Using Trace Data to Examine the Complex Roles of Cognitive, Metacognitive, and Emotional Self-Regulatory Processes During Learning with Multi-agent Systems

Abstract

The widespread use of advanced learning technologies (ALTs) poses numerous challenges for learners of all ages. Learning with these non-linear, multi-representational, open-ended learn-ing environments typically involves the use of numerous self-regulatory processes, such as plan-ning, cognitive strategies, metacognitive moni-toring and regulation, emotions, and motivation. Unfortunately, learners do not always monitor and regulate these processes during learning