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Self-regulated Learning with MetaTutor: Advancing the Science of Learning with MetaCognitive Tools

  • Roger Azevedo
  • Amy Johnson
  • Amber Chauncey
  • Candice Burkett
Chapter

Abstract

The key to understanding complex learning with advanced learning technologies (e.g., hypermedia) lies in our ability to comprehend the temporal deployment of students’ cognitive, metacognitive, motivational, and affective processes. Our chapter will focus on critically analyzing the use of mixed-method approaches to analyze the complex nature of self-regulated learning (SRL) during hypermedia learning. We will use examples from our own research (e.g., Azevedo 2008, Recent innovations in educational technology that facilitate student learning (pp. 127–156); Azevedo & Witherspoon, in press, Handbook of metacognition in education) and that of others (e.g., Biswas et al., 2005; Schwartz et al., in press; Winne & Nesbitt, in press, Handbook of metacognition in education) to present and discuss the strengths and weaknesses in using mixed methods to capture, model, trace, and infer the unfolding SRL processes during learning with nonlinear, multirepresentational computerized environments. The chapter will focus on the methods, and quantitative and qualitative analyses used to converge product data (e.g., learning outcomes), process data (e.g., think-aloud data), and log-file data collected during learning, develop coding schemes to categorize and infer the deployment of SRL processes, and the use of computational tools to examine learners’ behaviors and navigation paths. Lastly, we will present a theoretical model that integrates the various topics presented in this chapter that will guide future research and educational practices for fostering students’ SRL with hypermedia environments.

Keywords

Metacognitive Process Metacognitive Monitoring Metacognitive Judgment Human Tutor Adaptive Scaffolding 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The research presented in this paper has been supported by funding from the National Science Foundation (Early Career Grant DRL 0133346, DRL 0633918, DRL 0731828, HCC 0841835) awarded to the first author. The authors thank M. Cox, A. Fike, and R. Anderson for collection of data, transcribing, and data scoring. The authors would also like to thank M. Lintean, Z. Cai, V. Rus, A. Graesser, and D. McNamara for design and development of MetaTutor.

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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Roger Azevedo
    • 1
  • Amy Johnson
    • 1
  • Amber Chauncey
    • 1
  • Candice Burkett
    • 1
  1. 1.Cognition and Technology Research Lab, Department of Psychology, Institute for Intelligent SystemsUniversity of MemphisMemphisUSA

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