Early Prediction of Student Goals and Affect in Narrative-Centered Learning Environments.

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dc.contributor.advisor JOHN NIETFELD, Committee Member en_US
dc.contributor.advisor MUNINDAR P. SINGH, Committee Member en_US
dc.contributor.advisor R. MICHAEL YOUNG, Committee Member en_US
dc.contributor.advisor JAMES C. LESTER, Committee Co-Chair en_US
dc.contributor.advisor CARLA D. SAVAGE, Committee Co-Chair en_US
dc.contributor.author Lee, Sunyoung en_US
dc.date.accessioned 2010-04-02T18:26:12Z
dc.date.available 2010-04-02T18:26:12Z
dc.date.issued 2008-08-14 en_US
dc.identifier.other etd-08072008-144230 en_US
dc.identifier.uri http://www.lib.ncsu.edu/resolver/1840.16/3044
dc.description.abstract Recent years have seen a growing recognition of the role of goal and affect recognition in intelligent tutoring systems. Goal recognition is the task of inferring users' goals from a sequence of observations of their actions. Because of the uncertainty inherent in every facet of human computer interaction, goal recognition is challenging, particularly in contexts in which users can perform many actions in any order, as is the case with intelligent tutoring systems. Affect recognition is the task of identifying the emotional state of a user from a variety of physical cues, which are produced in response to affective changes in the individual. Accurately recognizing student goals and affect states could contribute to more effective and motivating interactions in intelligent tutoring systems. By exploiting knowledge of student goals and affect states, intelligent tutoring systems can dynamically modify their behavior to better support individual students. To create effective interactions in intelligent tutoring systems, goal and affect recognition models should satisfy two key requirements. First, because incorrectly predicted goals and affect states could significantly diminish the effectiveness of interactive systems, goal and affect recognition models should provide accurate predictions of user goals and affect states. When observations of users' activities become available, recognizers should make accurate "early" predictions. Second, goal and affect recognition models should be highly efficient so they can operate in real time. To address key issues, we present an inductive approach to recognizing student goals and affect states in intelligent tutoring systems by learning goals and affect recognition models. Our work focuses on goal and affect recognition in an important new class of intelligent tutoring systems, narrative-centered learning environments. We report the results of empirical studies of induced recognition models from observations of students' interactions in narrative-centered learning environments. Experimental results suggest that induced models can make accurate early predictions of student goals and affect states, and they are sufficiently efficient to meet the real-time performance requirements of interactive learning environments. en_US
dc.rights I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dis sertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to NC State University or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. en_US
dc.subject Intelligent Tutoring Systems en_US
dc.subject Affect Recognition en_US
dc.subject Goal Recognition en_US
dc.title Early Prediction of Student Goals and Affect in Narrative-Centered Learning Environments. en_US
dc.degree.name PhD en_US
dc.degree.level dissertation en_US
dc.degree.discipline Computer Science en_US


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