Understanding and Reasoning About Real-Time Cognitive, Affective, and Metacognitive Processes to Foster Self-Regulation with Advanced Learning Technologies

Self-regulated learning (SRL) involves learners’ ability to monitor and regulate their cognitive, affective, metacognitive, and motivational (CAMM2) processes and plays a critical role in learning about challenging domains while using advanced learning technologies (ALTs). Additionally, emerging empirical evidence indicates that CAM processes play an important role in learning and problem solving as well as self-regulation with ALTs. However, capturing CAM processes during learning with ALTs poses several major conceptual, theoretical, methodological, and analytical challenges. For example, researchers currently measure CAM SRL processes using several online trace methodologies, such as concurrent think-alouds, eye tracking, log files, physiological sensors, and so forth. While these methods have the potential to advance current SRL frameworks, models, and theories, they still pose serious challenges (e.g., temporal alignment of data channels, lack of analytical techniques, and accuracy of inferences made from individual channels and across data channels) that currently plague the field. Our chapter focuses on understanding and reasoning about real-time CAM processes to foster self-regulation with ALTs. (PsycInfo Database Record (c) 2022 APA, all rights reserved)