A Comprehensive Analysis of Learning Behaviour Characteristics and Predictive Modeling of Learning Outcomes to Enhance College-Level Academic Performance

Abstract

Understanding learner behavior has become increasingly important in modern higher education systems, where digital learning platforms generate vast quantities of student interaction data. This study presents an integrated framework for analyzing learning behaviour characteristics and predicting academic performance using machine learning and educational data mining techniques. Through behavioral feature profiling, engagement pattern detection, and predictive modeling, the system aims to identify at-risk learners early and recommend personalized interventions. The proposed approach leverages transfer learning, deep neural architectures, and interpretable analytics to improve prediction reliability and institutional decision-making. The findings demonstrate that incorporating behavioural metrics such as activity frequency, resource utilization patterns, and cognitive engagement signals significantly enhances prediction accuracy compared to traditional grade-based systems [1], [4], [7]. Ultimately, the research provides a data-driven pathway to improving academic success rates and optimizing college learning environments