An inclusive survey of students performance with various data mining methods

  • Authors

    • A S. Arunachalam
    • K Rajeswari
    2018-06-08
    https://doi.org/10.14419/ijet.v7i2.33.14825
  • Educational Data Mining, Students Performance, Patterns, Classification
  • Educational institutions are the source of generating quality students in order to make them do better service for the nation. It is a must for all educational institutions to be aware of the competency and academic level of every student so as to study their performance. The key attributes to identify the performance are to have controlled parameter with clear data. So it is important to set the standards and calibration measures in order to make the study of students’ performance an efficient one. There are several tools and techniques available to perform this prediction study. Among all, Data mining is the best and the most efficient technique to handle prediction process. Among data mining, EDM (Educational Data Mining) is much more popular in the present century and hence it is beneficial to make a research on the current technique. This survey paper focuses on various Data Mining approaches in order to forecast student’s performance and bring clarity in students’ results and faculty’s contribution as a success.

     

     

  • References

    1. [1] Amirah Mohamed Shahiria, Wahidah Husaina,Nur’aini Abdul Rashida, “A Review on Predicting Student’s Performance using Data Mining Techniquesâ€, The Third Information Systems International Conference, Procedia Computer Science 72 pp 414 – 422, 2015.

      [2] Umadevi, D.Sundar, Dr.P.Alli,“A Study on Stock Market Analysis for Stock Selection – Naïve Investors’ Perspective using Data Mining Techniqueâ€, International Journal of Computer Applications (0975 – 8887),Vol 34– No.3, 2011.

      [3] NityaUpadhyay, VinodiniKatiyar, “A Survey of the Classification Techniques In Educational Data Miningâ€, International Journal of Computer Applications,Technology and Research, Vol.3,Issue 11, pp 725 – 728, 2014.

      [4] S. K. Yadav and S. Pal, “Data Mining: A prediction for performance improvement of Engineering students using classificationâ€, World of Computer Science and Information Technology Journal (WCSIT), Vol. 2, No. 2, pp51-56, 2012.

      [5] Hijazi,S.T., and Naqvi, R.S.M.M., †Factors Affecting Student’s Performance: A Case of Private Collegesâ€, Bangladesh e-Journal of Sociology, 2006.

      [6] A. S. Arunachalam and T. Velmurugan , “A Survey on Educational Data Mining Tools and Techniques,†International Journal of Data Mining Techniques and Applications, vol. 5, no. 2, pp. 167–171, Dec. 2016.

      [7] Shanmuga Priya, “Improving the student’s performance using Educational data miningâ€, International Journal of Advanced Networking and Application, Vol.4, pp1680- 1685, 2013

      [8] C. Marquez-Vera, C.Romero and S.Ventura, “Predicting School Failure Using Data Miningâ€,2011

      [9] U.K. Pandey, and S. Pal, “Data Mining: A prediction of performer or underperformer using classificationâ€, (IJCSIT) International Journal of Computer Science and Information Technology, Vol. 2(2), pp.686-690.

      [10] BakerRSJd, Yacef K, “The state of educational datamining in 2009: A review and future visionsâ€, 2009.

      [11] Ahmed, A.B.E.D. and Elaraby, I.S., “Data Mining: A prediction for Student's Performance Using Classification Methodâ€, World Journal of Computer Application and Technology, Vol 2, pp.43-47, 2014.

      [12] Sudheep Elayidom, Sumam Mary Idikkula& Joseph Alexander,“A Generalized Data mining Framework for Placement Chance Prediction Problems†, International Journal of Computer Applications, Volume 31– No.3, 2011.

      [13] Tripti, Dwivedi Diwakar Singh, “Analyzing Educational Data through EDM Process: A Surveyâ€, International Journal of Computer Applications, Vol 136 ,No.5, 2016

      [14] Baker RS,Yacef K, “ The state of educational data mining in 2009: A review and Future visionsâ€. JEDM-Journal of Educational Data Mining, 2009.

      [15] Umadevi, D. Sundar, Dr. P. Alli, “An Effective Time Series Analysis for Stock Trend Prediction Using ARIMA Model for Nifty Midcap-50â€,International Journal of Data Mining & Knowledge Management Process (IJDKP),Vol.3, No.1, 2013.

      [16] B. UmadeviD.Sundar, Dr.P.Alli,â€An Optimized Approach to Predict the Stock Market Behavior and Investment Decision Making using Benchmark Algorithms for Naive Investorsâ€, Computational Intelligence and Computing Research (ICCIC), 2013 IEEE International Conference on (IEEE Xplore Digital Library),pg1 -5.,2013.

      [17] K.R.Kavyashree, LakshmiDurga,“ A Review on Mining Students’ Data for Performance Predictionâ€, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 5, 2016.

      [18] Ryan S.J.d. Baker, “Data Mining for Educationâ€, International Encyclopedia of Education (3rd edition). Oxford, UK: Elsevier, 2009.

      [19] B. Umadevi,D.Sundar, Dr.P.Alli, “An Effective Time Series Analysis for Stock Trend Prediction Using ARIMA Model for Nifty Midcap-50â€,International Journal of Data Mining & Knowledge Management Process (IJDKP),Vol.3, No.1, 2013.

      [20] B. UmadeviD.Sundar, Dr.P.Alli,â€An Optimized Approach to Predict the Stock Market Behavior and Investment Decision Making using Benchmark Algorithms for Naive Investorsâ€, Computational Intelligence and Computing Research (ICCIC), 2013 IEEE International Conference on ( IEEE Xplore Digital Library), p.1 -5.,2013.

      [21] K.R.Kavyashree, Lakshmi Durga, “A Review on Mining Students’ Data for Performance Predictionâ€, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 5, 2016.

      [22] Sharma, Mamta, and Monali Mavani. "Accuracy Comparison of Predictive Algorithms of Data Mining: Application in Education Sector." Advances in Computing, Communication and Control. Springer Berlin Heidelberg, 2011. 189-194.

      [23] Siraj, Fadzilah, and Mansour Ali Abdoulha. "Uncovering hidden information within university's student enrollment data using data mining." Modelling & Simulation, 2009. AMS'09. Third Asia International Conference on. IEEE, 2009.

      [24] Pumpuang, Pathom, Anongnart Srivihok, and Prasong Praneetpolgrang. "Comparisons of classifier algorithms: Bayesian network, C4. 5, decision forest and NBTree for Course Registration Planning model of undergraduate students." Systems, Man and Cybernetics, 2008. SMC 2008. IEEE International Conference on. IEEE, 2008.

      [25] Romero, Cristóbal, Sebastián Ventura, Pedro G. Espejo, and César Hervás. "Data Mining Algorithms to Classify Students." In EDM, pp. 8-17. 2008.

      [26] Nghe, Nguyen Thai, Paul Janecek, and Peter Haddawy. "A comparative analysis of techniques for predicting academic performance." Frontiers in Education Conference-Global Engineering: Knowledge without Borders, Opportunities without Passports, 2007. FIE'07.37th Annual. IEEE, 2007.

      [27] Dharmarajan, K., and M. A. Dorairangaswamy. “Discovering Student E-Learning Preferred Navigation Paths Using Selection Page and Time Preference Algorithm. & quotâ€, International Journal of Emerging Technologies in Learning (iJET) Vol.12 (10), PP. 202-211 2017

  • Downloads

  • How to Cite

    S. Arunachalam, A., & Rajeswari, K. (2018). An inclusive survey of students performance with various data mining methods. International Journal of Engineering & Technology, 7(2.33), 522-525. https://doi.org/10.14419/ijet.v7i2.33.14825