Using HCI task modeling techniques to measure how deeply students model

Sylvie Girard, Lishan Zhang, Yoalli Hidalgo-Pontet, Kurt Vanlehn, Winslow Burleson, Maria Elena Chavez-Echeagaray, Javier Gonzalez-Sanchez

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

Abstract

User modeling in AIED has been extended in the past decades to include affective and motivational aspects of learner's interaction in intelligent tutoring systems. In order to study those factors, various detectors have been created that classify episodes in log data as gaming, high/low effort on task, robust learning, etc. In this article, we present our method for creating a detector of shallow modeling practices within a meta-tutor instructional system. The detector was defined using HCI (human-computer interaction) task modeling as well as a coding scheme defined by human coders from past users' screen recordings of software use. The detector produced classifications of student behavior that were highly similar to classifications produced by human coders with a kappa of.925.

Original languageEnglish (US)
Title of host publicationArtificial Intelligence in Education - 16th International Conference, AIED 2013, Proceedings
Pages766-769
Number of pages4
DOIs
StatePublished - 2013
Event16th International Conference on Artificial Intelligence in Education, AIED 2013 - Memphis, TN, United States
Duration: Jul 9 2013Jul 13 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7926 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other16th International Conference on Artificial Intelligence in Education, AIED 2013
CountryUnited States
CityMemphis, TN
Period7/9/137/13/13

Keywords

  • Human-computer interaction
  • Intelligent tutoring system
  • Robust learning
  • Shallow learning
  • Task modeling

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Girard, S., Zhang, L., Hidalgo-Pontet, Y., Vanlehn, K., Burleson, W., Chavez-Echeagaray, M. E., & Gonzalez-Sanchez, J. (2013). Using HCI task modeling techniques to measure how deeply students model. In Artificial Intelligence in Education - 16th International Conference, AIED 2013, Proceedings (pp. 766-769). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7926 LNAI). https://doi.org/10.1007/978-3-642-39112-5-108