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Early prediction of student goals an...

North Carolina State University.

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Early prediction of student goals and affect in narrative-centered learning environments.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Early prediction of student goals and affect in narrative-centered learning environments./
作者:	Lee, Sunyoung.
面頁冊數:	98 p.
附註:	Advisers: James C. Lester; Carla D. Savage.
Contained By:	Dissertation Abstracts International69-09B.
標題:	Computer Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoeng/servlet/advanced?query=3329207
ISBN:	9780549820642

Early prediction of student goals and affect in narrative-centered learning environments.
Lee, Sunyoung.

Early prediction of student goals and affect in narrative-centered learning environments. - 98 p.

Advisers: James C. Lester; Carla D. Savage.

Thesis (Ph.D.)--North Carolina State University, 2008.

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.

ISBN: 9780549820642Subjects--Topical Terms:

626642
Computer Science.

Early prediction of student goals and affect in narrative-centered learning environments.
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245 1 0 $a Early prediction of student goals and affect in narrative-centered learning environments.
300 $a 98 p.
500 $a Advisers: James C. Lester; Carla D. Savage.
500 $a Source: Dissertation Abstracts International, Volume: 69-09, Section: B, page: 5538.
502 $a Thesis (Ph.D.)--North Carolina State University, 2008.
520 $a 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.
520 $a 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.
520 $a 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.
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