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Identification of Effective Learning Behaviors

  • Paul Salvador Inventado
  • Roberto Legaspi
  • Rafael Cabredo
  • Koichi Moriyama
  • Ken-ichi Fukui
  • Satoshi Kurihara
  • Masayuki Numao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7926)

Abstract

Self-regulated learners have been shown to learn more effectively. However, it is not easy to become self-regulated because learners have to be capable of observing and evaluating their thoughts, actions and behaviors while learning. In this work, we used Q-learning to reveal the effectiveness or ineffectiveness of a learning behavior that carries over learning episodes. We also showed different types of effective learning behavior discovered and how they were differentiated. Providing learners with knowledge about learning behavior effectiveness can help them observe how strategy selection affects their performance and will help them select more appropriate strategies in succeeding learning episodes for better future performance.

Keywords

Learning Strategy Learning Behavior Data Drive Approach Goal Related Activity Cognitive Disequilibrium 
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.

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References

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    Zimmerman, B.J.: Becoming a Self-Regulated learner: An overview. Theory into Practice 41(2), 64–70 (2002)MathSciNetCrossRefGoogle Scholar
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    Zimmerman, B.J.: Self-regulated learning and academic achievement: An overview. Educational Psychologist 25(1), 3–17 (1990)MathSciNetCrossRefGoogle Scholar
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    Inventado, P.S., Legaspi, R., Numao, M.: Student learning behavior in an unsupervised learning environment. In: Proceedings of the 20th International Conference on Computers in Education, pp. 730–737 (2012)Google Scholar
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    D’Mello, S., Graesser, A.: Dynamics of affective states during complex learning. Learning and Instruction 22(2), 145–157 (2012)CrossRefGoogle Scholar
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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Paul Salvador Inventado
    • 1
    • 2
  • Roberto Legaspi
    • 1
  • Rafael Cabredo
    • 1
    • 2
  • Koichi Moriyama
    • 1
  • Ken-ichi Fukui
    • 1
  • Satoshi Kurihara
    • 1
  • Masayuki Numao
    • 1
  1. 1.The Institute of Scientific and Industrial ResearchOsaka UniversityIbarakiJapan
  2. 2.Center for Empathic Human-Computer Interface, College of Computer StudiesDe La Salle UniversityManilaPhilippines

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