Evaluation of Methods and Algorithms of Educational Data Mining
Oswaldo Moscoso-Zea, Mayra Vizcaino, Sergio Luján-Mora
7th Research in Engineering Education Symposium (REES 2017), p. 972-980, Bogota (Colombia), July 6-8 2017. ISBN: 978-1-5108-4941-9.
(REES'17) Congreso internacional / International conference
Educational data mining (EDM) is an evolving discipline that allows the creation and exploration of knowledge from academic environments by means of developing and applying data mining (DM) methods and algorithms to information stored in data repositories of higher education institutions. The results of the application of these methods and algorithms allows these institutions to better understand the way the lecturers teach, the way the students learn and the activities of organizational processes to improve decision making. This paper describes DM, EDM and the existing methods and algorithms of the discipline. Furthermore, it presents the experiments carried out for the evaluation of methods and algorithms applied to two key performance indicators in a private university: student dropout and graduation rate. Finally, it compares these methods and algorithms and suggests which has better precision in certain scenarios.