Suggested Model for Business Intelligence in Higher Education

Suggested Model for Business Intelligence in Higher Education

Zaidoun Alzoabi (Arab International University, Syria), Faek Diko (Arab International University, Syria) and Saiid Hanna (Arab International University, Syria)
Copyright: © 2012 |Pages: 17
DOI: 10.4018/978-1-61350-050-7.ch011
OnDemand PDF Download:
Available
$37.50
Current Special Offers
No Current Special Offers
TOTAL SAVINGS: $37.50

Abstract

BI is playing a major role in achieving competitive advantage in almost every sector of the market, and the higher education sector is no exception. Universities, in general, maintain huge databases comprising data of students, human resources, researches, facilities, and others. Data in these databases may contain decisive information for decision making. In this chapter we will describe a data mining approach as one of the business intelligence methodologies for possible use in higher education. The importance of the model arises from the reality that it starts from a system approach to university management, looking at the university as input, processing, output, and feedback, and then applies different business intelligence tools and methods to every part of the system in order to enhance the business decision making process. The chapter also shows an application of the suggested model on a real case study at the Arab International University.
Chapter Preview
Top

Literature Review

Data mining is a set of systems that are really embedded in a larger BI system (Apte, et. al, 2002). Data mining itself is made up of several analytical, mathematical, and statistical techniques. Before applying these methods to data, the data has to be typically organized into history repositories, known as data warehouses (Luan, 2004).

Data mining has been used in several industries such as financial, telecommunication, and education (Delavari and Beikzadeh, 2008). Education organizations have shown interest in data mining due to the potential data mining can provide in this domain. For example, (Erdoğan and Timor, 2005) used data mining in studying the effect of admission test results on students performance in higher education. (Shaeela, et al 2010). (Luan, 2002) studied the potential data mining can provide to the decision makers in higher education.

Complete Chapter List

Search this Book:
Reset