Attribute Selection on Student Academic and Social Attributes Based on Randomized And Synthetic Dataset

  • Authors

    • Mr. C S.Sasikumar
    • Dr. A.Kumaravel
    https://doi.org/10.14419/ijet.v7i4.39.28504
  • Attribute Selection, Best First Search, Ranker Search, WEKA, accuracy, classification, randomization, synthetic data

  • Abstract

    Subset selection is important when the underline dataset contains insignificant attributes as they don’t contribute much to the final results especially, in the context of student performance prediction studies. Hence the exploration of procedures for such goal becomes relevant. Though this is the case, in general it happens to be NP-hard problem. In this paper we apply Best First Search, Greedy Search, and Ranker method (Information Gain Ratio) to select the attributes using weka tool with learning models based on decision rules, decision trees, neural networks, bayes NET and meta classifiers. The performance comparisons are made with ranked and non-ranked search methods over the synthetic and randomized datasets derived from original students’ performance dataset.

     

     

  • References

    1. [1] Jai Ruby & K. David, “A study model on the impact of various indicators in the performance of students in higher education“, IJRET International Journal of Research in Engineering and Technology, Vol. 3, Issue 5, May-2014, pp.750-755.

      [2] Baker R.S.J.D., & Yacef K, 2009,‟ “The state of educational data mining in 2009: A review and future vision“, Journal of Educational Data Mining, I, 3-17.

      [3] Monika Goyal & Rajan Vohra, “Applications of Data Mining in Higher Education†IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 2, No 1, March 2012, pp.130-120.

      [4] Mohd Maqsood Ali, International Journal of Computer Science and Mobile Computing Vol.2 Issue. 4, April- 2013, pg. 374-383

      [5] Alaa M El-Hales, “Mining Educational Data to Analyze Learning Behaviour A case study“, 2009

      [6] Rowley, J., “Is higher education ready for knowledge management?â€, International Journal of Educational Management, 2000, vol. 14(7), pp. 325–333.

      [7] Lubega, J. T., Omona, W., & Weide, T. V. D., “Knowledge management technologies and higher education processes_: approach to integration for performance improvementâ€, International Journal of Computing and ICT Research, 2011, vol. 5(Special Issue), pp. 55–68.

      [8] J. Shana, T. Venkatachalam, “Identifying Key Performance Indicators and Predicting the Result from Student Data.†International Journal of Computer Applications (0975 – 8887) Vol.25-No.9 July 2011.

      [9] Brijesh Kumar Baradwaj, Saurabh Pal,†Mining Educational Data to Analyze Students„Performance†IJACSA, Vol.2, No.6, 2011

      [10] El-Hales-A.(2008),‟Mining Students Data to Analyze Learning Behavior: A Case Study‟, The 2008 International Arab Conference of Information Technology(ACIT2008)- Conference Proceedings, University of Sfax, Tunisia,Dec 15-18.

      [11] Kovacic Z. J., “Early prediction of student success: Mining student enrollment dataâ€, Proceedings of Informing Science & IT Education Conference 2010.

      [12] Shanmuga Priya. K, & Senthil Kumar A.V., “Improving the Student‟s Performance Using Educational Data Mining, 2013.

      [13] Ramaswami M., & Bhaskaran R., “CHAID Based Performance Prediction Model in Educational Data Mining“, IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 1, No. 1, 2010.

      [14] Bengio Y., Buhmann J. M., Embrechts M., & Zurada J. M., "Introduction to the special issue on neural networks for data mining and knowledge discovery," IEEE Trans. Neural Networks, vol. 11, pp. 545-549, 2000.

      [15] Vasile P. B., “Analysis and Predictions on Students‟ Behavior Using Decision Trees in Weka Environmentâ€. Proceedings of the ITI 2007 29th Int. Conf. on Information Technology Interfaces, IEEE, (2007).

      [16] M. Wook, Y. H. Yahaya, N. Wahab, M. R. M. Isa, N. F. Awang, and H. Y. Seong, “Prediction NDUM student's academic performance using data mining techniques,†presented at the International Conference on Computer and Electrical Engineering, 2009.

      [17] Jai Ruby & K. David, “Predicting the Performance of Students in Higher Education Using Data Mining Classification Algorithms - A Case Study “, IJRASET International Journal for Research in Applied Science & Engineering Technology, Volume 2 Issue XI, November 2014 [18] Romero, C. and Ventura, S. (2007) „Educational data mining: A Survey from 1995 to 2005‟, Expert Systems with Applications (33), pp. 135-146.

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  • How to Cite

    C S.Sasikumar, M., & A.Kumaravel, D. (2018). Attribute Selection on Student Academic and Social Attributes Based on Randomized And Synthetic Dataset. International Journal of Engineering & Technology, 7(4.39), 1069-1072. https://doi.org/10.14419/ijet.v7i4.39.28504

    Received date: 2019-03-18

    Accepted date: 2019-03-18