Impact Prediction of Online Education During COVID-19 Using Machine Learning: A Case Study
6th World Conference on Smart Trends in Systems, Security and Sustainability, WS4 2022
; 579:567-582, 2023.
Article
in English
| Scopus | ID: covidwho-2263237
ABSTRACT
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. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Case report
/
Experimental Studies
/
Prognostic study
Language:
English
Journal:
6th World Conference on Smart Trends in Systems, Security and Sustainability, WS4 2022
Year:
2023
Document Type:
Article
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