Ver todos los resumenes/See all abstracts
Ver todas las publicaciones (sin resumenes)/See all publications (without abstracts)
|CI||Congreso internacional / International conference|
|CL||Capítulo de libro / Book chapter|
|CN||Congreso nacional / National conference|
|II||Informe interno / Internal report|
|LI||Libro / Book|
|RV||Revista / Journal|
URL Documento / Document Presentación / Slides
|Clave: CI Ref: REES'17|
Oswaldo Moscoso-Zea, Mayra Vizcaino, Sergio Luján-Mora. Evaluation of Methods and Algorithms of Educational Data Mining. 7th Research in Engineering Education Symposium (REES 2017), p. 972-980, Bogota (Colombia), July 6-8 2017. ISBN: 978-1-5108-4941-9.
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.