Computational Psychometrics: Analyzing Educational Behavior Using Machine Learning
Abstract
The integration of machine learning (ML) in psychometrics has enabled a deeper understanding of learners' cognitive and behavioral patterns. This paper presents a computational approach to analyzing educational behavior using ML techniques, including clustering, classification, and deep learning models. We examine datasets from online learning platforms to identify trends in student engagement, performance, and adaptability. A comparative analysis with traditional psychometric methods is conducted to highlight the advantages of ML-driven insights. The study aims to improve personalized learning experiences by enhancing predictive accuracy and adaptive learning strategies.
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