International Research Journal of Education and Technology

The rapid growth of digital technology has significantly increased the use of electronic gadgets such as smartphones, laptops, and gaming devices among students. Although these devices support learning and communication, excessive usage may lead to gadget addiction, sleep disturbances, reduced academic performance, and increased stress levels. This study proposes a machine learning–based system to predict electronic gadget addiction among students using behavioral analysis. The system collects data through questionnaires that evaluate screen time, social media usage, gaming habits, sleep duration, study hours, and stress levels.

Keywords: Gadget Addiction, Machine Learning, Digital Stress, Student Behavior Analysis, Sentiment Analysis, Flask