Students' Adaptability Level Prediction in Online Education using Machine Learning Approaches
12th International Conference on Computing Communication and Networking Technologies, ICCCNT 2021
; 2021.
Article
in English
| Scopus | ID: covidwho-1752361
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 decision-makers 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. © 2021 IEEE.
Classification; Machine Learning; Online Education; Prediction; Random Forest; Decision trees; Developing countries; E-learning; Learning algorithms; Nearest neighbor search; Neural networks; Students; Support vector machines; Surveys; Bangladesh; Colleges and universities; Decision makers; Educational activities; Educational institutions; Machine learning approaches; Machine-learning; On-line education; Random forests; Socio-demographic factors; Forecasting
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
English
Journal:
12th International Conference on Computing Communication and Networking Technologies, ICCCNT 2021
Year:
2021
Document Type:
Article
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