(676h) Impact of Learning Style Preferences On Student Performance and Perception in Chemical Engineering

Authors: 
Miskioglu, E., The Ohio State University
Wood, D. W., Ohio State University



Studies on learning styles are largely focused on the use of hypermedia, rather than traditional classroom lectures, as a teaching technique. The validity of learning styles indicators has also been a subject of debate in education. Felder and Silverman established the Felder-Silverman learning styles, which led to the Felder-Solomon Index of Learning Styles (ILS), in the context of engineering education specifically. The Felder-Silverman model identifies five dimensions of learning styles: active/reflective, sensing/intuitive, visual/auditory (verbal), sequential/global, and inductive/deductive. Inductive/deductive was later removed and the model is currently accepted as having four dimensions. The ILS is a 44-question survey designed to identify learning preferences in these four dimensions.

Learning styles are emphasized as being preferences for how an individual learns and indicators of how studying should be best approached, rather than indicators of success. This has not, however, prevented a few researchers from exploring whether or not student performance can be dependent on matching question type to learning style. A study by Cook, et al., with medical students failed to establish a connection between learning style and performance. The authors offer a number of reasons why no correlation may have been established, including small sample size and the inherently high inclination of the sample population to succeed. Also of consideration is the fact that this study was conducted with students in an advanced point in their educations, and that preferences can be influenced (adapted) by education.  For these reasons, we are interested in the correlations between learning style preferences, assigned problem or task perception, and student performance in the context of an introductory chemical engineering material balances course. Students in this class come from a variety of educational backgrounds (differing high schools and an array of general education electives) and their learning style preferences are not yet influenced by a shared chemical engineering curriculum.

Assignments have a natural degree of bias towards specific learning style components, and we are looking to see if student preferences correlate with their performance and perceptions when these biases are taken into consideration (e.g. whether students who have a visual preference over verbal perform better on and prefer homework problems with information presented visually rather than verbally). The sample size of this study is approximately 100 students per semester. Data collected include student grades by specific task/problem and self-efficacy survey responses. Surveys are administered with weekly homework assignments, as well as exam problems, with questions & statements evaluating student responses to problems based on a Likert scale. Surveys include items such as: I understood how to solve problem 5 immediately; I am confident in my answer for problem 5; I am uncertain I solved problem 5 correctly; I am comfortable solving problems similar to problem 5. Results will provide evidence regarding whether learning styles can affect student performance, as well as identify naturally occurring biases in chemical engineering problems that are translatable to other fields. It is commonly accepted that every field attracts a certain “type” of individual, but a correlation between learning styles and success that demonstrates similarities among students who do not succeed in introductory chemical engineering would suggest that our curriculum repels students otherwise interested in the field.  A better understanding of student background and learning preferences is invaluable in developing improved curriculum, especially in the ever-changing modern classroom. 

This study is designed to have minimal impact on the course. The only thing we have added is the survey. By evaluating the traditionally run lecture course as it is, we are able to establish a baseline for understanding the role of learning styles in our curriculum.