Analysis of data mining techniques applied to LMS for personalized education
William Villegas-Ch, Sergio Luján-Mora
1st IEEE World Engineering Education Conference (EDUNINE 2017), p. 85-89, Santos (Brazil), March 19-22. ISBN: 978-1-5090-4886-1. https://doi.org/10.1109/EDUNINE.2017.7918188
(EDUNINE'17a) Congreso internacional / International conference
This article describes the models and the use of data mining techniques applied to Learning Management Systems (LMS) which allow institutions to offer the student a personalized education. It considers the ways in which the concepts of educational data mining (EDM) are applied to the information extracted from the LMS. The data from these systems can be evaluated to convert the information collected into useful information to provide an education tailored to the needs of each student. This approach seeks to improve the effectiveness and efficiency of education by recognizing patterns in student performance. This article presents an analysis of the data mining techniques that fit LMS, specifically in terms of a case study applied to the e-learning platform Moodle. The objective is to provide stakeholders with guidance on the use of EDM tools.