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Bayesian expert systems and multi-agent modeling for learner-centric Web-based education

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dc.contributor.advisor Steven R. Lerman. en_US
dc.contributor.author Rabbat, Ralph R., 1978- en_US
dc.contributor.other Massachusetts Institute of Technology. Dept. of Civil and Environmental Engineering. en_US
dc.date.accessioned 2006-03-24T18:28:36Z
dc.date.available 2006-03-24T18:28:36Z
dc.date.copyright 2005 en_US
dc.date.issued 2005 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/30189
dc.description Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2005. en_US
dc.description Includes bibliographical references (leaves 140-150). en_US
dc.description.abstract Online distance education provides students with a wealth of information. When students submit course-related search term queries, the search engine returns the search hits based on keyword and topic match. A student's particular learning style is not taken into consideration. For instance, a visually oriented student may benefit more than others from viewing videos and interacting with simulations. We address this problem by designing and developing a knowledge-based system for the initial assessment of students' learning styles. Each student's membership in a learning style dimension (e.g. visual or verbal) is estimated probabilistically. We reach this probability value by using a sequential Bayesian approach to administer a dynamic questionnaire that aims to attain a desired confidence level estimate with the minimal number of questions. A multi-agent online tutoring system uses this initial learning style model to start suggesting learning material matching the student's style. Each agent is an expert in a learning style dimension and can suggest the learning materials matching the student's style. In addition, these agents closely follow the student's evolving preferences and continuously update the stochastic model based on the student's online activities. When the student searches for course material, the multi-agent system delivers the search matches in a cycle-free preference order influenced by the students' multi-dimensional learning style model. en_US
dc.description.statementofresponsibility by Ralph Rizkallah Rabbat. en_US
dc.format.extent 150 leaves en_US
dc.format.extent 7062661 bytes
dc.format.extent 7083067 bytes
dc.format.mimetype application/pdf
dc.format.mimetype application/pdf
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. en_US
dc.rights.uri http://dspace.mit.edu/handle/1721.1/7582
dc.subject Civil and Environmental Engineering. en_US
dc.title Bayesian expert systems and multi-agent modeling for learner-centric Web-based education en_US
dc.type Thesis en_US
dc.description.degree Ph.D. en_US
dc.contributor.department Massachusetts Institute of Technology. Dept. of Civil and Environmental Engineering. en_US
dc.identifier.oclc 60686136 en_US


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