Datawarehouse design for educational data mining

Business intelligence (BI) builds upon a set of tools and applications that enable the analysis of vast amounts of information (Big Data). Educational institutions handle large volumes of Big Data every year. There is a strong need for the use of BI in these institutions to improve their processes and support decision making. The core technology in a BI project is a datawarehouse (DW). This paper describes the design considerations for the implementation of the DW in an educational scenario. The DW will be used in a knowledge discovery process to handle the information for the analysis of key performance indicators using educational data mining (EDM) techniques. The DW along with an enterprise architecture (EA) repository are the key technological assets of a knowledge management framework (KMF). This framework was designed to put order in the creation, capture, transfer and digitalization of knowledge. This guide and the framework are two of the outcomes of a research project in a private university. Furthermore, a case study suggests how to choose the best methodology in higher institutions. In the case study the steps for the DW design are presented. This study can be useful for academics and practitioners that plan to design a DW to analyze information using EDM techniques.

[1]  Eduardo Kunzel Teixeira,et al.  Teorias utilizadas nas investigações sobre gestão do conhecimento , 2012 .

[2]  Sergio Luján-Mora,et al.  Knowledge Management Framework using Enterprise Architecture and Business Intelligence , 2016, ICEIS.

[3]  Alejandro Peña-Ayala Review: Educational data mining: A survey and a data mining-based analysis of recent works , 2014 .

[4]  Ryan S. Baker,et al.  The State of Educational Data Mining in 2009: A Review and Future Visions. , 2009, EDM 2009.

[5]  Esteban Zimányi,et al.  Conceptual Data Warehouse Design , 2014 .

[6]  Gottfried Vossen,et al.  Conceptual Data Warehouse Design , 2000 .

[7]  Ahmed Khan,et al.  Integrating Knowledge Management with Business Intelligence Processes for Enhanced Organizational Learning , 2013 .

[8]  Alejandro Peña Ayala,et al.  Educational data mining: A survey and a data mining-based analysis of recent works , 2014, Expert Syst. Appl..

[9]  C. Eden,et al.  Strategic management of stakeholders : theory and practice , 2011 .

[10]  Stephen R. Gardner Building the data warehouse , 1998, CACM.

[11]  John Lloyd,et al.  Identifying Key Components of Business Intelligence Systems and Their Role in Managerial Decision Making , 2011 .

[12]  Jing Zhang,et al.  EDUCATIONAL DATA MINING , 2016 .

[13]  Anna Sidorova,et al.  Business Intelligence (Bi) Success and the Role of Bi Capabilities , 2011, Intell. Syst. Account. Finance Manag..

[14]  Sergio Luján-Mora,et al.  Extending the UML for Multidimensional Modeling , 2002, UML.

[15]  Sebastián Ventura,et al.  Data mining in education , 2013, WIREs Data Mining Knowl. Discov..

[16]  Il-Yeol Song,et al.  A UML profile for multidimensional modeling in data warehouses , 2006, Data Knowl. Eng..