Abstract—Students are the key asset for any higher education institutions and their success in achieving the best academic performance within study planned period are resulting in producing graduates with high educational and professional quality skills. These graduates will become great leaders, manpower and play an important role in country’s economic and social development. In most Higher Education Institutions, the students’ studies length problem has not been investigated comprehensively despite the seriousness of this problem and its impact in the long and short terms. The aim of this research is to develop mining classification model based on decision tree to support academic administrators in decision making by defining features of linger students which could be used to develop an early warning system that has the ability to predict the students "who" might exceed the planned study length period. Data from faculty of computers and information technology at Uuniversity of Tabuk, KSA, has been collected using a survey method; students from male and female sections are participated in this survey. Then the data is preprocessed, after preprocessing of the data, C4.5 algorithm has been applied to discover the classifications.
Index Terms—Data mining, classification, computer in education, C4.5, linger students commas.
Osman A. Abdalla and Osman A. Abdalla are with the Information Technology Department, Faculty of Computing and Information Technology, Univesity of Tabuk, Tabuk, CO 71491 KSA (e-mail: o_mohammed@ ut.edu.sa, a.elfaki @ ut.edu.s).
Majed M. Aborokbah is with the Computer Science Department, Faculty of Computing and Information Technology, Univesity of Tabuk, Tabuk, CO 71491 KSA (e-mail: m.aborokbah @ ut.edu.sa).
Cite: Osman A. Abdalla, Majed M. Aborokbah, and Abdelrahman Osman ELfaki, "Developing Classification Model to Investigate the Problem of Computing Students Studies Length," International Journal of Machine Learning and Computing vol. 9, no. 2, pp. 174-180, 2019.