Evaluating Bayesian Networks' Precision for Detecting Students' Learning Styles

GARCIA, P.; ANALÍA AMANDI,; SILVIA SCHIAFFINO; MARCELO CAMPO,

COMPUTERS AND EDUCATION

Elsevier

Año: 2007 vol. 49 p. 794 - 808

0360-1315

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.