Predicting and Analyzing Student Exam Behavior with Distributed Machine Learning

Examinations are pivotal milestones in educational settings, serving not just as a measure of academic performance but also as a complex behavioral phenomenon. While traditional studies have explored student behaviors during examinations, the intricate, high-dimensional nature of such behaviors often escapes conventional analytical approaches. This paper introduces a novel methodology that employs Distributed Machine Learning techniques to analyze student behavior during exams compre- hensively. We design a system architecture that captures various behavioral metrics, and apply distributed machine learning algorithms to dissect this multi-faceted data. The findings offer actionable insights into optimizing examination processes and provide a pioneering framework for leveraging advanced com- putational techniques in educational psychology. Our empirical results demonstrate a substantial improvement in understanding student behaviors, potentially influencing educational policies and pedagogical strategies.