Bayesian based adaptive question generation technique

Bayesian based adaptive question generation technique

In this paper we aim to estimate the student knowledge model in a probabilistic domain using automatic adaptively generated assessment questions. The student answers are used to estimate the actual student model. Updating and verification of the model are conducted based on the matching between the...

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Main Authors: Nabila Khodeir, Nayer Wanas, Nevin Darwish, Nadia Hegazy
Format: Article
Language:English
Published: SpringerOpen 2014-05-01
Series:Journal of Electrical Systems and Information Technology
Subjects:
Intelligent Tutoring System
Student modeling
Abduction
Adaptive assessment
Item response theory
Online Access:http://www.sciencedirect.com/science/article/pii/S2314717214000087
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spelling doaj-af95bb71858e477892165f1bfc4ff7132020-11-25T02:12:44ZengSpringerOpenJournal of Electrical Systems and Information Technology2314-71722014-05-0111101610.1016/j.jesit.2014.03.007Bayesian based adaptive question generation techniqueNabila Khodeir0Nayer Wanas1Nevin Darwish2Nadia Hegazy3Informatics Department, Electronics Research Institute, Dokki, Giza, EgyptInformatics Department, Electronics Research Institute, Dokki, Giza, EgyptDepartment of Computer Engineering, Cairo University, Giza, EgyptInformatics Department, Electronics Research Institute, Dokki, Giza, EgyptIn this paper we aim to estimate the student knowledge model in a probabilistic domain using automatic adaptively generated assessment questions. The student answers are used to estimate the actual student model. Updating and verification of the model are conducted based on the matching between the student's and model answers. Moreover, a comparative study between using the adaptive and random generated questions for updating the student model is investigated. Results suggest that utilizing adapted generated questions increases the approximation accuracy of the student model by 40% in addition to decreasing of the required assessing questions by 35%.http://www.sciencedirect.com/science/article/pii/S2314717214000087Intelligent Tutoring SystemStudent modelingAbductionAdaptive assessmentItem response theory
collection DOAJ
language English
format Article
sources DOAJ
author Nabila Khodeir
Nayer Wanas
Nevin Darwish
Nadia Hegazy
spellingShingle Nabila Khodeir
Nayer Wanas
Nevin Darwish
Nadia Hegazy
Bayesian based adaptive question generation technique
Journal of Electrical Systems and Information Technology
Intelligent Tutoring System
Student modeling
Abduction
Adaptive assessment
Item response theory
author_facet Nabila Khodeir
Nayer Wanas
Nevin Darwish
Nadia Hegazy
author_sort Nabila Khodeir
title Bayesian based adaptive question generation technique
title_short Bayesian based adaptive question generation technique
title_full Bayesian based adaptive question generation technique
title_fullStr Bayesian based adaptive question generation technique
title_full_unstemmed Bayesian based adaptive question generation technique
title_sort bayesian based adaptive question generation technique
publisher SpringerOpen
series Journal of Electrical Systems and Information Technology
issn 2314-7172
publishDate 2014-05-01
description In this paper we aim to estimate the student knowledge model in a probabilistic domain using automatic adaptively generated assessment questions. The student answers are used to estimate the actual student model. Updating and verification of the model are conducted based on the matching between the student's and model answers. Moreover, a comparative study between using the adaptive and random generated questions for updating the student model is investigated. Results suggest that utilizing adapted generated questions increases the approximation accuracy of the student model by 40% in addition to decreasing of the required assessing questions by 35%.
topic Intelligent Tutoring System
Student modeling
Abduction
Adaptive assessment
Item response theory
url http://www.sciencedirect.com/science/article/pii/S2314717214000087
work_keys_str_mv AT nabilakhodeir bayesianbasedadaptivequestiongenerationtechnique
AT nayerwanas bayesianbasedadaptivequestiongenerationtechnique
AT nevindarwish bayesianbasedadaptivequestiongenerationtechnique
AT nadiahegazy bayesianbasedadaptivequestiongenerationtechnique
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