Evaluating Bayesian networks' precision for detecting students' learning styles

Authors:
Patricio García;Analía Amandi;Silvia Schiaffino;Marcelo Campo
Affiliations:
ISISTAN Research Institute, Facultad de Cs. Exactas, UNCPBA, Campus Universitario, Paraje Arroyo Seco, Tandil 7000, Buenos Aires, Argentina;ISISTAN Research Institute, Facultad de Cs. Exactas, UNCPBA, Campus Universitario, Paraje Arroyo Seco, Tandil 7000, Buenos Aires, Argentina and CONICET, Comisión Nacional de Investigaciones C ...;ISISTAN Research Institute, Facultad de Cs. Exactas, UNCPBA, Campus Universitario, Paraje Arroyo Seco, Tandil 7000, Buenos Aires, Argentina and CONICET, Comisión Nacional de Investigaciones C ...;ISISTAN Research Institute, Facultad de Cs. Exactas, UNCPBA, Campus Universitario, Paraje Arroyo Seco, Tandil 7000, Buenos Aires, Argentina and CONICET, Comisión Nacional de Investigaciones C ...
Venue:
Computers & Education
Year:
2007

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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.