STUDENT SELF-REGULATION PROFILES IN ONLINE LEARNING POST-COVID-19: A CLUSTER AND DISCRIMINANT ANALYSIS APPROACH
DOI:
https://doi.org/10.21831/majora.v30i2.82529Keywords:
Self-Regulated Learning, Cluster Analysis, Discriminant AnalysisAbstract
The transition to online learning has brought significant challenges and opportunities for higher education, emphasizing the importance of self-regulation as a critical skill for academic success. This study aims to profile students' self-regulation patterns in online learning environments using a combination of cluster and discriminant analyses. Data were collected from 577 undergraduate students using the Online Self-Regulated Learning Questionnaire (OSLQ), which measures six dimensions of self-regulation: goal setting, environment structuring, task strategies, time management, help-seeking, and self-evaluation. Cluster analysis, employing the K-Means method, identified three distinct student groups: (1) students with high self-regulation, excelling in goal setting, time management, and self-evaluation; (2) students with moderate self-regulation, showing adequate abilities with some limitations in task strategies and help-seeking; and (3) students with low self-regulation, struggling across all dimensions. Discriminant analysis revealed that self-evaluation, goal setting, and task strategies were the primary variables differentiating these clusters, with self-evaluation emerging as the most significant predictor. The findings underscore the critical role of self-regulation in online learning success and highlight the need for tailored interventions to support students with low self-regulation. These insights provide valuable implications for educators and institutions to design more adaptive and effective online learning strategies.References
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