Babette Bühler

Self-regulation (SR) has emerged as a pivotal construct in educational research, yet its diverse theoretical accounts often lack comprehensive integration. This paper addresses this by proposing an integrative conceptual framework that consolidates four core research traditions on SR in educational settings, each emphasizing distinct underlying mechanisms: (1) Stable Dispositions (e.g., personality traits; Matthews et al., 2009; Roberts et al., 2007; Song et al., 2020), (2) Limited Resources (e.g., working memory capacity and executive functioning; Friedman & Miyake, 2017; Paas et al., 2003; Sweller, 1994), (3) Driving Forces (e.g., motivation, interest, and affect; Eccles & Wigfield, 2002; Hidi & Renninger, 2006; Trautwein et al., 2019), and (4) Learning Activities (e.g., self-regulated learning, cognitive and metacognitive strategies; Winne & Hadwin, 1998; 2008; Zimmerman, 2000). By synthesizing these perspectives, our framework positions SR as a dynamic, interdependent system, emphasizing how interactions and compensatory mechanisms explain individual differences and influence learning outcomes. We further argue for an integrative methodological toolkit, leveraging advanced machine learning and computational models, to capture the multi-level, temporal, and social dynamics inherent in SR. This holistic approach is crucial for advancing a comprehensive and ecologically valid understanding of SR in educational contexts.

Attention is crucial for learners to enhance their learning and build knowledge. Sustaining prolonged attention requires students to self-regulate their behavioral, emotional, and cognitive processes. At the same time, learners engage in mind wandering approximately 30% of the time spent in educational activities, leading to a deterioration in learning outcomes. Despite its prevalence, the integration of mind-wandering research with self-regulated learning (SRL) frameworks remains limited. To address this gap, we first conceptually integrated research on mind wandering and SRL. Second, we assessed the extent to which this integration has been empirically explored by situating research on mind wandering during learning within the self-regulated learning process, multimodal data, and analysis (SMA) grid. To this end, we conducted a preregistered systematic literature review. We reviewed 134 studies from 2010 to 2024, analyzing the employed definitions of mind wandering, especially with regard to meta-awareness, the data streams used for measuring mind wandering, and whether mind wandering was investigated in relation to or in combination with cognitive, affective, metacognitive, and motivational (CAMM) SRL processes. Our analysis revealed that a significant number of studies have examined mind wandering alongside cognitive and metacognitive SRL processes and predominantly using self-report measures. An equal number of studies unobtrusively measured mind wandering through multimodal data streams (e.g., eye tracking, physiology, reaction time), with 27% utilizing machine learning (ML) techniques for assessment. However, most of these reports did not include SRL procedures and only a small number of studies used an integrated analytic approach, combining multimodal assessment and including SRL processes. Overall, affective and motivational processes received little to no attention. This review demonstrates how integrating research on mind wandering into established SRL frameworks can enhance our understanding of their complex interaction and role in the learning process. It revealed a blind spot in studying mind wandering in educational settings in conjunction with SRL processes using multimodal data streams, especially regarding affective and motivational SRL processes. Additionally, the results suggest that this integration can improve the effectiveness of real-time interventions based on automated mind-wandering detection in attention-aware learning technologies, thereby supporting SRL.

Large language models have not only captivated the public imagination but have also sparked a profound rethinking of how we learn. In the third year following the breakthrough launch of ChatGPT, everyday informal learning has been transformed as diverse user groups explore these novel tools. Who is embracing LLMs for self-directed learning, and who remains hesitant? What are their reasons for adoption or avoidance? What learning patterns emerge with this novel technological landscape? We present an in-depth analysis from a large-scale survey of 776 participants, showcasing that 88% of our respondents already incorporate LLMs into their everyday learning routines for a wide variety of (learning) tasks. Young adults are at the forefront of adopting LLMs, primarily to enhance their learning experiences independently of time and space. Four types of learners emerge across learning contexts, depending on the tasks they perform with LLMs and the devices they use to access them. Interestingly, our respondents exhibit paradoxical behaviours regarding their trust in LLMs' accuracy and privacy protection measures. Our implications emphasize the importance of including different media types for learning, enabling collaborative learning, providing sources and meeting the needs of different types of learners and learning by design.