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Mining Student Academic Performance on ITE subjects using Descriptive Model Approach

Author Affiliations

  • 1College of Computer Studies, Laguna State Polytechnic University, Brgy. Bubukal Sta. Cruz, Laguna, Philippines

Res. J. Computer & IT Sci., Volume 4, Issue (11), Pages 1-15, November,20 (2016)

Abstract

Data mining techniques has been useful to corporate world, however, it is transferable to educational environment. One of the objective of higher educational institution is to acquire potential alumni contributors. Secondary educational level echoes the educational value on college performance, the exact same reason the educational institution recognizes the significance of student admission, monitoring and evaluate potential students with good academic performance. Contemporary research paper on educational data mining focuses on applying data mining techniques in the context of student performance measurement and predictions. In this research, the descriptive model association and descriptive statistics cross tabulation is used to determine the subject area proficiencies of secondary educational institution and generate association rules using Predictive Apriori algorithm to discover hidden patterns. CRISP-DM methodology is used in this study to guide the researcher. The knowledge obtained from the model describes the subject area expertise of the high school as well as the subject ineptness of the school that would decision makers in student admission and academic planning of the educational institution.

References

  1. Baradwaj B. and Pal S. (2011)., Mining Educational Data to Analyze Student s Performance., International Journal of Advanced Computer Science and Applications, 2(6), 63-69.
  2. Han J. and Kamber M. et. al. (2006)., Data Mining: Concepts and Techniques., 2nd edition. The Morgan Kaufmann Series in Data Management Systems, Jim Gray, Series Editor.
  3. Baker R.S.J.d. and Yacef K. (2009)., The State of Educational Data Mining in 2009: A Review and Future Visions., Journal of Educational Data Mining, 1(1), 3-17.
  4. Ramesh V., Parkavi P. and Ramar K. (2013)., Predicting Student Performance: A Statistical and Data Mining Approach., International Journal of Computer Applications, 63(8), 09758887.
  5. Bienkowski M., Feng M. and Means B. (2012)., Enhancing Teaching and Learning through Educational Data Mining and Learning Analytics: An Issue Brief., Office of Educational Technology, U.S. Department of Education, 1-57.
  6. Romero C. and Ventura S. (2010)., Educational Data Mining: A Review of the State of the Art., Systems, Man, and Cybernetics, 40(6), 601-618. doi: 10.1109/tsmcc.2010. 2053532.
  7. Ranjan J. and Malik K. (2007)., Effective educational process: a data-mining approach., The Journal of Information and Knowledge Management Systems, 37(4), 502-515.
  8. Luan J. (2004)., Data Mining Applications in Higher Education., Executive Report SPSS, http://www.pse.pt/ Documentos/Data%20mining%20in%20higher%20education.pdf.
  9. Luan J. (2002)., Data mining knowledge management in higher education potential applications., Proc. Workshop Assoc. Inst. Res. Int. Conf., 1-18.
  10. Luan J., Zhao C.M. and Hayek J. (2004)., Exploring a new frontier in higher education research: A case study analysis of using data mining techniques to develop NSSE National Survey of Student Engagement institutional typologies., California Association for Instituional Research, California, USA.
  11. Waiyamai K. (2003)., Improving Quality of Graduate Students by Data Mining., Dept. of Computer Engineering, Faculty of Engineering, Kasetsart University, Bangkok, Thailand http://slideplayer.com/slide/8285125/.
  12. Delavari N., Amnuaisuk S.P. and Beikzadeh M.R. (2008)., Data Mining Application in Higher Learning Institutions., Informatics in Education - International Journal, 7(1), 31-54.
  13. Goyal M. and Vohra R. (2012)., Applications of Data Mining in Higher Education., IJCSI International Journal of Computer Science, 9(2), 1,
  14. Chapman et al, (2000)., CRISP-DM 1.0 - Step-by-step Data Mining Guide., https://www.the-modeling-agency .com/crisp-dm.pdf.
  15. Scheffer T. (2001)., Finding Association Rules That Trade Support Optimally against Confidence., Proc. of the 5th European Conf. on Principles and Practice of Knowledge Discovery in Databases, 424-435, Springer-Verlag.
  16. Agrawal R., Imieliński T. and Swami A. (1993)., Mining association rules between sets of items in large databases., Proceedings of the 1993 ACM SIGMOD international conference on Management of data - SIGMOD
  17. Agrawal R. and Srikant R. (1994)., Fast Algorithms for Mining Association Rules., In Proc. of the 20th Int. Conf. on Very
  18. Witten I.H. and Frank E. (2005)., Data Mining: Practical machine learning tools and techniques., 2nd Edition, Morgan Kaufmann, San Francisco.
  19. Khattak A.M., Khan A.M., Lee S. and Young Koo (2010)., Analyzing Association Rule Mining and Clustering on Sales Day Data with XLMiner and Weka., , International Journal of Database Theory and Application, 3(1), 13-22.