Predicting student attrition is an intriguing yet challenging problem for any academic
institution. Class-imbalanced data is a common in the field of student retention, mainly …

This paper explores the socio-demographic variables (age, gender, ethnicity, education,
work status, and disability) and study environment (course programme and course block) …

As providers of higher education begin to harness the power of big data analytics, one very
fitting application for these new techniques is that of predicting student attrition. The ability to …

Student retention in online education is a concern for students, faculty and administration.
Retention rates are 20% lower in online courses than in traditional face-to-face courses. As …

This paper explores the socio-demographic variables (age, gender, ethnicity, education,
work status, and disability) and study environment (course programme and course block) …

Using the theoretical framework of Tinto (2013), the first objective of the current research
was to establish first year students' experience in the first few weeks of their studies in …

A major problem that the Higher Education Institutions (HEIs) face is the misconduct of
students' behavior. The objective of this study is to decrease these misconducts by …

Students dropout prediction is an indispensable for numerous intelligent systems to
measure the national and international loss for develo** countries as well as for …

Abstract Information technology is resha** higher education globally and analytics can
help provide insights into complex issues in higher education, such as student recruitment …

Pre-trained convolutional neural network (CNN) structures are considered as one of the
emerging education management tools that can help improve the quality of education by …