{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,4,11]],"date-time":"2024-04-11T05:49:23Z","timestamp":1712814563515},"reference-count":32,"publisher":"World Scientific Pub Co Pte Ltd","issue":"04","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Soft. Eng. Knowl. Eng."],"published-print":{"date-parts":[[2023,4]]},"abstract":"<jats:p> Engaging students\u2019 personalized data in the aspects of education has been on focus by different researchers. This paper considers it vital for exploring the student\u2019s progress, moreover, it could predict the student\u2019s level which consequently leads to identifying the required student material to raise his current education level. Although the topic has been vital before the COVID-19 pandemic, however, the importance of the topic has increased exponentially ever since. The research supports the decision-makers in educational institutions as considering personalized data for the student\u2019s educational tasks and activities proved the positive impact of raising the student level. The paper proposes a framework that considers the students\u2019 personal data in predicting their learning skills as well as their educational level. The research included engaging five well-known clustering algorithms, one of the most successful classification algorithms, and a set of 10 features selection techniques. The research applied two main experiment phases, the first phase focused on predicting the students\u2019 learning skills, and the second focused on predicting the students\u2019 level. Two datasets are involved in the experiments and their sources are mentioned. The research revealed the success of the clustering and prediction tasks by applying the selected techniques to the datasets. The research concluded that the highest clustering algorithm accuracy is enhanced k-means (EKM) and the highest contributing features selection method is the evolutionary computation method. <\/jats:p>","DOI":"10.1142\/s0218194023500092","type":"journal-article","created":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T14:21:40Z","timestamp":1677680500000},"page":"603-617","source":"Crossref","is-referenced-by-count":1,"title":["A Proposed Framework for Student\u2019s Skills-Driven Personalization of Cloud-Based Course Content"],"prefix":"10.1142","volume":"33","author":[{"given":"Alaa A.","family":"Qaffas","sequence":"first","affiliation":[{"name":"Department of Management Information Systems, College of Business, University of Jeddah, Jeddah, KSA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ibraheem","family":"Alharbi","sequence":"additional","affiliation":[{"name":"Department of Management Information Systems, College of Business, University of Jeddah, Jeddah, KSA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amira M.","family":"Idrees","sequence":"additional","affiliation":[{"name":"Faculty of Computers and Information Technology, Future University in Egypt, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sherif A.","family":"Kholeif","sequence":"additional","affiliation":[{"name":"Department of Information Systems, Faculty of Computers and Artificial Intelligence, Helwan University, Cairo, Egypt"},{"name":"College of Business, University of Jeddah, Jeddah, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2023,4,18]]},"reference":[{"issue":"3","key":"S0218194023500092BIB001","doi-asserted-by":"crossref","first-page":"250","DOI":"10.17706\/jcp.12.3.250-257","volume":"12","author":"Khedr A. 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