Abstract:
In this rapid advancement of technology online education has come out as an alternative to
traditional classroom based learning systems especially in the contexts like Bangladesh. While
Bangladesh embraced online education during the COVID-19 pandemic there remain critical
challenges to ensure its quality and equitable access. This research finds out the ways to
improve the standard of online learning from Bangladesh's distinct point of view and proposes
strategies for improvement considering the country's distinct socioeconomic and cultural
background. Firstly it investigates the infrastructure issues that have a substantial impact on
the success of online learning efforts such as internet connectivity and digital device access.
Secondly the study examines the pedagogical strategies used in the delivery of online learning
highlighting the importance of engaging and culturally appropriate teaching strategies. The
study aims to determine tactics that maximize learning results and student engagement in the
online environment by looking at best practices and empirical data. For the purpose of training
and evaluating our dataset, we can use the Random Forest Classifier (RF), Decision Tree
Classifier (DT), K-Nearest Neighbor (KNN), Logistic Regression (LR), Naïve Bayes, Support
Vector Classifier (SVC), and XGBoost Algorithm (XGB). Highest accuracy gain Random
forest 98% and lowest accuracy linear Logistic Multiclass,Naïve Bayes, 43% . Accuracy
obtain in KNeighbors Classifier 82%, Accuracy obtain in SVC 70% , Accuracy obtain Linear
SVC 44% Accuration obtain in Ensemble 85% .
