Reza Feyzi-Behnagh

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.

Inferring Learning from Gaze Data during Interaction with an Environment to Support Self-Regulated Learning

In this paper, we explore the potential of gaze data as a source of information to predict learning as students interact with MetaTutor, an ITS that scaffolds self-regulated learning. Using data from 47 college students, we show that a classifier using a variety of gaze features achieves considerable accuracy in predicting student learning after seeing gaze data from the complete interaction. We also show promising results on the classifier ability to detect learning in real-time during interaction.

Dysregulated Learning with Advanced Learning Technologies

Successful learning with advanced learning technologies is based on the premise that learners adaptively regulate their cognitive and metacognitive behaviors during learning. However, there is abundant empirical evidence that suggests that learners typically do not adaptively modify their behavior, thus suggesting that they engage in what is called dysregulated behavior. Dysregulated learning is a new term that is used to describe a class of behaviors that learners use that lead to minimal learning. Examples of dysregulated learning include failures to: (1) encode contextual demands, (2) deploy effective learning strategies, (3) modify and update internal standards, (4) deal with the dynamic nature of the task, (5) metacognitive monitor the use of strategies and repeatedly make accurate metacognitive judgments, and (6) intelligently adapt behavior during learning so as to maximize learning and understanding of the instructional material. Understanding behaviors associated with dysregulated learning is critical since it has implications for determining what they are, when they occur, how often they occur, and how they can be corrected during learning.