Introduction As artificial intelligence (AI) technology becomes more widespread in the
classroom environment, educators have relied on data-driven machine learning (ML) …

Educational institutions are actively engaged in extensive initiatives to mitigate student
dropout rates. In addition to various strategies, the integration of machine learning (ML) …

Statistical predictive models play an important role in learning analytics. In this work, we
seek to harness the power of predictive modeling methodology for the development of an …

Today, educational institutions produce large amounts of data with the deployment of
learning management systems. These large datasets provide an untapped potential to …

Abstract Campus Management Systems (CMSs) are vital tools in managing educational
institutions, handling tasks like student enrollment, scheduling, and resource allocation. The …

This study explores the application of artificial intelligence (AI) and machine learning (ML) in
predicting high school student performance during the transition to university. Recognizing …

This study introduces a machine learning-based model for predicting student performance
using a comprehensive dataset derived from educational sources, encompassing 15 key …

This research explores the application of predictive analytics and machine learning
techniques in the educational sector, focusing on forecasting student performance and …

The presence of" big data" in higher education has led to the increasing popularity of
predictive analytics for guiding various stakeholders on appropriate actions to support …

Predicting academic performance for students majoring in computer science has long been
a significant field of research in computing education. Previous studies described that …