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<p>Self-regulated learning (SRL) is crucial for academic success in online learning. Previous studies have employed a variable-centered approach to examine the relationship between students’ SRL and a variety of determinants and consequences. However, few studies employ a person-centered approach to categorize students’ self-regulated learning and examine its predictors and outcomes, especially the non-academic outcomes. Therefore, this study employed a person-centered approach to identify profiles of self-regulated learners and investigate how SRL profile membership is predicted by factors in an online learning environment as well as the differences in learning engagement and well-being across SRL profiles. This study surveyed 385 college students from China. Four SRL profiles were identified using latent profile analysis (LPA), namely: low, below average with high ES, average, and high SRL profiles. Results indicated that the four profiles differed in the extent to which students employed SRL strategies. Additionally, we ran the three-step (R3STEP) command to examine the predictive role of online learning environments (OLE). Finally, the Block-Croon-Hagenaars approach (BCH) was conducted to reveal the differences in learning engagement and well-being across SRL profiles. The multinomial logistic regression results indicated that SRL profile membership is significantly predicted by contextual factors, including pedagogical, social, and technical affordances. The behavioral, cognitive, emotional engagement, and well-being differed significantly among the four profiles. More specifically, the factors of OLE are antecedents of SRL profile membership, and SRL could enhance learning engagement and well-being. The findings contribute to a deeper understanding of students’ self-regulated learning profiles and the underlying mechanisms of online learning engagement and well-being, offering practical implications for both educators and researchers in higher education. Moreover, this study provides theoretical and empirical implications for subsequent studies.</p>

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A person-centered perspective in assessing college students′ self-regulated learning in an online learning environment: potential profiles, antecedents, and outcomes

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月7日

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  • Yafei Shi,
  • Mingyue Wu,
  • Yantao Wei,
  • Jiali Chen,
  • Qianqian Dong,
  • Ke Zhu

摘要

Self-regulated learning (SRL) is crucial for academic success in online learning. Previous studies have employed a variable-centered approach to examine the relationship between students’ SRL and a variety of determinants and consequences. However, few studies employ a person-centered approach to categorize students’ self-regulated learning and examine its predictors and outcomes, especially the non-academic outcomes. Therefore, this study employed a person-centered approach to identify profiles of self-regulated learners and investigate how SRL profile membership is predicted by factors in an online learning environment as well as the differences in learning engagement and well-being across SRL profiles. This study surveyed 385 college students from China. Four SRL profiles were identified using latent profile analysis (LPA), namely: low, below average with high ES, average, and high SRL profiles. Results indicated that the four profiles differed in the extent to which students employed SRL strategies. Additionally, we ran the three-step (R3STEP) command to examine the predictive role of online learning environments (OLE). Finally, the Block-Croon-Hagenaars approach (BCH) was conducted to reveal the differences in learning engagement and well-being across SRL profiles. The multinomial logistic regression results indicated that SRL profile membership is significantly predicted by contextual factors, including pedagogical, social, and technical affordances. The behavioral, cognitive, emotional engagement, and well-being differed significantly among the four profiles. More specifically, the factors of OLE are antecedents of SRL profile membership, and SRL could enhance learning engagement and well-being. The findings contribute to a deeper understanding of students’ self-regulated learning profiles and the underlying mechanisms of online learning engagement and well-being, offering practical implications for both educators and researchers in higher education. Moreover, this study provides theoretical and empirical implications for subsequent studies.