Learning styles' recognition in e-learning environments with feed-forward neural networks

JORGE VILLAVERDE; DANIELA GODOY; ANALIA AMANDI

JOURNAL OF COMPUTER ASSISTED LEARNING

Blackwell Publishing

Lugar: Hoboken, USA; Año: 2006 vol. 22 p. 197 - 206

0266-4909

People have unique ways of learning, which may greatly affect the learning process and, therefore, its outcome. In order to be effective, e-learning systems should be capable of adapting the content of courses to the individual characteristics of students. In this regard, some educational systems have proposed the use of questionnaires for determining a student learning style; and then adapting their behaviour according to the students´ styles. However, the use of questionnaires is shown to be not only a time-consuming investment but also an unreliable method for acquiring learning style characterisations. In this paper, we present an approach to recognize automatically the learning styles of individual students according to the actions that he or she has performed in an e-learning environment. This recognition technique is based upon feed-forward neural networks.