Predict Students' Performance by Using J48 Algorithm

Authors

  • Myo Thandar Tun  Information Technology Supporting and Maintenance Department, University of Computer Studies, Mandalay, Myanmar
  • Yin Yin Htay  Faculty of Information Science, University of Computer Studies, Magway, Myanmar

DOI:

https://doi.org/10.32628/IJSRSET2073124

Keywords:

Data Mining, Student Academic Performance, Classification, J48 algorithm.

Abstract

The critical issue to the academic community of higher education is to monitor the progress of students’ academic performance. We can use data mining techniques for this purpose. J48 algorithm is one of the famous classification algorithms present today to generate decision trees in data mining technique. The data set used in this study is taken from University of Computer Studies (Mandalay). Weka machine learning tool is applied to make classification. In this work, we tested result classification accuracy was computed. This J48 classification algorithm give accuracy with 78.2%.

References

  1. B.K. Bharadwaj and S. Pal. “Mining Educational Data to Analyze Students Performance”, International Journal of Advance Computer Science and Applications (IJACSA), Vol. 2, No. 6, pp.63-69, 2011.
  2. P.K. Srimani and Annapurna S Kamath.” Data Mining Techniques for the Performance Analysis of a Learning Model-A case study” International Journal of Computer Applications (0975-8887), volume 53-No 5 September 2012.
  3. Surjeet Kumar Yadav, Bhardwaj and S Pal.” Data Mining Applications: A Comparative Study for Predicting Student’s performance” International Journal of Innovative Technology & Creative Engineering (ISSN;2045-711) Vol.1 no.12 December.
  4. C. MARQUEZ-VERA, C. ROMERO and S. VENTURA “Predicting School Failure Using Data Mining” 2011
  5. Sajadin Sembiring, M. Zarlis ET.AL.” Prediction of Student Academic Performance by an Application of Data Mining Techniques”2011 International Conference on Management and Artificial Intelligence IPEDR vol.6(2011) IACSIT press, Bali, Indonesia.
  6. K. Nandhiini and S. Saranya “ID3 Classifier for pupils Status Prediction” International Journal of Computer Application (0975-8887) vol 57-No.3, November 2012.
  7. R.R. Kabra and R.S. Bichkar.” Performance of Engineering Students using Decision Trees”. International Journal of Computer Applications (0975-8887) volume 36-No.11, December 2011.
  8. Mrinal Pandey and Vivek Kumar Sharma. “A Decision Tree Algorithm Pertaining to the Student Performance Analaysis and Prediction”. International Journal of Computer Applications (0975-8887) volume 61-No.13, January 2013.
  9. Ricardo Mendes and Joao P. Vilela, “Privacy-Preserving Data Mining: Methods, Metrics and Applications”, IEEE,2017.
  10. A. Dinesh Kumar, R. Pandi Selvam, K. Sathesh Kumar, “Review on Prediction Algorithms in Educational Data Mining”, International Journal of Pure Applied and Mathematics (IJPAM), Volume-118.
  11. J. John Kennedy, R. Pandi Selvam, “Cloud-Centric IoT based Decision Support System for Gestational Diabetes Mellitus using Optimal Support Vector Machine”, International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8, Issue-1, May 2019.
  12. Maryam Zaffar, Manzoor Ahmed, K. S. Savita, “Performance Analysis of Feature Selection Algorithm for Educational Data Mining”, IEEE Conference on Big Data and Analytics (ICBDA), 2017.
  13. Jiawei Han, Micheline Kamber, Jian Pei,” Data Mining Concepts and Techniques”, 3rd Edition.
  14. Athanasios S. Drigas, P. Lelipoulous, “The Use of Big Data in Education”, Internal Journal of Computer Science Issues, Vol-11, September 2014.
  15. R. Swathi, N. Pavan Kumar, L. KiranKranth, “Systematic Approach on Big Data Analytics in Education Systems”, International Conference on Intelligent Computing and Control Systems (ICICCS), 2017.
  16. A. Dinesh Kumar, R. Pandi Selvam, V. Palanisamy, “Prediction of Student Performance using Hybrid Classification”, International Journal of Recent Technology and Engineering (IJRTE), November 2019.
  17. Ramanathan L, Saksham Dhanda, Suresh Kumar D, “Predicting Students’ Performance using Modified ID3 Algorithm”, International Journal of Engineering and Technology (IJET), 2013.
  18. https://www.softwaretestinghelp.com/data-mining-tools

International Journal of Scientific Research in Science, Engineering and Technology

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Published

2020-06-30

Issue

Volume 7, Issue 3, May-June-2020

Section

Research Articles

License

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Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Myo Thandar Tun, Yin Yin Htay "Predict Students' Performance by Using J48 Algorithm" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 7, Issue 3, pp.578-582, May-June-2020. Available at doi : https://doi.org/10.32628/IJSRSET2073124