PREDICTION OF STUDENT ACADEMIC PERFORMANCE BY AN APPLICATION OF DATA MINING TECHNIQUES

One of the significant facts in higher learning institution is the explosive growth educational data. These data are increasing rapidly without any benefit to the management. The main objective of any higher educational institution is to improve the quality of managerial decisions and to impart quality education. Good prediction of student's success in higher learning institution is one way to reach the highest level of quality in higher education system. There are many prediction model available with difference approach in student performance was reported by researcher, but there is no certainty if there are any predictors that accurately determine whether a student will be an academic genius, a drop out, or an average performer. The aims of this study was to apply the kernel method as data mining techniques to analyze the relationships between students's behavioral and their success and to develop the model of student performance predictors . This is done by using Smooth Support Vector Machine (SSVM) classification and kernel k-means clustering techniques. The results of this study reported a model of student academic performance predictors by employing psychometric factors as variables predictors.

[1]  Jing Luan,et al.  Knowledge management : building a competitive advantage in higher education , 2002 .

[2]  Mark A. Girolami,et al.  Mercer kernel-based clustering in feature space , 2002, IEEE Trans. Neural Networks.

[3]  Jiawei Han,et al.  Data Mining: Concepts and Techniques, Second Edition , 2006, The Morgan Kaufmann series in data management systems.

[4]  Yuh-Jye Lee,et al.  SSVM: A Smooth Support Vector Machine for Classification , 2001, Comput. Optim. Appl..

[5]  T.R. Rhoads,et al.  Learning from student data , 2004, Proceedings of the 2004 IEEE Systems and Information Engineering Design Symposium, 2004..

[6]  Sebastián Ventura,et al.  Educational data mining: A survey from 1995 to 2005 , 2007, Expert Syst. Appl..

[7]  E.N. Ogor.  Student Academic Performance Monitoring and Evaluation Using Data Mining Techniques , 2007, Electronics, Robotics and Automotive Mechanics Conference (CERMA 2007).

[8]  Vincent Tinto.  Limits of Theory and Practice in Student Attrition , 1982 .

[9]  Kalina Yacef,et al.  Educational Data Mining: a Case Study , 2005, AIED.

[10]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[11]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[12]  Jing Luan,et al.  Data Mining and Its Applications in Higher Education , 2002 .

[13]  Suryanti Awang,et al.  Smooth support vector machine for face recognition using principal component analysis , 2009 .

[14]  Rong Zhang,et al.  A large scale clustering scheme for kernel K-Means , 2002, Object recognition supported by user interaction for service robots.

[15]  N. Delavari,et al.  Application of enhanced analysis model for data mining processes in higher educational system , 2005, 2005 6th International Conference on Information Technology Based Higher Education and Training.