Computational Psychometrics: Analyzing Educational Behavior in Learners Using Machine Learning
Abstract
The application of machine learning (ML) in education has unlocked new possibilities for assessing learner behavior, engagement, and performance. This paper explores computational psychometrics, leveraging ML algorithms to analyze educational behavior patterns in diverse learning environments. We propose an innovative model that integrates supervised and unsupervised learning techniques to identify key behavioral indicators, predict academic outcomes, and personalize learning interventions. Through extensive experiments on real-world educational datasets, we demonstrate the efficacy of our approach in improving learning experiences and educational decision-making. Our research highlights the potential of ML-driven psychometric analysis in optimizing student engagement and curriculum design.
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