COVID-19 Adaptive E-Learning: Data-Driven Student Engagement Analysis

The rapid growth in online learning during the COVID-19 epidemic demanded an evaluation of adaptive e-learning's impact on student performance and engagement. Using data-driven methodologies, the research examines the effectiveness of adaptive e-learning tactics during the pandemic. The data gathered from various educational institutions, including the xAPI-Educational Mining Dataset, yields intriguing results. Adaptive e-learning improves student engagement, as demonstrated by an average 10% increase in online discussion participation, a 15% increase in assignment submissions, and a 20% increase in LMS usage among students who use adaptive platforms. The regression analysis confirms a substantial link (p < 0.001) between adaptive e-learning utilization and increased engagement. The results highlight the essential importance of adaptive e-learning in maintaining and increasing student interest and academic accomplishment. Recognizing the limits of the data's pandemic-centric emphasis, the study proposes additional research on long-term consequences and personalized measures depending on demographic parameters. The research provides useful insights for educators and institutions navigating the changing environment of virtual education, emphasizing the significance of adaptive e-learning for different student groups.