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' …

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

Student engagement is a flexible, complicated concept that includes behavioural, emotional,
and cognitive involvement. In order for the instructor to understand how the student interacts …

The process of learning about a student's knowledge and comprehension of a particular
subject is referred to as student knowledge assessment. It helps to identify areas where …

The educational research is increasingly emphasizing the potential of student engagement
and its impact on performance, retention and persistence. This construct has emerged as an …

Teacher evaluation is presented as an object of study of great interest, where multiple efforts
converge to establish models from the association of heterogeneous data from academic …

Background Universities enroll students from diverse backgrounds every year, with 300
million students expected in higher education by 2025. However, with widening …

Advanced machine learning (ML) methods can predict student's performance with key
features based on academic, behavioral, and demographic data. Significant works have …

Постановка проблемы. Представлен один из подходов к решению задачи
прогнозирования академической успеваемости обучающихся. В отличии от …

In pursuing increased access to higher education through Open, Distance and e-Learning
(ODeL), and in the attempt to improve learner retention, the instructor role remains an area …