Ranking feature sets for emotion models used in classroom based intelligent tutoring systems

David G. Cooper, Kasia Muldner, Ivon Arroyo, Beverly Park Woolf, Winslow Burleson

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

Abstract

Recent progress has been made by using sensors with Intelligent Tutoring Systems in classrooms in order to predict the affective state of students users. If tutors are able to interpret sensor data with new students based on past experience, rather than having to be individually trained, then this will enable tutor developers to evaluate various methods of adapting to each student's affective state using consistent predictions. In the past, our classifiers have predicted student emotions with an accuracy between 78% and 87%. However, it is still unclear which sensors are best, and the educational technology community needs to know this to develop better than baseline classifiers, e.g. ones that use only frequency of emotional occurrence to predict affective state. This paper suggests a method to clarify classifier ranking for the purpose of affective models. The method begins with a careful collection of a training and testing set, each from a separate population, and concludes with a non-parametric ranking of the trained classifiers on the testing set. We illustrate this method with classifiers trained on data collected in the Fall of 2008 and tested on data collected in the Spring of 2009. Our results show that the classifiers for some affective states are significantly better than the baseline model; a validation analysis showed that some but not all classifier rankings generalize to new settings. Overall, our analysis suggests that though there is some benefit gained from simple linear classifiers, more advanced methods or better features may be needed for better classification performance.

Original languageEnglish (US)
Title of host publicationUser Modeling, Adaptation, and Personalization - 18th International Conference, UMAP 2010, Proceedings
Pages135-146
Number of pages12
DOIs
StatePublished - 2010
Event18th International Conference on User Modeling, Adaptation and Personalization, UMAP 2010 - Big Island, HI, United States
Duration: Jun 20 2010Jun 24 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6075 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other18th International Conference on User Modeling, Adaptation and Personalization, UMAP 2010
CountryUnited States
CityBig Island, HI
Period6/20/106/24/10

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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