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<p>Self-regulated learning (SRL) has been regarded as one of the indispensable factors affecting students’ academic success in online learning environments. However, the current understanding of the mechanism/causes of SRL in online ill-structured problem-solving remains insufficient. This study, therefore, examines the configural causal effects of goal attributes, motivational beliefs, creativity, and grit on self-regulated learning. With the fuzzy sets approach (fsQCA), the proposed association was analyzed based on a sample of students (<i>n</i> = 88) participating in an educational design competition activity. The results uniquely revealed the predictive factors of SRL at both high and low levels. In addition, it was found that no single condition of factors leads to the prediction of high or low self-regulation. More specifically, different conditions of factors, in terms of gender, goal attributes (goal setting and achievement goals), grit, task value, creativity, and self-efficacy, can largely predict high and low self-regulated learning during ill-structured problem-solving in the context of online learning. Implications for theory and policy prescriptions were discussed to enhance self-regulated learning in online ill-structured problem-solving.</p>

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The combined effects of goal attributes, motivational beliefs, creativity and grit on self-regulation in online ill-structured problem solving: a fsQCA approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年1月9日

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  • Huanhuan Wang,
  • Shaofeng Wang,
  • Ahmed Tlili,
  • Mengti Li,
  • Dong Yang,
  • Michael Agyemang Adarkwah,
  • Xixian Zhu,
  • Ling Zhu,
  • Ronghuai Huang,
  • Hongyan Kuai

摘要

Self-regulated learning (SRL) has been regarded as one of the indispensable factors affecting students’ academic success in online learning environments. However, the current understanding of the mechanism/causes of SRL in online ill-structured problem-solving remains insufficient. This study, therefore, examines the configural causal effects of goal attributes, motivational beliefs, creativity, and grit on self-regulated learning. With the fuzzy sets approach (fsQCA), the proposed association was analyzed based on a sample of students (n = 88) participating in an educational design competition activity. The results uniquely revealed the predictive factors of SRL at both high and low levels. In addition, it was found that no single condition of factors leads to the prediction of high or low self-regulation. More specifically, different conditions of factors, in terms of gender, goal attributes (goal setting and achievement goals), grit, task value, creativity, and self-efficacy, can largely predict high and low self-regulated learning during ill-structured problem-solving in the context of online learning. Implications for theory and policy prescriptions were discussed to enhance self-regulated learning in online ill-structured problem-solving.