Quantitative Analysis Of Student Engagement Patterns: A Big Data Framework For Personalized Learning Assessment

Abstract

Conventional, one-size-fits-all teaching strategies have clearly exhibited severe limits in rising complexity and diversity of student populations in higher education. Many times, these conventional approaches fall short in adequately involving students, which lowers engagement, generates unfair academic results, and increases the discrepancy between instructional delivery and specific student needs. Addressing this significant issue necessitates more dynamic solutions that can adapt to the evolving landscape of contemporary colleges.

This approach employs robust machine learning capabilities and extensive data analytics to customise learning opportunities and enhance student engagement. The study results demonstrate how real-time participation tracking, when integrated with adaptive instructional design, is analysed through a mixed-methods approach that includes quantitative survey analysis. The methodology primarily depends on predictive analytics and data-driven feedback systems to assist educators in identifying at-risk students, tailoring interventions, and fostering a more inclusive and responsive classroom environment.

The results show that including big data analytics significantly increases student participation, improves conceptual knowledge, and allows customized learning routes. The results highlight the need of strong ethical governance as well as the need of technology support to leverage these advantages. This study helps scalable, fair, effective educational innovation to find roots.