{"title":"Educational data mining: An holistic view","authors":"Oswaldo Moscoso-Zea, S. Luján-Mora","doi":"10.1109/CISTI.2016.7521411","DOIUrl":null,"url":null,"abstract":"Datamining (DM) brings together a wide range of techniques and algorithms which allow the extraction of knowledge from databases for timely decision making. DM has been applied to different fields of study. One important research field is Education. Applying DM in education is known as educational datamining (EDM). The main purpose of EDM is to analyze data from educational institutions using different techniques such as: prediction, clustering, time-series analysis, classification, among others. This paper presents an holistic view of EDM including classification of algorithms, methods and tools used in DM processes. Furthermore, processes and indicators that could be improved are analyzed in educational institutions. This study covers papers presented from 2005 to 2015.","PeriodicalId":339556,"journal":{"name":"2016 11th Iberian Conference on Information Systems and Technologies (CISTI)","volume":"77 9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 11th Iberian Conference on Information Systems and Technologies (CISTI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CISTI.2016.7521411","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
Datamining (DM) brings together a wide range of techniques and algorithms which allow the extraction of knowledge from databases for timely decision making. DM has been applied to different fields of study. One important research field is Education. Applying DM in education is known as educational datamining (EDM). The main purpose of EDM is to analyze data from educational institutions using different techniques such as: prediction, clustering, time-series analysis, classification, among others. This paper presents an holistic view of EDM including classification of algorithms, methods and tools used in DM processes. Furthermore, processes and indicators that could be improved are analyzed in educational institutions. This study covers papers presented from 2005 to 2015.
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