A Proposed Framework for Student's Skills-Driven Personalization of Cloud-Based Course Content

Engaging students' personalized data in the aspects of education has been on focus by different researchers. This paper considers it vital for exploring the student's progress, moreover, it could predict the student's 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's educational tasks and activities proved the positive impact of raising the student level. The paper proposes a framework that considers the students' 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' learning skills, and the second focused on predicting the students' 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. © 2023 World Scientific Publishing Company.

A Smart Testing Model Based on Mining Semantic Relations

Engaging personalization in the education process is considered one of the success factors for raising the educational process quality by altering the educational institutions' vision for gaining more flexibility while attaining the institution's objectives. It is a fact that the situation of the COVID-19 pandemic is one of the main reasons that forwarded attention to online learning as an obligatory path rather than being optional until the arisen situation of the COVID-19 pandemic. This situation has altered the educational institutions' perspective permanently. This research proposes an intelligent model which considers the personalized student characteristics in exploring the student learning styles variation, then considering this variation in building the student exam. Following this model ensures the compatibility of the conducted exam with the student's capabilities as well as the course Intended Learning Outcomes (ILOs) coverage. The balance in building the exam with covering the course objectives as well as the appropriateness with the student's personalized characteristics is the main objective of this research. The proposed model has been applied and proved its applicability in enhancing the students' exam results to 92.36% and raising the exam quality level. © 2013 IEEE.