| Title: |
Satisfaction prediction of online education in COVID-19 situation using data mining techniques: Bangladesh perspective |
| Authors: |
Poushy, Lamisha Haque; Bhuiyan, Salauddin Ahmed; Parvin, Masuma; Hossain, Refath Ara; Moon, Nazmun Nessa; Nooder, Jarin; Mahbuba, Ashrarfi |
| Source: |
International Journal of Electrical and Computer Engineering (IJECE); Vol 12, No 5: October 2022; 5553-5561 ; 2722-2578 ; 2088-8708 ; 10.11591/ijece.v12i5 |
| Publisher Information: |
Institute of Advanced Engineering and Science |
| Publication Year: |
2022 |
| Subject Terms: |
COVID-19; F1-score and accuracy; machine learning; online education; prediction |
| Description: |
This research focuses on the education-based online learning platform. Due to the coronavirus disease (COVID-19) epidemic, online education is gaining global popularity. It has shown how successful it is in investigating the quality of online education at the COVID-19 pandemic situation by 799 students from different academic institutions, schools, colleges, and universities. A Google web form has been utilized as the data gathering mechanism for this survey. This paper perused the prediction of online education through data mining and machine learning approaches in an online program. The data was collected through online questionnaires. To predict online education's satisfaction rate, four different types of classifiers are used e.g., logistic regression classifiers, k-nearest neighbors, support vector machine, naive Bayes classifiers. The key purpose of this research is to find out an answer to a question which is, "are the student's satisfied with starting the new online teaching system, or will it be an ambivalent effect for students in the future?". |
| Document Type: |
article in journal/newspaper |
| File Description: |
application/pdf |
| Language: |
English |
| Relation: |
https://ijece.iaescore.com/index.php/IJECE/article/view/27102/15999; https://ijece.iaescore.com/index.php/IJECE/article/view/27102 |
| DOI: |
10.11591/ijece.v12i5.pp5553-5561 |
| Availability: |
https://ijece.iaescore.com/index.php/IJECE/article/view/27102; https://doi.org/10.11591/ijece.v12i5.pp5553-5561 |
| Rights: |
Copyright (c) 2022 Lamisha Haque Poushy, Salauddin Ahmed Bhuiyan, Masuma Parvin, Refath Ara Hossain, Nazmun Nessa Moon, Jarin Nooder, Ashrarfi Mahbuba ; http://creativecommons.org/licenses/by-sa/4.0 |
| Accession Number: |
edsbas.E1D425DA |
| Database: |
BASE |