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Cognitive Processing of Cryptography Concepts: An fMRI Study

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Conference

2017 ASEE Annual Conference & Exposition

Location

Columbus, Ohio

Publication Date

June 24, 2017

Start Date

June 24, 2017

End Date

June 28, 2017

Conference Session

Emerging Computing and Information Technologies II

Tagged Division

Computing & Information Technology

Tagged Topic

Diversity

Page Count

17

Permanent URL

https://peer.asee.org/28045

Download Count

167

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Paper Authors

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Joseph William Beckman Purdue University

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Joseph Beckman is a Ph.D. student in information security at Purdue University researching cognitive processing as it applies to learning in information security.

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Melissa Jane Dark Purdue University

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Melissa Dark is W.C. Furnas Professor of Technology in the College of Technology at Purdue University. Her work is in cybersecurity teaching, learning and thinking.

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Pratik Kashyap Purdue University

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Pratik Kashyap is a PhD student in Electrical Engineering at Purdue University whose field of research is in biomedical signal and image processing.

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Sumra Bari Purdue University, School of Electrical and Computer Engineering

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Sumra Bari received the Bachelor's degree in Electrical Engineering in 2011 from the University of Engineering and Technology, Lahore, Pakistan and the Master's degree in 2015 from Purdue University, West Lafayette, IN where she is currently working towards the Ph.D. degree in School of Electrical and Computer Engineering. Her research interests include functional neuroimaging, statistical biomedical imaging and signal processing and model based image processing.

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Samuel S. Wagstaff Jr Purdue University

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Sam Wagstaff is a computer science professor at Purdue University. He works in cryptography and computational number theory. He has published four books and more than sixty papers. He taught the cryptography class on which some of the research of this article is based.

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Yingjie Chen Purdue Polytechnic Institute

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Dr. Yingjie Chen is an assistant professor in the Department of Computer Graphics Technology of Purdue University. He received his Ph.D. degree in the areas of human-computer interaction, information visualization, and visual analytics from the School of Interaction Arts and Technology at Simon Fraser University (SFU) in Canada. He earned the Bachelor degree of Engineering from the Tsinghua University in China, and a Master of Science degree in Information Technology from SFU. His research covers interdisciplinary domains of information visualization, visual analytics, digital media, and human computer interaction. He seeks to design, model, and construct new forms of interaction in visualization and system design, by which the system can minimize its influence on design and analysis, and become a true free extension of human’s brain and hand.

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Baijian Yang Purdue Polytechnic Institute

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Dr. Yang is current an Associate Professor at Department of Computer and Information Technology, Purdue University

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Abstract

This study investigated the effects of using Model Eliciting Activities that build representational fluency on the cognitive processing of selected cryptography concepts. The study used an experimental design where in the control group the cryptography concepts were taught to 5 participants using two representational forms (language and mathematics) and in the treatment group the same concepts were taught to 5 participant using four representational forms (language, mathematics, graphic and concrete). Cognitive processing was measured using Functional Magnetic Resonance Imaging (fMRI) to determine where in the brain cryptography concepts are processed and whether the use of MEAs focused on representational fluency impacted cognitive processing of cryptography concepts. fMRI image data were gathered from five volunteers by presenting multiple choice questions to the students visually and recording their responses while they were undergoing fMRI scanning. fMRI image analysis from the post-course scans showed common areas of brain activation among the ten fMRI participants that differed based on whether the questions were presented using language, math, or graphical representational forms. This paper discusses the differences in brain activation patterns resulting from each representation, as well as a direction for future work measuring cognitive processing of cryptography concepts in multiple representational forms.

Beckman, J. W., & Dark, M. J., & Kashyap, P., & Bari, S., & Wagstaff, S. S., & Chen, Y., & Yang, B. (2017, June), Cognitive Processing of Cryptography Concepts: An fMRI Study Paper presented at 2017 ASEE Annual Conference & Exposition, Columbus, Ohio. https://peer.asee.org/28045

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