Wang Huanhuan, Wang Shaofeng, Tlili Ahmed, Li Mengti, Yang Dong, Adarkwah Michael Agyemang, Zhu Xixian, Zhu Ling, Huang Ronghuai, Kuai Hongyan
National Engineering Research Center of Cyberlearning and Intelligent Technology (China), Faculty of Education, Beijing Normal University, Beijing, China.
International Business School, Fuzhou University of International Studies and Trade, Fuzhou, China.
BMC Psychol. 2025 Jan 9;13(1):27. doi: 10.1186/s40359-024-02317-0.
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.
自我调节学习(SRL)被视为影响学生在在线学习环境中学术成就的不可或缺的因素之一。然而,目前对在线非结构化问题解决中SRL的机制/原因的理解仍然不足。因此,本研究考察了目标属性、动机信念、创造力和毅力对自我调节学习的构型因果效应。采用模糊集方法(fsQCA),基于参与教育设计竞赛活动的学生样本(n = 88)对所提出的关联进行了分析。结果独特地揭示了高水平和低水平自我调节学习的预测因素。此外,研究发现没有单一的因素条件能够预测高水平或低水平的自我调节。更具体地说,在性别、目标属性(目标设定和成就目标)、毅力、任务价值、创造力和自我效能感等方面,不同的因素条件在很大程度上可以预测在线学习背景下非结构化问题解决过程中的高水平和低水平自我调节学习。讨论了对理论和政策规定的启示,以加强在线非结构化问题解决中的自我调节学习。