This thesis examines the interaction between students' awareness of cybersecurity and quantitative mathematics modeling applications in gamified teaching environments. The primary reason is to utilize statistical fundamentals, mathematical logic, and quantitative modeling to analyze the knowledge, perceptions, and how students are prepared to identify and manage cyber threats. It presents a mathematical view on how information and knowledge can be used to create a model of sense and ability to make students create those models and systems, and to incorporate probability analysis, matrices, and logical algorithms to assess the level of sensitivity about cybersecurity. All the studies conducted in the thesis demonstrate a view and the student concept to know and find practical information from peers, to show the mathematical models, and to offer solutions on how to teach computational thinking. Also, the study will show the way students think that this computational science applies to, to understand if this method should be applied earlier in their school time, or if it will be perfectly suited for them in the high school curriculum only. This view integrates the mathematical concept and artificial intelligence to create a gamified environment in which students can practice critical thinking and database analysis, to make education about cybersecurity more effective. The outcome provides a strong base for strategy development and combines strong analytical skills, mathematical logic, and awareness about cybersecurity.
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Quantitative Modeling of Teen Cybersecurity Awareness in Gamified Learning
Published:
04 June 2026
by MDPI
in The 2nd International Online Conference on Mathematics and Applications
session Mathematics, Computer Science and Artificial Intelligence
Abstract:
Keywords: Quantitative modeling; Teen cybersecurity awareness; Gamified learning; Mathematical logic; Adolescent education; Cybersecurity risks; Mathematical reasoning; Awareness development; Educational technology; Quantitative assessment; Student knowledge gaps;
