학술논문

Analysis of Learning Modalities Towards Effective Undergraduate Cybersecurity Education Design
Document Type
Conference
Source
2019 IEEE International Conference on Engineering, Technology and Education (TALE) Engineering, Technology and Education (TALE), 2019 IEEE International Conference on. :1-6 Dec, 2019
Subject
Engineering Profession
Education
Computer security
Clustering algorithms
Machine learning algorithms
Classification algorithms
Python
Intelligence augmented education
Cybersecurity education
Tailored education
VARK learning styles
Language
ISSN
2470-6698
Abstract
Cybersecurity education is a critical component of today’s computer science and IT curriculum. To provide for a highly effective cybersecurity education, we propose using machine-learning techniques to identify common learning modalities of cybersecurity students in order to optimize how cybersecurity core topics, threats, tools and techniques are taught. We test various hypothesis, e.g. that students of selected VARK learning styles will outperform their peers. The results indicate that for the class assignments in our study preference of read/write and kinesthetic modalities yielded the best results. This further indicates that specific learning instruments can be tailored for students based on their individual VARK learning styles