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Direct linkQazdar, Aimad; Er-Raha, Brahim; Cherkaoui, Chihab; Mammass, Driss – Education and Information Technologies, 2019
The use of machine learning with educational data mining (EDM) to predict learner performance has always been an important research area. Predicting academic results is one of the solutions that aims to monitor the progress of students and anticipates students at risk of failing the academic pathways. In this paper, we present a framework for…
Descriptors: Data Analysis, Academic Achievement, At Risk Students, High School Students
Kordaki, Maria; Papastergiou, Marina; Psomos, Panagiotis – Education and Information Technologies, 2016
The aim of this work was twofold. First, an empirical study was designed aimed at investigating the perceptions that entry-level non-computing majors--namely Physical Education and Sport Science (PESS) undergraduate students--hold about basic Computer Literacy (CL) issues. The participants were 90 first-year PESS students, and their perceptions…
Descriptors: Undergraduate Students, Student Attitudes, Computer Assisted Instruction, Educational Games
