Educational Data Mining plays a critical role in advancing the learning environment by
contributing state-of-the-art methods, techniques, and applications. The recent development …

The last few years have witnessed an upsurge in the number of studies using Machine and
Deep learning models to predict vital academic outcomes based on different kinds and …

This study investigates current approaches to learning analytics (LA) dashboarding while
highlighting challenges faced by education providers in their operationalization. We analyze …

In recent years, artificial intelligence (AI) and learning analytics (LA) have been introduced
into the field of education, where their use has great potential to enhance the teaching and …

Student retention is an essential measurement metric in education, indicated by retention
rates, which are accumulated as students re-enroll from one academic year to the next. High …

Educational data mining is an emerging interdisciplinary research area involving both
education and informatics. It has become an imperative research area due to many …

Student performance is related to complex and correlated factors. The implementation of a
new advancement of technologies in educational displacement has unlimited potentials …

Learning Analytics is a field that measures, analyses, and reports data about students and
their contexts to understand/improve learning and the place in which it occurs. Educational …

A significant body of recent research in the field of Learning Analytics has focused on
leveraging machine learning approaches for predicting at-risk students in order to initiate …

Predicting students' performance is one of the most important issues in educational data
mining (EDM), which has received more and more attention. By predicting students' …