A data mining approach to profiling of MBA students

• Main Authors: Ang, Chooi Leng, Segumpan, Reynaldo G., Adam, Mohamad Zainol Abidin, Mohd Shaharanee, Izwan Nizal
• Format: Monograph
• Language: English; English
• Published: Universiti Utara Malaysia 2007
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://repo.uum.edu.my/7907/1/Pro.pdf; http://repo.uum.edu.my/7907/3/1.Ang%20Chooi%20Leng.pdf; http://repo.uum.edu.my/7907/; http://lintas.uum.edu.my:8080/elmu/index.jsp?module=webopac-l&action=fullDisplayRetriever.jsp&szMaterialNo=0000293954

Data mining is the non-trivial discovery of meaningful, new correlations, patterns and trends, and the extraction of implicit, previously unknown, and potentially useful information from large amounts of data (Berry & Linoff, 2000).This paper explored the strategic application of data mining in higher education, more specifically in the MBA programme of Universiti Utara Malaysia (UUM).The database known as Graduate Academic Information System (GAIS) was the main data source used for mining 847 usable out of 1,758 available data sets utilising E-miner.Analyses showed that age when enrolled for the programme and years of working experience were significantly important for segmenting the MBA students.It was also found that centre of study and entrance qualification were statistically significant in differentiating MBA students who are likely to complete their study successfully.Moreover, ethnic group (Chinese), mode of study (full-time), CGPA (3.13), marital status (married), and centre of study (UUM) were significant predictors linked to successful completion of MBA.The researchers recommend, among others, careful consideration of significant variables that had statistical bearing on programme completion.