Analyzing Student Adaptability In Online Education Using Descriptive Analytics

Abstract

This study investigates the factors influencing online education by analyzing Pearson Correlation Coefficients, Chi-square tests, and hypothesis testing. The findings reveal significant correlations among age, education level, IT student status, and the use of self-learning management systems (LMS), indicating that older students tend to have higher education levels, are more likely to be IT students, and utilize LMS more frequently. Financial condition shows a moderate correlation with adaptivity levels and internet type, underscoring the impact of socioeconomic status on digital learning experiences. Chi-square tests confirm significant associations between education level, device usage, internet type, and adaptivity level. Conversely, gender and load-shedding display weaker correlations, suggesting minimal impact on online learning. Hypothesis testing highlights significant differences in adaptivity levels based on gender, internet type, and IT student status, as well as associations between location and load-shedding frequency, and class duration based on LMS usage. These results emphasize the complex interplay of demographic, technological, and institutional factors in online education, providing valuable insights for enhancing digital learning strategies and policies.