SaTC-EDU: EAGER Enhancing Cybersecurity Education Through aRepresentational Fluency Model
Principal Investigator: Baijian Yang
Create Cybersecurity experts with not only deep technical skills, but also the capabilities to recognize and respond to complex and emergent behavior, as well as a “security mindset”, which includes mastery in using abstractions and principles, assessing risk and handling uncertainty, problem-solving, and reasoning; coupled with facility in adversarial thinking.
Other PIs: Dr. Melissa Dark Dr. Yingjie Chen Dr. Samuel Wagstaff
Students: Beckman Joe Sumra Bali Wenjie Wu Zhenzhi Xu Ziyang Zhang
Beckman, J.G, Bari, S.G, Chen, Y., Dark, M. J., & Yang, B. (2017). The Impacts of Representational Fluency on Cognitive Processing of Cryptography Concepts (pp. 59-67). USENIX.
Beckman, J.G, Dark, M. J., P.G, Bari, S.G, Wagstaff, S. S., Chen, Y., & Yang, B. (2017). Cognitive Processing of Cryptography Concepts: An fMRI Study. Columbus, Ohio: ASEE 2017.
Serrano Aazco, M., Magana DeLeon, A. J., Yang, B. (2016). Employing Model-Eliciting Activities in Cybersecurity Education (pp. 9). ASEE.