Educational Data Mining (EDM) is used to ameliorate the teaching and learning process by
analyzing and classifying data that can be applied to predict the students' academic …

Accurately predicting students' performance in e-learning is very important in education.
With the advancement of technology and the rise of e-learning platforms, a vast amount of …

This paper aims to explore the effectiveness of JFLAP as a pedagogical tool for automata
theory and its impact on student performance. We implement machine learning algorithms to …

Predicting learners' prospective performance depending on their current educational data is
critical for implementing appropriate educational measures for guaranteeing learners' timely …

In higher education, student learning relies increasingly on autonomy. With the rise in
blended learning, both online and offline, students need to further improve their online …

Finding a solution to the problem of student retention is an often-required task across Higher
Education. Most often managers and academics alike rely on intuition and experience to …

Comparing the performance of predictive models of student success has become a central
task in the field of learning analytics. In this paper, we argue that research seeking to …

Methods that accurately predict the grade of a student at a given activity or course can
identify students that are at risk in failing a course and allow their educational institution to …

The pervasive collection of data has opened the possibility for educational institutions to use
analytics methods to improve the quality of the student experience. However, the adoption of …