Students' Adaptability Level Prediction in Online Education using Machine Learning Approaches

Abstract

Online Education has become a buzzword since the COVID-19 hit the World. Most of the educational institutions went online to continue educational activities while developing countries like Bangladesh took a significant period of time to ensure online education at every education level. Students of several levels also faced many difficulties when they got introduced to online education. It is important for the decisionmakers of educational institutions to be informed about the effectiveness of online education so that they can take further steps to make it more beneficial for the students. Our main motivation is to contribute to this matter by analyzing the relevant factors associated with online education. In this work, we have collected students' information of all three different levels(School, College, and University) by conducting both online and physical surveys. The surveys form consists of an individual's socio-demographic factors. To get an idea about the effectiveness of online education we have applied several machine learning algorithms named Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and also Artificial Neural Network(ANN) on our dataset to predict the adaptability level of the students to online education. Among used algorithms, the Random Forest classifier achieved the best accuracy of 89.63% and outperformed other algorithms.