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<p>This special issue examines advances in the measurement and support of self-regulated learning (SRL), emphasizing the integration of multimodal data and artificial intelligence (AI) in educational contexts. SRL is a goal-directed process in which learners plan, monitor, and control their learning, influenced by the interplay of cognition, affect, metacognition, and motivation (CAMM). Traditional methods in educational psychology, such as self-reports and interviews, often fall short of capturing the dynamic and recursive nature of SRL. Recent research employs multimodal data and process-oriented approaches to better understand the complex interactions among CAMM processes. The self-regulated learning, multimodal data, and analysis grid (SMA) is used as a framework for mapping and analyzing these processes across diverse data streams. The special issue includes three review papers and two empirical studies that illustrate the benefits and challenges of integrating multiple data sources and analytical techniques, while emphasizing the need for reliable and valid measures to enable personalized support for SRL. Collectively, the studies provide a multidisciplinary perspective on the current state and future directions of SRL research, advocating for innovative, theory-driven approaches that leverage existing technological capabilities to empower agentic learners in digital environments.</p>

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Self-Regulated Learning, Multimodal Data, and Analysis Grid: Where Are We Now and Where Are We Going?

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月13日

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  • Joni Lämsä,
  • Susanne de Mooij,
  • Martine Baars,
  • Inge Molenaar,
  • Sanna Järvelä

摘要

This special issue examines advances in the measurement and support of self-regulated learning (SRL), emphasizing the integration of multimodal data and artificial intelligence (AI) in educational contexts. SRL is a goal-directed process in which learners plan, monitor, and control their learning, influenced by the interplay of cognition, affect, metacognition, and motivation (CAMM). Traditional methods in educational psychology, such as self-reports and interviews, often fall short of capturing the dynamic and recursive nature of SRL. Recent research employs multimodal data and process-oriented approaches to better understand the complex interactions among CAMM processes. The self-regulated learning, multimodal data, and analysis grid (SMA) is used as a framework for mapping and analyzing these processes across diverse data streams. The special issue includes three review papers and two empirical studies that illustrate the benefits and challenges of integrating multiple data sources and analytical techniques, while emphasizing the need for reliable and valid measures to enable personalized support for SRL. Collectively, the studies provide a multidisciplinary perspective on the current state and future directions of SRL research, advocating for innovative, theory-driven approaches that leverage existing technological capabilities to empower agentic learners in digital environments.