An Educational Data Mining System for Advising Higher Education Students

Educational  data mining  is  a  specific  data   mining field applied to data originating from educational environments, it relies on different  approaches to discover hidden knowledge  from  the  available   data. Among these approaches are   machine   learning techniques which are used to build a system that acquires learning from previous data. Machine learning can be applied to solve different regression, classification, clustering and optimization problems.

In  our  research, we propose  a “Student  Advisory  Framework” that  utilizes  classification  and  clustering  to  build  an  intelligent system. This system can be used to provide pieces of consultations to a first year  university  student to  pursue a  certain   education   track   where  he/she  will  likely  succeed  in, aiming  to  decrease   the  high  rate   of  academic  failure   among these  students.  A real case study  in Cairo  Higher  Institute  for Engineering, Computer  Science  and  Management  is  presented using  real  dataset   collected  from  2000−2012.The dataset has two main components: pre-higher education dataset and first year courses results dataset. Results have proved the efficiency of the suggested framework.

• Heba Mohammed Nagy
• Walid Mohamed Aly
• Osama Fathy Hegazy

• Classification
• Clustering
• Educational Data Mining (EDM)
• Machine Learning.

[1] T. M. Mitchell. Machine Learning. McGraw-Hill, New York, 1997.
[2] Shi Na, Liu Xumin, and Guan Yong. Research on k-means clustering
algorithm: An improved k-means clustering algorithm. In Intelligent
Information Technology and Security Informatics (IITSI), 2010 Third
International Symposium on, pages 63–67, 2010.
[3] A. M. El-Halees M. M. Abu Tair. Mining educational data to improve
students’ performance: A case study. International Journal of Information
and Communication Technology Research, 2(2): 140–146, April 2011.
[4] S. Karthik M. Sukanya, S. Biruntha and T. Kalaikumaran. Data mining:
Performance improvement in education sector using classification and
clustering algorithm. In Proceedings of the International Conference on
Computing and Control Engineering, ICCCE 2012, 2012.
[5] Mahfuza Haque Md. Hedayetul Islam Shovon. Prediction of student
academic performance by an application of k-means clustering algorithm.
International Journal of Advanced Research in Computer Science and
Software Engineering, 2(7): 353–355, July 2012.
[6] M. tech Er. Rimmy Chuchra. Use of data mining techniques for the
evaluation of student performance: a case study. International Journal of
Computer Science and Management Research, 1(3): 425–433, October
2012.
[7] Brijesh Kumar Bhardwaj and Saurabh Pal. Data mining: A prediction for
performance improvement using classification. (IJCSIS) International
Journal of Computer Science and Information Security, 9(4), April 2011.
[8] Md. Hedayetul Islam Shovon and Mahfuza Haque. An approach of
improving students academic performance by using k-means clustering
algorithm and decision tree. (IJACSA) International Journal of Advanced
Computer Science and Applications, 3(8): 146–149, August 2012.