Uncovering Hidden Information Within University's Student Enrollment Data Using Data Mining

Abstract

To date, higher educational organizations are placed in a very high competitive environment. To remain competitive, one approach is to tackle the student and administration challenges through the analysis and presentation of data, or data mining. This study presents the results of applying data mining to enrollment data of Sebha University in Libya. The results can be used as a guideline or roadmap to identify which part of the processes can be enhanced through data mining technology and how the technology could improve the conventional processes by getting advantages of it. Two main approaches were used in this study, namely the descriptive and predictive approaches. Cluster analysis was performed to group the data into clusters based on its similarities. For predictive analysis, three techniques have been used Neural Network, Logistic regression and the Decision Tree. The study shows that Neural Network obtains the highest results accuracy among the three techniques.