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

This chapter emphasizes the importance of using multi-channel trace data to examine the complex roles of cognitive, affective, and metacognitive (CAM) self-regulatory processes deployed by students during learning with multi-agent systems. We argue that tracing these processes as they unfold in real-time is key to understanding how they contribute both individually and together to learning and problem solving. In this chapter we describe MetaTutor (a multi-agent, intelligent hypermedia system) and how it can be used to facilitate learning of complex biological topics and as a research tool to examine the role of CAM processes used by learners. Following a description of the theoretical perspective and underlying assumptions of self-regulated learning (SRL) as an event, we provide empirical evidence from five different trace data, including concurrent think-alouds, eye-tracking, note taking and drawing, log-files, and facial recognition, to exemplify how these diverse sources of data help understand the complexity of CAM processes and their relation to learning. Lastly, we provide implications for future research of advanced leaning technologies (ALTs) that focus on examining the role of CAM processes during SRL with these powerful, yet challenging, technological environments.