Document Details
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Document Type |
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Thesis |
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Document Title |
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Machine Learning approach for Measuring the Impact of COVID-19 on Distance education: An Applied Case on Saudi Arabia Universities استخدام التعلم الآلي لقياس تأثير جائحة COVID-19على التعليم عن بعد: حالة تطبيقية على جامعات المملكة العربية السعودية |
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Subject |
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Faculty of Computing and Information Technology |
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Document Language |
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Arabic |
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Abstract |
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Since the World Health Organization announced the COVID-19 pandemic, many countries have made strict decisions to prevent the spread of the virus, closing borders, preventing travel and even roaming within cities, and they transitioned edu- cation from being physical attendance, to distance education in order to achieve full physical distance as the most important way to prevent the spread of the pandemic. Saudi Arabia was one of the first countries to transfer physical attendance education to distance education, as the transition was rapid. This research aims to predict the impact of COVID-19 on the distance education of King Abdulaziz University students through a comprehensive framework using a machine-learning approach. Based on the most common factors and challenges that students in general faced with distance education during the COVID-19 pandemic in several previous stud- ies, these factors were extracted and applied in this research on King Abdulaziz University students to discover the extent of the impact of said factors on them. This was carried out in addition to knowing the pros and cons of distance education during the pandemic period and suggestions that contribute to improving the educa- tional process under these conditions. The questionnaire was prepared based on the extracted factors and is aimed at King Abdulaziz University students; it examines the impact from several aspects, namely the psychological, health, educational, and
social ones during distance education in this emergency situation (COVID-19). The data collected from the questionnaire were analysed using SPSS. Five machine- learning algorithms were implemented: KNN, Decision Tree, R-Forst, XGBOOST, and SVM. The performance of the five models used for prediction was evaluated by multiple evaluation criteria, namely: accuracy, precision, recall, F1-measure, and Receiver Operating Characteristics (ROC). The results indicated that the SVM model predicted an accuracy of up to 84.407% compared to other models used in this research. The results of this thesis greatly serve the education sector and con- tribute to knowing the extent of the impact of distance education on King Abdulaziz University students and to revealing the factors that may have an impact on students in such an emergency situation.
Key Word: COVID-19 pandemic, Distance education, Machine learning, Education. |
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Supervisor |
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Prof. Abdullah Saad AL-Malaise AL-Ghamdi |
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Thesis Type |
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Master Thesis |
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Publishing Year |
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1444 AH
2023 AD |
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Co-Supervisor |
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Dr. Farrukh Saleem |
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Added Date |
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Monday, March 20, 2023 |
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Researchers
| روان حمود المحمدي | Al-Mohammadi, Rawan Hamoud | Researcher | Master | |
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