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

4 Citations (Scopus)

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 publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages766-769
Number of pages4
Volume7926 LNAI
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)03029743
ISSN (Electronic)16113349

Other

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

Fingerprint

Task Modeling
Human computer interaction
Detector
Students
Detectors
Interaction
User Modeling
Intelligent Tutoring Systems
Datalog
Gaming
Intelligent systems
Model
Coding
Classify
Software
Human
Modeling

Keywords

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

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7926 LNAI, 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

Using HCI task modeling techniques to measure how deeply students model. / Girard, Sylvie; Zhang, Lishan; Hidalgo-Pontet, Yoalli; VanLehn, Kurt; Burleson, Winslow; Chavez-Echeagaray, Maria Elena; Gonzalez-Sanchez, Javier.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7926 LNAI 2013. p. 766-769 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7926 LNAI).

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

Girard, S, Zhang, L, Hidalgo-Pontet, Y, VanLehn, K, Burleson, W, Chavez-Echeagaray, ME & Gonzalez-Sanchez, J 2013, Using HCI task modeling techniques to measure how deeply students model. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 7926 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7926 LNAI, pp. 766-769, 16th International Conference on Artificial Intelligence in Education, AIED 2013, Memphis, TN, United States, 7/9/13. https://doi.org/10.1007/978-3-642-39112-5-108
Girard S, Zhang L, Hidalgo-Pontet Y, VanLehn K, Burleson W, Chavez-Echeagaray ME et al. Using HCI task modeling techniques to measure how deeply students model. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7926 LNAI. 2013. p. 766-769. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-39112-5-108
Girard, Sylvie ; Zhang, Lishan ; Hidalgo-Pontet, Yoalli ; VanLehn, Kurt ; Burleson, Winslow ; Chavez-Echeagaray, Maria Elena ; Gonzalez-Sanchez, Javier. / Using HCI task modeling techniques to measure how deeply students model. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7926 LNAI 2013. pp. 766-769 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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