The Role of Big Data in Optimizing E-Learning Platforms and Student Engagement in Afghanistan: A Case Study of Kabul University

Abstract

The rapid expansion of digital technologies in higher education has opened new possibilities for institutions in conflict-affected and lower-income countries to leapfrog traditional infrastructure constraints. This study investigates the role of big data analytics in optimizing e-learning platforms and enhancing student engagement at Kabul University, Kabul, Afghanistan. Adopting a mixed-methods design, the research combines survey data collected from 412 students and 48 faculty members with institutional log data drawn from the university's learning management system (LMS) spanning the academic years 2019 to 2026. The findings demonstrate that the integration of big data tools—including Apache Hadoop, Apache Spark, predictive machine learning models, and personalized recommender systems—significantly improved student engagement scores by an average of 28.4 percentage points and raised course completion rates from 71% to 91% over the study period. Regression analysis reveals that big data adoption index, learning analytics usage, and personalized content delivery are the strongest predictors of student outcomes (adjusted R² = 0.831, F = 57.4, p < .001). The study introduces a conceptual framework linking big data infrastructure to educational outcomes and identifies critical barriers—including intermittent connectivity, digital literacy gaps, and absence of data governance policy—that constrain full adoption. Policy recommendations are offered for university administrators, the Ministry of Higher Education (MoHE), and international development partners.