Analysis of Learning Styles of Undergraduate Science, Engineering, and Technology Students

Presenter Information

Sarah M. Painter, Minnesota State University, Mankato

Location

CSU 253/4/5

Start Date

4-4-2011 1:30 PM

End Date

4-4-2011 3:00 PM

Student's Major

Mathematics and Statistics

Student's College

Science, Engineering and Technology

Mentor's Name

Rebecca Bates

Mentor's Department

Integrated Engineering

Mentor's College

Science, Engineering and Technology

Description

The Felder-Silverman Learning Style Model maps individuals‘ learning preferences on five tiers: sensory versus intuitive perceptions, visual versus verbal input, inductive versus deductive organization, active versus reflective processing, and sequential versus global understanding. The Index of Learning Styles assessment tool provides online feedback about these tiers (excluding inductive versus deductive organization). The tool has been validated for use in engineering education and allows students to better understand how to get the most out of their classes. Existing data collected from over 250 students in multiple majors over the course of three years has been collected and analyzed to show relationships between learning styles and demographic factors. The majors include automotive engineering technology, electrical engineering, computer science, civil engineering, and mechanical engineering. The analysis performed shows correlation information between learning style results and major programs, parental education levels, ethnicities, and grade point averages. There are substantial differences on the active versus reflexive and sensing versus intuitive axes across different majors. Information about aggregate learning styles for class and major groups can be distributed to faculty in the studied majors in order to provide information about the breadth of learning styles in their classes. Faculty could then make adjustments to their style of teaching to better match the variety of student needs in their classrooms.