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.
Highlights
During the process of preparing our engineering program, to obtain accreditation by the NationalCommission for Academic Accreditation and Assessment, much attention has been paid to several issues, such as the program strategic plan, curricula development processes, supporting facilities and services, staff training and development, and community services
A brief discussion about Myers–Briggs-based type indicator (MBTI) styles will be covered in this study in order provide a better understanding of the dimensions used to develop the learning styles used in higher education
The classification developed by Felder and Silverman is based on answering questions for the following four dimensions:
Summary
During the process of preparing our engineering program, to obtain accreditation by the National. Many researchers studied the factors affect the learning style of students and their influence on learning preferences, which can affect students’ performance and their achievement of the intended learning outcomes. Among these factors are the students’ gender, and the level and type of education [1,2,3,4]. It was found that the learning style preference of students improves their academic performance in higher education [1,3,5,6,7,8]. The development of teaching strategies that are based on students’ learning styles is an important aspect for preparing future engineers. Several learning-style models have been designed [25,26,27]
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