E-Learning Emergence:

Abstract

This study explores the growth of e-learning in Bangladesh, analyzing its socioeconomic, environmental, and sustainability implications using survey data. The analysis includes aspects like internet connectivity, device usage, study hours, course quality, efficacy, engagement, and affordability. This study applied seven machine learning classification models—Logistic Regression, Random Forest, K-Nearest Neighbors, Support Vector Machine, Gaussian Naïve Bayes, Decision Tree, and XGBoost. Random Forest notably outperformed with 78.22% accuracy in predicting the potential of online education to replace traditional methods. According to the findings, 98.8% of students had regular internet access, with 50% reporting extremely stable connections. 60.9% of respondents use smartphones as their primary device, highlighting the need for mobile-friendly learning systems. Approximately 50% of students spend less than five hours each week on online learning platforms such as YouTube and Zoom. 68% of students assess course quality positively, while 33.7% believe online classes are more effective than traditional methods. Despite these advantages, obstacles such as technological difficulties, a lack of desire, and feelings of isolation remain. To overcome these difficulties, the research advises increasing technical assistance, creating interesting material, and boosting teacher training. Furthermore, advancements in internet infrastructure and device accessibility are crucial to the sustained expansion of e-learning. The paper continues with recommendations for future research, emphasizing the importance of longitudinal studies to measure e-learning's long-term impact, as well as the potential benefits of hybrid learning models that blend online and traditional teaching approaches. All things considered, e-learning in Bangladesh has a lot of promise, but for it to grow sustainably, it must overcome current obstacles.