Visible to the public Analysis of Learning Modalities Towards Effective Undergraduate Cybersecurity Education Design

TitleAnalysis of Learning Modalities Towards Effective Undergraduate Cybersecurity Education Design
Publication TypeConference Paper
Year of Publication2019
AuthorsChao, Henry, Stark, Benjamin, Samarah, Mohammad
Conference Name2019 IEEE International Conference on Engineering, Technology and Education (TALE)
Date PublishedDec. 2019
ISBN Number978-1-7281-2665-4
KeywordsClassification algorithms, Clustering algorithms, computer security, cyber education, cyber physical systems, cybersecurity education, Education, Intelligence augmented education, machine learning algorithms, privacy, pubcrawl, python, Tailored education, VARK learning styles
AbstractCybersecurity 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.
Citation Keychao_analysis_2019