PREDICTION OF STUDENTS' SUCCESS IN MATHEMATICS BY A CLASSIFICATION TECHNIQUE VIA POLYHEDRAL CONIC FUNCTIONS

Abstract

There has been a lot of work that has been already done using data mining in educational institutes and organizations and due to great success, the people are getting more and more interested in this field. In this paper a not long ago developped polyhedral conic functions classification algorithm is applied to a dataset of student performance in mathematics. Implemantations are made in MATLAB and WEKA. Results are shown in tables. This method can be applied to various datasets related with education. It will be helpfull for all educational fields.

Keywords

• Educational data mining,classification,polyhedral conic functions,mathematics education

References

1. Alpaydın, E. (2010). Introduction To Machine Learning. The MIT Press Cambridge, Massachusetts London, England. Anderberg, M. R. (1973). Cluster Analysis for Applications. Academic Press. Baker, R.S.J.d. (2010) Data Mining for Education. In McGaw, B., Peterson, P., Baker, E. (Eds.) International Encyclopedia of Education (3rd edition), vol. 7, pp. 112-118. Oxford, UK: Elsevier. Barnes, T., Desmarais, M., Romero, C., & Ventura, S. (2009) (Eds.) Educational Data Mining 2009: 2nd International Conference on Educational Data. Cordoba, Spain. July 1-3. Cortez P. (2008). Students’ Performance Data Set. UCI repository of machine learning databases. Technical report, Department of Information and Computer Science, University of California, Irvine, available online at: https://archive.ics.uci.edu/ml/datasets/Student+Performance Gasimov, R.N. & Öztürk, G. (2006). Separation via Polyhedral Conic Functions. Optimization Methods and Software, 21/ 4 :527-540. Kaufman, L. & P, J, Rousseeuw. (1990). Finding Groups in Data. John Wiley & Sons, New York. Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection, International Joint Conference on Artificial Intelligence. Kusiak, A. (2001). Data Analysis: Models and Algorithms. Proc. SPIE Vol. 4191, pp. 1-9. Romero, C. & Ventura S. (2010). Educational Data Mining: A Review of the State of the Art, IEEE Transactions on Systems, Man, and Cybernetics—PART C: Applications and Reviews, Vol. 40, No. 6, pp. 601-618 Thakar, P. & Mehta, A. & Manisha. (2015). Performance Analysis nad Prediction in Educational Data Mining: A Research Travelogue. International Journal of Computer Applications 110(15): 60-68.