THE ENHANCEMENT OF ODL STUDENT RECRUITING CAMPAIGN WITH DATA WAREHOUSE DEVELOPMENT AND DATA MINING TECHNIQUES

The purpose of this research was to develop an OLAP cube data warehouse, and, using data mining techniques, to support the university's public relations, admissions, and planning divisions in the efficient recruiting of students by surveying, through interviews; the opinions of management and operational personnel, and through documents; the attributes in application forms and annual reports. User requirements, source data and systems were all examined. The data warehouse and front-end applications developed are described below. 1. Student Data Warehouse—this repository was designed to store students' historical data and to facilitate analysis and reporting following the user requirements. Students' historical data including demographic data from 2001-2005 were extracted, loaded and transformed from source systems, then they were cleaned before uploading to the data warehouse using star schema. 2. OLAP Cub—this 122 multidimensional structure enables users to analyze the students' demographic data in many dimensions such as “Number of Registered Students in each year by Semester, Major, School, Gender, Occupation, Region, etc.” Predefined reports were created and published to an intranet and users were able to create ad-hoc reports through web browsers as well as XLAddin. 3. Data Mining—this technique finds hidden knowledge and patterns in ODL student data supporting decision making, using three algorithms: Naïve Bayes, Clustering and Association Rules. Occupation of students is the strongest factor influencing students' choices of Schools. Students' demographic data can be clustered into groups with similar or dissimilar characteristics such as “Single, Unemployed, Low Income ( School=School of Educational Studies” or “Occupation=Police, Marital Status=Single -> School=School of Law”. The results from the study indicated that users were satisfied using information and applications from the data warehouse, OLAP cube and data mining techniques which enable the university to reduce costs and to reach the desired enrolment target effectively