本文全面探讨了自我调节学习理论及其在教育实践中的应用,文章首先综述了5个核心理论模型,包括Zimmerman的循环模型、Pintrich的行为模型和Winne与Hadwin的阶段模型、Boekaerts双重加工模型、Efklides的MASRL模型,这些模型强调了不同的...本文全面探讨了自我调节学习理论及其在教育实践中的应用,文章首先综述了5个核心理论模型,包括Zimmerman的循环模型、Pintrich的行为模型和Winne与Hadwin的阶段模型、Boekaerts双重加工模型、Efklides的MASRL模型,这些模型强调了不同的学习过程,并为教育者提供了促进自我调节能力的策略。文中通过实证研究分析了自我调节学习在减轻认知负荷方面的作用。文章讨论了教育技术,如智能教学系统和人工智能等技术实践,以及在课堂教学和远程教育中的自我调节学习策略实践,阐述了如何通过提供个性化学习路径和实时反馈等来支持自我调节学习。This paper thoroughly examines the theory of self-regulated learning and its implementation in educational practices. It begins with a review of five fundamental theoretical models: Zimmerman’s cycle model, Pintrich’s behavioral model, Winne and Hadwin’s stage model, Boekaerts’ dual-processing model, and Efklides’ MASRL model. These models highlight various learning processes and offer educators methods to enhance self-regulation skills. The article further investigates the impact of self-regulated learning on reducing cognitive load through empirical studies. It covers the use of educational technology, such as intelligent teaching systems and artificial intelligence, in facilitating self-regulated learning strategies in both classroom settings and distance education. Additionally, it details how to support self-regulated learning by providing personalized learning paths and real-time feedback.展开更多
文摘本文全面探讨了自我调节学习理论及其在教育实践中的应用,文章首先综述了5个核心理论模型,包括Zimmerman的循环模型、Pintrich的行为模型和Winne与Hadwin的阶段模型、Boekaerts双重加工模型、Efklides的MASRL模型,这些模型强调了不同的学习过程,并为教育者提供了促进自我调节能力的策略。文中通过实证研究分析了自我调节学习在减轻认知负荷方面的作用。文章讨论了教育技术,如智能教学系统和人工智能等技术实践,以及在课堂教学和远程教育中的自我调节学习策略实践,阐述了如何通过提供个性化学习路径和实时反馈等来支持自我调节学习。This paper thoroughly examines the theory of self-regulated learning and its implementation in educational practices. It begins with a review of five fundamental theoretical models: Zimmerman’s cycle model, Pintrich’s behavioral model, Winne and Hadwin’s stage model, Boekaerts’ dual-processing model, and Efklides’ MASRL model. These models highlight various learning processes and offer educators methods to enhance self-regulation skills. The article further investigates the impact of self-regulated learning on reducing cognitive load through empirical studies. It covers the use of educational technology, such as intelligent teaching systems and artificial intelligence, in facilitating self-regulated learning strategies in both classroom settings and distance education. Additionally, it details how to support self-regulated learning by providing personalized learning paths and real-time feedback.