A genetic algorithm approach to recognise students' learning styles

VIRGINIA YANNIBELLI; DANIELA GODOY; ANALÍA AMANDI

Interactive Learning Environments

Routledge, part of the Taylor & Francis Group

Lugar: Oxford; Año: 2006 vol. 14 p. 55 - 78

1049-4820

Learning styles encapsulate the preferences of the students, regarding how they learn. By including information about the student learning style, computer-based educational systems are able to adapt a course according to the individual characteristics of the students. In accomplishing this goal, educational systems have been mostly based on the use of questionnaires for establishing a student learning style. However, this method has shown to be not only time-consuming but also unreliable. A genetic algorithm approach to automatically identify the individual learning styles of students based on their actions while attending an academic course is presented in this paper. The application of a genetic algorithm to this domain allows us to both discover the learning styles of individual students as they attend different academic units, as well as track the changes on these styles that might occur over time.