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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

31 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationIntelligent Tutoring Systems - 9th International Conference, ITS 2008, Proceedings
PublisherSpringer Verlag
Pages614-625
Number of pages12
ISBN (Print)3540691308, 9783540691303
DOIs
StatePublished - 2008
Externally publishedYes
Event9th International Conference on Intelligent Tutoring Systems, ITS 2008 - Montreal, QC, Canada
Duration: Jun 23 2008Jun 27 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5091 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other9th International Conference on Intelligent Tutoring Systems, ITS 2008
CountryCanada
CityMontreal, QC
Period6/23/086/27/08

Keywords

  • Behavior Analysis
  • Hidden Markov modeling
  • Learning by Teaching environments
  • Metacognition

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Using hidden markov models to characterize student behaviors in learning-by-teaching environments'. Together they form a unique fingerprint.

Cite this