During the past years scholars have shown an increasing interest in supporting students'
self-regulated learning (SRL). Learning analytics (LA) can be applied in various ways to …

The growing need for tailored learning experiences in post-secondary education has
resulted in the adoption of educational data mining (EDM) methodologies to derive …

Educational Data Mining (EDM) is a promising area of research that leverages
computational methods to improve educational outcomes by extracting valuable insights …

Student persistence and retention in STEM disciplines is an important yet complex and multi-
dimensional issue confronting universities. Considering the rapid evolution of online …

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 …

A central assumption of the scientific method is that inferences derived from group-level
analysis align with and generalize to the individual level. This study was conducted to put …

Learning analytics is a fast‐growing discipline. Institutions and countries alike are racing to
harness the power of using data to support students, teachers and stakeholders. Research …

Student success is essential for improving the higher education system student outcome.
One way to measure student success is by predicting students' performance based on their …

In the last decade, artificial intelligence (AI), machine learning (ML) and learning data
analytics have been introduced with great effect in the field of higher education. However …

Higher educational institutes generate massive amounts of student data. This data needs to
be explored in depth to better understand various facets of student learning behavior. The …