Using multi-level models to assess data from an intelligent tutoring system

Jennifer L. Weston, Danielle S. McNamara

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

Abstract

Intelligent tutoring systems yield data with many properties that render it potentially ideal to examine using multi-level models (MLM). Repeated observations with dependencies may be optimally examined using MLM because it can account for deviations from normality. This paper examines the applicability of MLM to data from the intelligent tutoring system Writing-Pal using intraclass correlations. Further analyses were completed to assess the impact of individual differences on daily essay scores along with the differential impact of daily vs. mean attitudinal ratings.

Original languageEnglish (US)
Title of host publicationProceedings of the 6th International Conference on Educational Data Mining, EDM 2013
EditorsSidney K. D'Mello, Rafael A. Calvo, Andrew Olney
PublisherInternational Educational Data Mining Society
ISBN (Electronic)9780983952527
StatePublished - Jan 1 2013
Event6th International Conference on Educational Data Mining, EDM 2013 - Memphis, United States
Duration: Jul 6 2013Jul 9 2013

Publication series

NameProceedings of the 6th International Conference on Educational Data Mining, EDM 2013

Conference

Conference6th International Conference on Educational Data Mining, EDM 2013
CountryUnited States
CityMemphis
Period7/6/137/9/13

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Keywords

  • Intelligent tutoring systems
  • Multi-Level models
  • Writing

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems

Cite this

Weston, J. L., & McNamara, D. S. (2013). Using multi-level models to assess data from an intelligent tutoring system. In S. K. D'Mello, R. A. Calvo, & A. Olney (Eds.), Proceedings of the 6th International Conference on Educational Data Mining, EDM 2013 (Proceedings of the 6th International Conference on Educational Data Mining, EDM 2013). International Educational Data Mining Society.