Comparative Study of Student Academic Outcomes and Behavioral Patterns through Data-Driven Approaches

Predicting student performance and understanding behavioral patterns have become central themes in modern educational research, particularly with the rise of digital and blended learning environments. The growing availability of data from Learning Management Systems (LMS) and online learning platforms has led to a wide array of data-driven and AI-based approaches for analyzing academic outcomes and learner behaviors. This paper presents a comprehensive comparative review of existing intelligent learning models, examining how various studies utilize behavioral indicators, such as engagement, interaction patterns, and study habits, to predict student performance. By synthesizing findings across diverse methodologies and datasets, the review highlights current trends, strengths, limitations, and research gaps, offering educators and researchers valuable insights for developing more effective, data-informed student support strategies.