International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
권호 표지
DOI QR코드

DOI QR Code

A Better Prediction for Higher Education Performance using the Decision Tree

  • Hilal, Anwar (Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University) ;
  • Zamani, Abu Sarwar (Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University) ;
  • Ahmad, Sultan (Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University) ;
  • Rizwanullah, Mohammad (Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University)
  • Received : 2021.04.05
  • Published : 2021.04.30
Download PDF
⟨ Previous Next ⟩

Abstract

Data mining is the application of specific algorithms for extracting patterns from data and KDD is the automated or convenient extraction of patterns representing knowledge implicitly stored or captured in large databases, data warehouses, the Web, other massive information repositories or data streams. Data mining can be used for decision making in educational system. But educational institution does not use any knowledge discovery process approach on these data; this knowledge can be used to increase the quality of education. The problem was happening in the educational management system, but to make education system more flexible and discover knowledge from it huge data, we will use data mining techniques to solve problem.

Keywords

References

  1. Al-Radaideh, Q.A., Al-Shawakfa, E.M., and Al-Najjar, M.I. (2006) "Mining Students Data Using Decision Trees", The 2006 International Arab Conference on Information Technology.
  2. Chen, G., Liu, C., Ou, K., and Liu, B. (2000) Discovering Decision Knowledge from Web Log Portfolio managing Classroom process by applying decision tree and data cube technology. Journal of Education Computing Research, 23(3), pp. 305-332 https://doi.org/10.2190/5jnm-b6hp-yc58-pm5y
  3. Han, J., Kamber, M. (2001) Data mining: Concepts and Techniques. USA: Morgan Kaudmann Publishers.
  4. Hsu, P.L., Lai, R., Chiu, C.C., and Hsu, C.I (2003) "The hybrid of association rule algorithms and genetic algorithms for tree induction: an example of predicting the student course performance", Expert System with Applications, 25, pp. 51-62 https://doi.org/10.1016/S0957-4174(03)00005-8
  5. Mierle K., Laven K., Roweis S., and Wilson G. (2005) Mining Student CVS Repositories for Performance Indicators.
  6. Romero, C., Ventura, S. (2007) "Educational data mining: A survey from 1995 to 2005", Expert Systems with Applications, 33, pp. 135-146 https://doi.org/10.1016/j.eswa.2006.04.005
  7. Tang, C., Yin, H., Li, T., Lau, R., Li, Q., and Kilis, D. (2000) Personalized courseware construction based on web data mining. In Proceeding of the first international conference on web information system engineering, Washington, DC, USA, pp. 204-211
  8. Zhang, C., Zhang, S. (2002) Association Rule mining. Springer-Verlag Berlin Heidelberg.
  9. Delavari N, Beikzadeh M. R. A New Model for Using Data Mining in Higher Educational System, 5th International Conference on Information Technology based Higher Education and Training: ITEHT '04, Istanbul, Turkey, 31st May-2nd Jun 2004.