Clustering students into groups according to their learning style

This method article aims to use group technology to classify engineering students at classroom level into clusters according to their learning style preferences. The Felder and Silverman's Index Learning Style (ILS) was used to evaluate students' learning style preferences. Students were then grouped into clusters based on the similarities of their learning styles preferences by using clustering algorithms, such as complete clustering. Prior research on Learning Styles preferences in engineering education is limited in Saudi Arabia. Students' learning style preferences allows instructors to adopt suitable teaching approach. Students having same learning styles can work together in group assignments. Grouping students into clusters, we find that outlier students who having different learning styles than the rest may allow instructors to deal with them accordingly. (C) 2019 The Author(s). Published by Elsevier B.V.