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Learning Style Dynamics

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2015 ASEE Annual Conference & Exposition


Seattle, Washington

Publication Date

June 14, 2015

Start Date

June 14, 2015

End Date

June 17, 2015





Conference Session

Teaching & Learning in Graduate Programs

Tagged Division

Graduate Studies

Page Count


Page Numbers

26.1076.1 - 26.1076.23



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Paper Authors


Quintana M. Clark Purdue University, West Lafayette Indiana

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Quincy Clark, a graduate from the College of Technology at Purdue University. Her research interests include emerging technologies for teaching and learning in STEM, e-learning instructional theory and design, and social media as applied to learning styles.

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James L. Mohler Purdue University, West Lafayette


Alejandra J. Magana Purdue University, West Lafayette Orcid 16x16

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Alejandra Magana is an Assistant Professor in the Department of Computer and Information Technology and an affiliated faculty at the School of Engineering Education at Purdue University. She holds a B.E. in Information Systems, a M.S. in Technology, both from Tec de Monterrey; and a M.S. in Educational Technology and a Ph.D. in Engineering Education from Purdue University. Her research is focused on identifying how model-based cognition in STEM can be better supported by means of expert technological and computing tools such as cyberinfrastructure, cyber-physical systems, and computational modeling and simulation tools.

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Learning Style DynamicsKnowledge of an individual’s learning style dynamics might be used to further improvepersonalized learning, instruction, or educational materials. This study extends learning styletheory by demonstrating the existence of dynamics in learning style.A learning style is the type of training method an individual prefers to use in developing workingknowledge. We define learning style dynamics as the change in preferred learning styles as afunction of external factors. Such factors might include type of subject matter being studied,educational level, instructional type, interests, etc. The present study focuses on the type ofsubject matter.Prior work in this area includes the Kolb Learning Style Inventory (KLSI), which includes theidentification of nine discrete learning styles and the measurement of one’s ability to flexbetween learning styles. Flexibility measures the ability of an individual to use a different stylethan their preferred style of learning. The underlying assumption in KLSI is that an individualprefers to use only one type of learning style, independent of external factors. That is, there areno external factor questions within the KLSI survey.Therefore, our research question is: Might an individual routinely change their preferred learningstyle based on an external factor.In this study, we developed an online survey to collect dynamic learning style data from asample of 185 university students studying technology. The external factor we tested for in thesurvey was the type of subject matter; in particular, mathematics versus English. Each surveyquestion was strategically chosen so that it could be applied to both subjects. We developedcomputer algorithms to statistically analyze the survey data. Our results showed that 36 percentof the students use a different learning style between the two subject matters: mathematics versusEnglish. These statistically significant results support the existence of dynamics in learningstyles at least between the subjects of mathematics and English. These results are expected tomotivate further investigation of other external factors in learning style dynamics.

Clark, Q. M., & Mohler, J. L., & Magana, A. J. (2015, June), Learning Style Dynamics Paper presented at 2015 ASEE Annual Conference & Exposition, Seattle, Washington. 10.18260/p.24413

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