Using Hidden Markov Models to Characterize Student Behaviors in Learning-by-Teaching Environments

  • Hogyeong Jeong
  • Amit Gupta
  • Rod Roscoe
  • John Wagster
  • Gautam Biswas
  • Daniel Schwartz
Conference paper

DOI: 10.1007/978-3-540-69132-7_64

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5091)
Cite this paper as:
Jeong H., Gupta A., Roscoe R., Wagster J., Biswas G., Schwartz D. (2008) Using Hidden Markov Models to Characterize Student Behaviors in Learning-by-Teaching Environments. In: Woolf B.P., Aïmeur E., Nkambou R., Lajoie S. (eds) Intelligent Tutoring Systems. ITS 2008. Lecture Notes in Computer Science, vol 5091. Springer, Berlin, Heidelberg

Abstract

Using hidden Markov models (HMMs) and traditional behavior analysis, we have examined the effect of metacognitive prompting on students’ learning in the context of our computer-based learning-by-teaching environment. This paper discusses our analysis techniques, and presents evidence that HMMs can be used to effectively determine students’ pattern of activities. The results indicate clear differences between different interventions, and links between students learning performance and their interactions with the system.

Keywords

Learning by Teaching environments Metacognition Behavior Analysis hidden Markov modeling 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Hogyeong Jeong
    • 1
  • Amit Gupta
    • 1
  • Rod Roscoe
    • 1
  • John Wagster
    • 1
  • Gautam Biswas
    • 1
  • Daniel Schwartz
    • 2
  1. 1.Vanderbilt UniversityNashvilleUSA
  2. 2.2Stanford UniversityNashvilleUSA

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