Educational data mining (EDM) is a growing academic research area, which aims to gain signifcant insights on student behavior, interactions, and performance and to improve the technology-enhanced learning methods in a data-driven way by applying data mining methods on educational data (Bousbia & Belamri, 2014). During the last decade, research has been focused to enhance the learning experience and institutional effectiveness by merging the computer-assisted learning systems and automatic analysis of educational data. EDM can offer opportunities and great potentials to increase our understanding about learning processes to optimize learning through educational systems. These opportunities have been strengthened by a huge shift in the availability of the data resources, which constitute an inspiring motivation for growing research in this academic research area. In this regard, EDM can be utilized to inform and support learners, teachers, and their institutions and therefore help them understand how these powerful tools can lead to huge benefts in learning and success in educational outcomes, through personalization and adaptation of education based on the learner’s needs (Greller & Drachsler