EMPIRICAL METRICS AND STATISTICAL EVALUATION OF IOT-BASED SMART LEARNING IMPLEMENTATION EFFICIENCY IN UNIVERSITY ENVIRONMENTS

Abstract

The high rate of implementing Internet of Things (IoT) technologies in institutions of higher learning has led to the creation of smart learning environments in traditional classrooms. The paper is an empirical assessment of the effectiveness of an IoT-based Smart Learning Framework in two large state-run universities of Sindh, Pakistan University of Sindh, Jamshoro and Sindh Agriculture University, Tandojam. The sample size of 300 participants (150 students, 100 faculty members, and 50 administrative/technical staff) was selected during the 2024-2025 academic year in both the institutions. They were the ten KPIs measured: system responsiveness, data accuracy, energy usage, network reliability, security compliance, user satisfaction, learning gain score, attendance automation rate, resource utilization rate, and cost-saving index. The paired t-test, ANOVA, and multiple regression analysis results have shown that the suggested framework improved the learning gains score by 28.4 percent (p < 0.001), operational costs reduction by 31.7 percent, and user satisfaction by 94.2 percent. Network reliability (β = 0.412, p < 0.01) and security compliance (β = 0.356, p < 0.01) were the best predictors of overall implementation efficiency. The results present statistically proven data that properly developed IoT ecosystems can play a crucial role in increasing the efficiency of institutions and the learning process in higher education settings with limited resources.