Transforming Language Education with Big Data: Adaptive Learning Analytics for Student Centered Pedagogy

Abstract

The incorporation of Big Data into language education has reshaped how learning processes are organized, delivered, and assessed. Conventional teacher-centered models, which often rely on uniform instruction, are limited in addressing the diverse needs of learners in multilingual and digitally mediated settings. This study examines the role of Big Data-driven adaptive learning analytics in advancing student-centered pedagogy. Using extensive datasets from learning management systems (LMS), online assessments, and learner interaction logs, predictive modeling and discourse analysis were applied to identify learning behaviors, personalize instructional materials, and monitor progress. The findings show that adaptive analytics significantly enhance student participation, retention, and achievement when compared with traditional static approaches. At the same time, issues such as data security, algorithmic bias, and the preparedness of educators present ongoing challenges. The study concludes that, when guided by ethical considerations and integrated into pedagogical practice, adaptive learning analytics powered by Big Data can transform language education into a more dynamic, inclusive, and learner-focused system that supports autonomy and long-term learning development.