Evaluating Bayesian networks’ precision for detecting students’ learning styles

• Published in: Computers and education 2007-11, Vol.49 (3), p.794-808
• Main Authors: García, Patricio, Amandi, Analía, Schiaffino, Silvia, Campo, Marcelo
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Bayesian networks; Bayesian Statistics; Cognitive Style; e-Learning; Educational Technology; Evaluation Research; Internet; Learning Processes; Learning styles; Student Behavior; Student modeling; Web Based Instruction
• ISSN: 0360-1315; 1873-782X
• DOI: 10.1016/j.compedu.2005.11.017

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.