Design and Behavioral Analysis of Students during Examinations using Distributed Machine Learning

This research focuses on the design and behavioral analysis of students during examinations through the utilization of Distributed Machine Learning (DML). With the increasing integration of technology in education, the ability to monitor and understand student behavior during exams has become crucial for educators and institutions. DML offers a powerful approach to process and analyze data collected from various sources, such as online exams, wearable devices, and eye-tracking sensors. This study proposes a framework that employs DML techniques to gather, process, and interpret student behavioral data in real-time. The collected data encompasses factors like gaze patterns, typing speed, hesitation intervals, and physiological responses. By employing machine learning algorithms distributed across multiple nodes, the system aims to provide insights into student engagement, cognitive load, and potential instances of academic misconduct. The analysis of behavioral patterns during examinations can aid educators in adapting teaching methodologies, providing timely interventions, and enhancing the overall learning experience. The proposed approach highlights the potential of DML in the field of education and contributes to the broader discourse on leveraging technology for pedagogical improvement.