TY - GEN
T1 - Impact Prediction of Online Education During COVID-19 Using Machine Learning
T2 - 6th World Conference on Smart Trends in Systems, Security and Sustainability, WS4 2022
AU - Hossain, Sheikh Mufrad
AU - Rahman, Md Mahfujur
AU - Barros, Alistair
AU - Whaiduzzaman, Md
N1 - Funding Information:
Acknowledgements This research is partly supported through the Australian Research Council Discovery Project: DP190100314, “Re-Engineering Enterprise Systems for Microservices in the Cloud”.
Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2023
Y1 - 2023
N2 - The transition from traditional to online education is challenging and has many obstacles in various situations. Due to the Covid-19 situation, we use digital blended education from the traditional system. However, in some cases, it can harm our student’s academic performance. In this research, we aim to identify the factors that impact the student’s academic performance in online education. On the other hand, this study also finds the student Cumulative Grade Point Average (CGPA) fluctuation using machine learning classifiers. To achieve this, we survey to gather data perspective of Bangladesh private university, and this data allows us to analyze and classify using machine learning techniques such as Logistic Regression (LR), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), Decision Tree (DT), and Random Forest (RF). This study finds Random Forest (RF) outperforms the other state-of-art classifiers.
AB - The transition from traditional to online education is challenging and has many obstacles in various situations. Due to the Covid-19 situation, we use digital blended education from the traditional system. However, in some cases, it can harm our student’s academic performance. In this research, we aim to identify the factors that impact the student’s academic performance in online education. On the other hand, this study also finds the student Cumulative Grade Point Average (CGPA) fluctuation using machine learning classifiers. To achieve this, we survey to gather data perspective of Bangladesh private university, and this data allows us to analyze and classify using machine learning techniques such as Logistic Regression (LR), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), Decision Tree (DT), and Random Forest (RF). This study finds Random Forest (RF) outperforms the other state-of-art classifiers.
KW - Machine learning
KW - Online education
KW - Performance
UR - http://www.scopus.com/inward/record.url?scp=85149941926&partnerID=8YFLogxK
U2 - 10.1007/978-981-19-7663-6_54
DO - 10.1007/978-981-19-7663-6_54
M3 - Conference contribution
AN - SCOPUS:85149941926
SN - 9789811976629
T3 - Lecture Notes in Networks and Systems
SP - 567
EP - 582
BT - Intelligent Sustainable Systems - Selected Papers of WorldS4 2022
A2 - Nagar, Atulya K.
A2 - Singh Jat, Dharm
A2 - Mishra, Durgesh Kumar
A2 - Joshi, Amit
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 24 August 2022 through 27 August 2022
ER -