Abstract:
Distance learning (DL) is a method of instruction that makes use of technological
advancements to allow for indirect connections between students and teachers who are
separated by physical distance. Since COVID-19 swept the globe, the term "online
education" has gained popularity. Most schools have moved their operations online so that
instruction may continue even while they expand. It took a long time for a country like
Bangladesh to guarantee online education at all educational levels. Our real objective is to
contribute to this discussion by researching important aspects of online education. In this
research, we conducted physical and online questionnaires to gather data from students at
all three academic levels (school, college, and university) and also from Kaggle. The
sociodemographic characteristics of a person are included in the survey form. A total of 14
variables were used: student gender, student type, age range, educational level of an
institution, type of educational institution, IT student, student location, load shading level,
family financial situation, category of internet, used device type, network connection type,
and adaptability level of the learner. Our dataset was used to predict the level of student
adaptability to online education using several machine learning algorithms, including
Random Forest Classifier (RF), Decision Tree Classifier (DT), K-Nearest Neighbor
(KNN), Logistic Regression (LR), Support Vector Classifier (SVC), and XGBoost
Algorithm (XGB). The decision tree classifier surpassed other algorithms and had the
highest accuracy (93%), compared to those that were used.
