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International Handbook of Metacognition and Learning Technologies pp 427–449Cite as

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

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Part of the book series: Springer International Handbooks of Education ((SIHE,volume 28))

Abstract

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.

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Acknowledgements

The research presented in this chapter has been supported by funding from the National Science Foundation (DRL 0633918 and IIS 0841835) and the Social Sciences and Humanities Research Council of Canada (430-2011-0170) awarded to the first author and (DRL 1008282) awarded to the last author.

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  1. Laboratory for the Study of Metacognition and Advanced Learning Technologies, Department of Educational and Counselling Psychology, McGill University, 3700 McTavish Street, Montreal, QC, Canada, H3A 1Y2

    Roger Azevedo Ph.D., Jason Harley, Gregory Trevors, Melissa Duffy, Reza Feyzi-Behnagh & François Bouchet

  2. Illinois Institute of Technology, College of Psychology, 3105 S. Dearborn St., Life Sciences #252, Chicago, IL, 60616, USA

    Ronald Landis

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Correspondence to Roger Azevedo Ph.D. .

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  1. Department of Educational and Counseling, McGill University Department of Educational and Counseling, Montreal, Québec, Canada

    Roger Azevedo

  2. Human-Computer Interaction Inst., Carnegie Mellon University Human-Computer Interaction Inst., Pittsburgh, Pennsylvania, USA

    Vincent Aleven

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Azevedo, R. et al. (2013). Using Trace Data to Examine the Complex Roles of Cognitive, Metacognitive, and Emotional Self-Regulatory Processes During Learning with Multi-agent Systems. In: Azevedo, R., Aleven, V. (eds) International Handbook of Metacognition and Learning Technologies. Springer International Handbooks of Education, vol 28. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-5546-3_28

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