%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 }