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Impact Prediction of Online Education during COVID-19 using Machine Learning_ A Case Study.pptx

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Impact Prediction of Online Education during COVID-19 using Machine Learning_ A Case Study.pptx

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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.

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.

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Impact Prediction of Online Education during COVID-19 using Machine Learning_ A Case Study.pptx

  1. 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. 2. Table of contents Introduction 01 Experimental Results 04 Related Work 02 Discussion 05 Research Methodology 03
  3. 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. 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. 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. 6. 04 Experimental Results Prediction performance of machine learning models with top features
  7. 7. Prediction performance of machine learning models with different groups
  8. 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. 9. THANK YOU!

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