An Analysis of Academic Behavior Analytics in Blended Mode of Teaching and Learning

Abstract

With the increasing adoption of online education, Blended Learning (BL) has become an essential instructional approach that integrates traditional face-to-face teaching with digital learning environments. This combination not only enhances communication between teachers and students but also improves the overall effectiveness and flexibility of the learning process. BL has gained global attention due to its potential to support student engagement and enable more accurate forecasting of academic performance. The survey aims to undertake a comprehensive review of the different methods of the BL model, like the self-blended model, rotation model, and other BL models. This survey is carried out by considering multiple BL models for academic behavior analysis. In this survey, 50 existing approaches are reviewed on the merits and demerits of the BL model to determine the academic behavior analysis of the BL model teaching and learning. In addition, the academic behavior analysis of the BL model is carried out based on multiple parameters. This review focuses on how these models influence learning patterns, motivation, participation, and performance outcomes across different educational contexts. The survey also highlights the practical challenges associated with implementing BL in real academic settings. Among the examined models, the self-blended model emerges as particularly effective for analyzing student academic behavior, as it allows learners more control over their study pace and content selection. The insights gained from this review can help educators design more effective teaching strategies and support institutions in improving the quality, adaptability, and long-term success of blended learning in modern education.