Students are characterized by different learning styles, focusing on different types of information and processing this information in different ways. One of the desirable characteristics of a Web-based education system is that all the students can learn despite their different learning styles. To achieve this goal we have to detect how students learn: reflecting or acting; steadily or in fits and starts; intuitively or sensitively. In this work, we evaluate Bayesian networks at detecting the learning style of a student in a Web-based education system. The Bayesian network models different aspects of a student behavior while he/she works with this system. Then, it infers his/her learning styles according to the modeled behaviors. The proposed Bayesian model was evaluated in the context of an Artificial Intelligence Web-based course. The results obtained are promising as regards the detection of students' learning styles. Different levels of precision were found for the different dimensions or aspects of a learning style.
%0 Journal Article
%1 citeulike:12034637
%A Garc'ıa, Patricio
%A Amandi, Anal'ıa
%A Schiaffino, Silvia
%A Campo, Marcelo
%C Oxford, UK, UK
%D 2007
%I Elsevier Science Ltd.
%J Comput. Educ.
%K learning-style, personalized-learning
%N 3
%P 794--808
%R 10.1016/j.compedu.2005.11.017
%T Evaluating Bayesian networks' precision for detecting students' learning styles
%U http://dx.doi.org/10.1016/j.compedu.2005.11.017
%V 49
%X Students are characterized by different learning styles, focusing on different types of information and processing this information in different ways. One of the desirable characteristics of a Web-based education system is that all the students can learn despite their different learning styles. To achieve this goal we have to detect how students learn: reflecting or acting; steadily or in fits and starts; intuitively or sensitively. In this work, we evaluate Bayesian networks at detecting the learning style of a student in a Web-based education system. The Bayesian network models different aspects of a student behavior while he/she works with this system. Then, it infers his/her learning styles according to the modeled behaviors. The proposed Bayesian model was evaluated in the context of an Artificial Intelligence Web-based course. The results obtained are promising as regards the detection of students' learning styles. Different levels of precision were found for the different dimensions or aspects of a learning style.
@article{citeulike:12034637,
abstract = {{Students are characterized by different learning styles, focusing on different types of information and processing this information in different ways. One of the desirable characteristics of a Web-based education system is that all the students can learn despite their different learning styles. To achieve this goal we have to detect how students learn: reflecting or acting; steadily or in fits and starts; intuitively or sensitively. In this work, we evaluate Bayesian networks at detecting the learning style of a student in a Web-based education system. The Bayesian network models different aspects of a student behavior while he/she works with this system. Then, it infers his/her learning styles according to the modeled behaviors. The proposed Bayesian model was evaluated in the context of an Artificial Intelligence Web-based course. The results obtained are promising as regards the detection of students' learning styles. Different levels of precision were found for the different dimensions or aspects of a learning style.}},
added-at = {2017-11-15T17:02:25.000+0100},
address = {Oxford, UK, UK},
author = {Garc'{\i}a, Patricio and Amandi, Anal'{\i}a and Schiaffino, Silvia and Campo, Marcelo},
biburl = {https://www.bibsonomy.org/bibtex/2ecb971843dfb035fa42ec21b025c9e00/brusilovsky},
citeulike-article-id = {12034637},
citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1248247},
citeulike-linkout-1 = {http://dx.doi.org/10.1016/j.compedu.2005.11.017},
doi = {10.1016/j.compedu.2005.11.017},
interhash = {8f12cf9b06974ff192493959477a5318},
intrahash = {ecb971843dfb035fa42ec21b025c9e00},
issn = {0360-1315},
journal = {Comput. Educ.},
keywords = {learning-style, personalized-learning},
month = nov,
number = 3,
pages = {794--808},
posted-at = {2013-02-18 02:56:54},
priority = {2},
publisher = {Elsevier Science Ltd.},
timestamp = {2017-11-15T17:02:25.000+0100},
title = {{Evaluating Bayesian networks' precision for detecting students' learning styles}},
url = {http://dx.doi.org/10.1016/j.compedu.2005.11.017},
volume = 49,
year = 2007
}