Unfair behaviors of Machine Learning (ML) software have garnered increasing attention and
concern among software engineers. To tackle this issue, extensive research has been …

Recently, machine learning (ML) has evolved and finds its application in higher education
(HE) for various data analysis. Studies have shown that such an emerging field in …

Student drop-out is one of the most burning issues in STEM higher education, which induces
considerable social and economic costs. Using machine learning tools for the early …

Beyond managing student dropout, higher education stakeholders need decision support to
consistently influence the student learning process to keep students motivated, engaged …

MOOCs have attracted hundreds of millions of learners with advantages such as being cost-
free and having flexible time and space. However, high dropout rates have become the main …

Neural networks are ubiquitous in applied machine learning for education. Their pervasive
success in predictive performance comes alongside a severe weakness, the lack of …

Featured Application Application to student counseling and reducing the dropout rate in
universities. Abstract Since a high dropout rate for university students is a significant risk to …

Nowadays, fewer students are choosing to enroll in STEM (science, technology,
engineering, and mathematics) fields. STEM students in schools and in higher educational …

High dropout rates and delayed completion in higher education are associated with
considerable personal and social costs. In Latin America, 50% of students drop out, and only …

Prediction of student performance in Introductory programming courses can assist struggling
students and improve their persistence. On the other hand, it is important for the prediction to …