Bayesian network student modeling of novice C programmers
Intelligent Tutoring Systems must make use of student models to represent the knowledge of their users. Although there are currently several different implementations of Bayesian network student models in existence, they all model the students current state of knowledge only. They do not address the...
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oai:animorepository.dlsu.edu.ph:etd_masteral-101792022-06-01T03:56:59Z Bayesian network student modeling of novice C programmers Argao, Jose Emmanuel O. Intelligent Tutoring Systems must make use of student models to represent the knowledge of their users. Although there are currently several different implementations of Bayesian network student models in existence, they all model the students current state of knowledge only. They do not address the need to come up with a way to integrate the representation of misconceptions that may exist in a students mind into their network. Because the presence of misconceptions can affect the thought processes of students, the ability to model them in a Bayesian network student model should improve that models ability to make approximations about the user. The aim of this research is to design a Bayesian network student model for Novice C Programmers. Keywords: Intelligent Tutoring System, Bayesian network, Student Model, Misconception 2005-12-10T08:00:00Z text application/pdf https://animorepository.dlsu.edu.ph/etd_masteral/3341 https://animorepository.dlsu.edu.ph/cgi/viewcontent.cgi?article=10179&context=etd_masteral Master's Theses English Animo Repository Bayesian statistical decision theory Intelligent tutoring systems Computer programs Computer Sciences |
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Bayesian statistical decision theory Intelligent tutoring systems Computer programs Computer Sciences Argao, Jose Emmanuel O. Bayesian network student modeling of novice C programmers |
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Intelligent Tutoring Systems must make use of student models to represent the knowledge of their users. Although there are currently several different implementations of Bayesian network student models in existence, they all model the students current state of knowledge only. They do not address the need to come up with a way to integrate the representation of misconceptions that may exist in a students mind into their network. Because the presence of misconceptions can affect the thought processes of students, the ability to model them in a Bayesian network student model should improve that models ability to make approximations about the user. The aim of this research is to design a Bayesian network student model for Novice C Programmers.
Keywords: Intelligent Tutoring System, Bayesian network, Student Model, Misconception |
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Argao, Jose Emmanuel O. |
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Argao, Jose Emmanuel O. |
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Argao, Jose Emmanuel O. |
title |
Bayesian network student modeling of novice C programmers |
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Bayesian network student modeling of novice C programmers |
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Bayesian network student modeling of novice C programmers |
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Bayesian network student modeling of novice C programmers |
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Bayesian network student modeling of novice C programmers |
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bayesian network student modeling of novice c programmers |
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2005 |
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https://animorepository.dlsu.edu.ph/etd_masteral/3341 https://animorepository.dlsu.edu.ph/cgi/viewcontent.cgi?article=10179&context=etd_masteral |
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