Investigating Engineering Student Learning Style Trends by Using Multivariate Statistical Analysis

Abstract

This study aims to use group technology to classify students at the classroom level into clusters according to their learning style preferences. Group technology is used, due to the realization that many problems are similar, and that by grouping similar problems, single solutions can be found for a set of problems. The Felder and Silverman style, and the index learning style (ILS) are used to find student learning style preferences; students are grouped into clusters based on the similarities of their preferences, by using multivariate statistical analysis. Based on the developed groups, instructors can use the proper teaching style to teach their students. The formation of clusters based on the statistical analyses of two sets of data collected from students of two classes at the same level, belonging to same engineering department indicates that each class has different learning style preferences. This is an eye-opener to educators, in that different teaching styles can be used for their students, based on the students’ learning styles, even though the students seem to have a common interest.