| Autor: |
Mostafa, Salama A., Dawood, Aya Qusay, Mustapha, Aida, Hassan, Mustafa Hamid, Alkhayyat, Ahmed, Khaleefah, Shihab Hamad, Obaid, Hawaa A. |
| Předmět: |
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| Zdroj: |
AIP Conference Proceedings; 2023, Vol. 3015 Issue 1, p1-7, 7p |
| Abstrakt: |
COVID-19 has changed the world in all sectors; schools and universities are also affected. The education sector had to be suspended for some time, and then all universities and schools started distance learning during this pandemic. Transforming into an online method makes it possible to continue the studies and be safe from viruses. The large scale of this method is in education, in which the lectures are delivered remotely via various digital platforms. So far, this change has opened challenges to government regulations, educators, students, families, and administrators. In this paper, we analyze a dataset of surveys that conducted the questionnaire that can help measure the effectiveness of distance learning during pandemics. We perform data analysis and implement four machine learning models: Linear Regression (LR), Neural Network (NN), Decision Forest (DF), and Decision Jangle (DJ). The models are used to estimate the effectiveness of distance learning during the Covid-19 pandemic. The experimental results show that the NN model has the highest accuracy of 86.93% in the data split of 30% training and 70% testing. Nevertheless, the DJ model has achieved an overall highest average accuracy of 85.00% and recall of 47.04%. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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