An Integrated Study of Student Habits, Academic Performance, and Security Awareness in Cloud, Network, and Cyber Domains

Abstract

In modern education systems where digital means predominate, the cloud computing platforms lure the students
into being inactive with academic activities on one side and on the other hand, pose different sorts of cybersecurity.
This paper proposes such a Deep Learning (DL)system achieving a unique hybridization of Gated Recurrent Units
(GRU) with Convolutional Neural Networks (CNN) for predicting academic performance from behavioural habits
with cybersecurity the dataset was pre-processed with mean imputation and min-max normalization, and the
features considered in the model included study hours, sleep patterns, use of social media, and digital security
practices. The system was shown to be perfect, with an accuracy of 98.00%, a precision of 97.12%, a recall of
96.32%, and an F1-score of 98.16%, proving its efficacy to identify at-risk students. The results further indicate a
significant relationship between cybersecurity awareness and academic performance, thus recommending
educational interventions that would combine the development of study habits with the skills of digital literacy
and security for better performance and safety in the learning environment.