Automatic detection of Learning Styles using clustering and classification techniques

• Rafael Miranda Abreu Universidade Federal dos Vales do Jequitinhonha e Mucuri
• Cristiano Grijó Pitangui Universidade Federal de São João Del-Rei
• Alessandro Vivas Andrade Universidade Federal dos Vales do Jequitinhonha e Mucuri
• Luciana Pereira Assis Universidade Federal dos Vales do Jequitinhonha e Mucuri https://orcid.org/0000-0002-7891-7172
• Cristiano Maciel Silva Universidade Federal de São João del Rei

DOI: https://doi.org/10.5753/cbie.sbie.2020.1022

Abstract

In recent years Learning Management Systems have been widely used
to support Education. However, most of these systems provide the same content in the same way and formats to all students, which can be detrimental to the teaching/learning process. In this sense, this work, based on Felder-Silverman Learning Style model, presents an automatic approach to detect the students’ Learning Styles so that the content offered is modeled according to their preferences. The proposed model, based on clustering and classification techniques, achieved an accuracy rate up to 90% proving to be a promising approach to automatically detect the students’ learning preferences.

Keywords: learning styles, data mining, learning management systems

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