Impact Prediction of Online Education during COVID-19 using Machine Learning: A Case Study. COVID-19 directly affected the students of Bangladesh Long-term negative effects on students could have been devastating A study was conducted to predict changes in patterns The survey was done to collect data from private university students Data were analyzed using machine learning approaches based on multiple features Comparison between factors of impact was done through different models.

1. Impact Prediction of Online
Education during COVID-19 using
Machine Learning: A Case Study
Mufrad Hossain, Md. Mahfujur Rahman,
Alistair Barros, Md Whaiduzzaman
August 26, 2022
World Conference on Smart Trends
in Systems, Security and
Sustainability (WorldS4 2022)

2. Table of contents
Introduction
01 Experimental
Results
04
Related Work
02 Discussion
05
Research
Methodology
03

3. 01
Introduction
● COVID-19 directly affected the students of Bangladesh
● Long term negative effect on students could have been devastating
● Study was conducted to predict changes in patterns
● Survey was done to collect data from private university students
● Data was analyzed using machine learning approaches based on
multiple features
● Comparison between factors of impact were done through different
models

4. 02
Related Work
● Islam et al. argue that expansion of the utilization of ICT can directly
enhance the condition for online education for Bangladesh
● Hossain et al. highlight the critical steps that the government can take
to improve the OE/DL in Bangladesh
● Efta Khairul Haque et al. state in their study that the availability of
gadgets and access to the internet has been a significant element in
online education
● Tamanna et al. talk about the satisfaction level of students in Online
education
● Study by Parvej et al. sheds light on the scenario of teachers and
online education facilities

5. 03
Research Methodology
● Data Collection
● Dataset Pre-processing
● Machine Learning Classifiers
● Correlation Analysis
● Train-Test Splitting Data
● Train & Fit Model
● Performance Evaluation
A functional prototype of the proposed system

6. 04
Experimental Results
Prediction performance of machine learning models with top features

7. Prediction performance of machine learning models with different
groups

8. 05
Discussion
● We applied machine learning classifiers to predict the fluctuation of
CGPA using several attributes
● We compared the performance of different models with different sets
of features
● We have exhaustively studied the factors and observed that
adaptability plays an essential role in increasing CGPA.
● Students who were not comfortable with the implementation of online
classes, assignments, and exams were the ones who had the most
impact on their academic outcomes
● The network infrastructure available to the student at the time of online
education also affects how the student might perform academically

9. THANK YOU!