Abstract: Since the World Health Organization announced the Covid-19 pandemic, many countries have made strict decisions to prevent the spread of the Covid-19 epidemic, so they closed borders, prevented travel, prevented roaming within cities, and transition education from physical education to distance education to achieve physical distance as the most important way to prevent the spread of the epidemic. Saudi Arabia is one of the first countries to transfer physical 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. Machine learning algorithms KNN, Decision Tree, R-Forst, XGBOOST and SVM were used. Based on the factors and challenges that have created an impact on students from multiple aspects, namely psychological, social, educational and health aspects during Distance Education in this emergency situation. The factors were extracted through a questionnaire directed to King Abdulaziz University students. The data collected from the questionnaire were analyzed using SPSS, and then the results of the models used for Prediction were evaluated using multiple metrics, namely: accuracy, precision, recall, F1-measure and Receiver Operating Characteristics (ROC). The results indicated that the SVM model predicted with an accuracy of up to 84.407% compared to other models used in this research. The results of this research are expected to greatly serve the education sector and contribute to knowing the extent of the impact of distance education on King Abdulaziz University students and to knowing the factors that may have an impact on students in such an emergency situation.

Keywords: Covid-19 pandemic, Distance education, Machine learning, Education.

PDF | DOI: 10.17148/IJARCCE.2022.11919

Open chat
Chat with IJARCCE