A Business Intelligence Framework for Analyzing Educational Data

• Published in: Sustainability Vol. 12; no. 14; p. 5745
• Main Authors: Villegas-Ch, William, Palacios-Pacheco, Xavier, Luján-Mora, Sergio
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2020
• Subjects: Algorithms; Business intelligence; Case studies; Data analysis; Data mining; Digital libraries; Education; Learning; Literature reviews; Methods; Online data bases; Students; Sustainability
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su12145745

Currently, universities are being forced to change the paradigms of education, where knowledge is mainly based on the experience of the teacher. This change includes the development of quality education focused on students’ learning. These factors have forced universities to look for a solution that allows them to extract data from different information systems and convert them into the knowledge necessary to make decisions that improve learning outcomes. The information systems administered by the universities store a large volume of data on the socioeconomic and academic variables of the students. In the university field, these data are generally not used to generate knowledge about their students, unlike in the business field, where the data are intensively analyzed in business intelligence to gain a competitive advantage. These success stories in the business field can be replicated by universities through an analysis of educational data. This document presents a method that combines models and techniques of data mining within an architecture of business intelligence to make decisions about variables that can influence the development of learning. In order to test the proposed method, a case study is presented, in which students are identified and classified according to the data they generate in the different information systems of a university.