Gender differences and the value of choice in intelligent tutoring systems

Derek T. Green, Thomas J. Walsh, Paul R. Cohen, Carole R. Beal, Yu Han Chang

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

3 Scopus citations

Abstract

Students interacted with an intelligent tutoring system to learn grammatical rules for an artificial language. Six tutoring policies were explored. One, based on a Dynamic Bayes' Network model of skills, was learned from the performance of previous students. Overall, this policy and other intelligent policies outperformed random policies. Some policies allowed students to choose one of three problems to work on, while others presented a single problem at each iteration. The benefit of choice was not apparent in group statistics; however, there was a strong interaction with gender. Overall, women learned less than men, but they learned different amounts in the choice and no choice conditions, whereas men seemed unaffected by choice. We explore reasons for these interactions between gender, choice and learning.

Original languageEnglish (US)
Title of host publicationUser Modeling, Adaption, and Personalization - 19th International Conference, UMAP 2011, Proceedings
Pages341-346
Number of pages6
DOIs
StatePublished - Jul 19 2011
Event19th International Conference on User Modeling, Adaptation and Personalization, UMAP 2011 - Girona, Spain
Duration: Jul 11 2011Jul 15 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6787 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other19th International Conference on User Modeling, Adaptation and Personalization, UMAP 2011
CountrySpain
CityGirona
Period7/11/117/15/11

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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