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Hybrid Learning Styles

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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

Exploring Graduate Student Experiences

Tagged Division

Graduate Studies

Page Count


Page Numbers

26.868.1 - 26.868.14



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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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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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Hybrid Learning StylesThis study extends learning style theory by demonstrating statistical evidence that supports theexistence of hybridity in learning styles. The eventual application of a more refined learningstyle instrument may improve the quality of instruction, learning, and educational standards byincluding personalized instructions that appeal to broader audiences. Refined learning styleindicators may also increase an individual’s knowledge of how they learn and improve theirability to learn material they previously felt was too difficult. Such attributes may eventuallyhelp to reduce the STEM achievement gap.Over the last 30 years there has been a slow and steady call for teaching and learning to embracea more personalized pedagogy that addresses an individual’s learning styles. This call reflects thelarge number of American students that fail to reach adequate levels of proficiency in K-12education. The problem is much worse for the areas of STEM, where few high school studentsgo on to obtain a degree in a STEM discipline. It is widely thought that personalized learning canbe improved by identifying an individual’s learning styles.Several models that predict an individual’s preferred learning style have been developed over thelast three decades. One of the more recent and prominent learning style indictors is the KolbLearning Style Inventory (KLSI), which is based on neuroscience and identifies nine discretelearning styles. Thousands of individuals have been tested with KLSI. However, what has notbeen studied is the possible continuum that might exist between the nine discrete learning styles.Therefore, the research question we address in this study is: Can an individual possess a hybridlearning style, which is comprised of a distribution of two or more of Kolb’s discrete learningstyles?      In this study, a new survey was developed to collect learning-ability data from a sample of 185university students studying technology. The data was analyzed using algorithms that wespecifically designed to identify and measure the hybridity in learning styles. Our analysisrevealed that 43 percent of the students did not fit into one of Kolb’s nine discrete learningstyles. These results appear to suggest that a statistically significant portion of the populationmight possess a hybrid learning style.

Clark, Q. M., & Magana, A. J. (2015, June), Hybrid Learning Styles Paper presented at 2015 ASEE Annual Conference & Exposition, Seattle, Washington. 10.18260/p.24205

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