membership. A cross-sectional secondary-data analysis was conducted using responses from 1,316 university students from Chile and Ecuador. Nineteen GAEU-1 items were grouped into four learning constructs. Descriptive statistics, Cronbach’s alpha, Pearson correlations, latent profile analysis, chi-square tests, Kruskal–Wallis tests, and multinomial logistic regression were performed. Five latent profiles were identified. These ranged from consistently low learning-strategy patterns to highly self-regulated, motivated, and high-performing profiles. All four learning dimensions were positively correlated. Nationality and gender differed significantly across profiles, while age and several other demographic characteristics showed no significant overall differences. Selected demographic predictors were associated with membership in specific profiles. University students demonstrated clear heterogeneity in multidimensional learning patterns. Latent profile modeling provided a useful person-centered framework for identifying students with different academic strengths and support needs.
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