Data mining toolkit for extraction of knowledge from LMS
William Villegas-Ch, Diego Buenaño-Fernández, Sergio Luján-Mora
Proceedings of the 2017 9th International Conference on Education Technology and Computers (ICETC 2017), p. 31-35, Barcelona (Spain), December 20 - 22 2017. ISBN: 978-1-4503-5435-6. https://doi.org/10.1145/3175536.3175553
(ICETC'17e)
Congreso internacional / International conference
Resumen
Today, information technology (IT) is an active part of education. Its main impact is in the administration of learning management systems (LMS). The support provided by IT in LMS has generated greater dexterity in the evaluation of the quality of education. The evaluation process usually includes the use of tools applied to online analytical processing (OLAP). The application of OLAP allows the consultation of large amounts of data. Data mining algorithms can be applied to the data collected to perform a pattern analysis. The potential use of these tools has resulted in their specialization, both in the presentation and in the algorithmic techniques, allowing the possibility of educational data mining (EDM). EDM seeks to enhance or customize education within LMS by classifying groups of students in terms of similar characteristics that require specific resources. The ease of use and extensive information about some of the EDM tools has caused many educational institutions to consider them for their own use. However, these institutions often make errors in data management. Errors in the use of data mean that the improvements in LMS are inadequate. The work described in this paper provides a guide on the use of applied methodology in the process of knowledge extraction (KDD). It also enumerates some of the tools that can be used for each step of the process.