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MODELING THE LEARNER’S ATTENTION AND LEARNING GOALS
USING BAYESIAN NETWORK
by
Lei Qu
A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(COMPUTER SCIENCE)
August 2007
Copyright 2007 Lei Qu
Object Description
| Title | Modeling the learner's attention and learning goals using Bayesian network |
| Author | Qu, Lei |
| Author email | leiqu@usc.edu |
| Degree | Doctor of Philosophy |
| Document type | Thesis |
| Degree program | Computer Science |
| School | Viterbi School of Engineering |
| Date defended/completed | 2007-03-07 |
| Date submitted | 2007 |
| Restricted until | Unrestricted |
| Date published | 2007-07-12 |
| Advisor (committee chair) | Johnson, W. Lewis |
| Advisor (committee member) |
Beal, Carole Boehm, Barry Miller, Lynn Carol |
| Abstract | Intelligent Tutoring Systems (ITSs) have evolved dramatically from the simple prompt for remediation based on a wrong answer to the complex, adaptive systems of today that truly qualify as intelligent. Modern ITSs include a variety of applications that allow the emulation of a human teacher and the ITS acts as the student's private tutor, and interacts to effectively lend pedagogical assistance to the learner. Researchers have focused on modeling the learner's cognitive processes while solving problems, i.e., the "model tracing" approach. However, there is growing recognition that learning involves more than cognition, and that students' attention with an ITS also reflects "engagement" meaning the transient shifts in focus of attention and the emotions that are often associated with learning. Failure to track the learner's engagement could cause the ITSs to interrupt the learner with advice when the learner does not really need it.; The primary research questions addressed in this dissertation are (1) Can an ITS access learner's focus of attention and how? (2) If so, can learner's attention signals be used to infer learner's engagement? The research work in this dissertation provides a probabilistic model for ITSs to track learner's attention based on multiple sources of user input data. The learner's attention sequences thus were machine classified into five finite-state machines indicating guessing strategies, appropriate help use, and independent problem solving; over 90% of problem events were categorized. Students were grouped via cluster analyses based on self reports of motivation. This indicated that students learned by themselves and learned from instructions, which are two primary learning goals when students were studying in ITSs. And a Dynamic Bayesian Network (DBN) was built to enable ITSs to have the ability to detect the attention shifts and learning states. With this ability, ITSs will be more sensitive to the cognitive and motivational states of the learner and then be able to promote the learner's motivation through interaction with the learner.; To validate this Dynamic Bayesian Network Model, experiments have been designed and performed on undergraduate students at University of Southern California and high school students at Los Angeles area. The evaluation results show that applying this model should enable ITSs to track learner's attention and recognize learner's learning goals. |
| Keyword | artificial intelligence; intelligent tutoring system; dynamic Bayesian network; engagement; attention; classification |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Type | texts |
| Legacy record ID | usctheses-m614 |
| Rights | Qu, Lei |
| Repository name | Libraries, University of Southern California |
| Repository address | Los Angeles, California |
| Repository email | http://www.usc.edu/isd/libraries/services/ask_a_librarian/email/ |
| Filename | etd-Qu-20070712 |
| Archival file | uscthesesreloadpub_Volume44/etd-Qu-20070712.pdf |
Description
| Title | Page 1 |
| Full text | MODELING THE LEARNER’S ATTENTION AND LEARNING GOALS USING BAYESIAN NETWORK by Lei Qu A Dissertation Presented to the FACULTY OF THE GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (COMPUTER SCIENCE) August 2007 Copyright 2007 Lei Qu |
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