A Review on Various Algorithms for Student Performance Prediction

Authors(6) :-Mayuri S. Dongre, Neha S.Vidya, Vanita D. Telrandhe, Shweta R. Chaudhari, Anup A. Umrikar, Prof. Prachiti V. Adghulkar

Data in educational institutions are growing progressively along these lines there is a need of progress this tremendous data into helpful data and information utilizing data mining. Educational data mining is the zone of science where diverse techniques are being produced for looking and investigating data and this will be valuable for better comprehension of understudies and the settings they learned. Classification of data objects in view of a predefined learning of the articles is a data mining and information administration procedure utilized as a part of collection comparable data questions together. Decision Tree is a valuable and well known classification method that inductively takes in a model from a given arrangement of data. One explanation behind its prominence comes from the accessibility of existing calculations that can be utilized to assemble decision trees. In this paper we will survey the different ordinarily utilized decision tree calculations which are utilized for classification. We will likewise contemplating how these decision tree calculations are appropriate and valuable for educational data mining and which one is ideal.

Authors and Affiliations

Mayuri S. Dongre
Department of computer Science & Engineering, Suryodaya collage of Engineering & Technology, Nagpur, Maharashtra, India
Neha S.Vidya
Department of computer Science & Engineering, Suryodaya collage of Engineering & Technology, Nagpur, Maharashtra, India
Vanita D. Telrandhe
Department of computer Science & Engineering, Suryodaya collage of Engineering & Technology, Nagpur, Maharashtra, India
Shweta R. Chaudhari
Department of computer Science & Engineering, Suryodaya collage of Engineering & Technology, Nagpur, Maharashtra, India
Anup A. Umrikar
Assistant Professor, Department of computer Science & Engineering, Suryodaya collage of Engineering & Technology, Nagpur, Maharashtra, India
Prof. Prachiti V. Adghulkar

Educational data mining (EDM), Classification, Dropout Prediction, Selection Failure

  1. Carlos Márquez-Vera, Cristóbal Romero Morales, and Sebastián Ventura Soto, “Predicting School Failure and Dropout by Using Data Mining Techniques”, Ieee Journal Of Latin-American Learning Technologies, Vol. 8, No. 1, February 2013 IEEE.
  2. IU Qin, “Data Mining Method Based on Computer Forensics-based ID3 Algorithm”, 978-1-4244-5265-1/10/$26.00©2010IEEE
  3. Rong Cao, Lizhen Xu, “Improved C4.5 Algorithm for the Analysis of Sales”, Sixth Web Information Systems and Applications Conference, 978-0-7695-3874-7/09 $25.00 DOI 10.1109/WISA36, © 2009 IEEE
  4. Gaganjot Kaur , Amit Chhabra , “Improved J48 Classification Algorithm for the Prediction of Diabetes” , International Journal of Computer Applications (0975 – 8887) Volume 98 – No.22, July 2014.
  5. Tomasz Bujlow, Tahir Riaz, Jens Myrup Pedersen, “A method for classification of network traffic based on C5.0 Machine Learning Algorithm”, Workshop on Computing, Networking and Communications, 2012 IEEE.
  6. Raisul Islam Rashu, Naheena Haq, Rashedur M Rahman, “Data Mining Approaches to Predict Final Grade by Overcoming Class Imbalance Problem”, 17th International Conference on Computer and Information Technology (ICCIT) 2014.
  7. Hina Gulati, “Predictive Analytics Using Data Mining Technique”, 978-9-3805-4416-8/15/$31.00c 2015 IEEE.
  8. Rutvija Pandya , Jayati Pandya , “C5.0 Algorithm to Improved Decision Tree with Feature Selection and Reduced Error Pruning ”, International Journal of Computer Applications (0975 – 8887) Volume 117 – No. 16, May 2015.
  9. Alana M. de, Morais and Joseana M. F. R. Araújo, Evandro B. Costa, “Monitoring Student Performance Using Data Clustering and Predictive Modelling”, 978-1-4799-3922-0/14/$31.00 ©2014 IEEE.
  10. Kin Fun Li, David Rusk and Fred Song, “Predicting Student Academic Performance”, 2013 Seventh International Conference on Complex, Intelligent, and Software Intensive Systems.
  11. Mishra, T., Kumar, D. Gupta, S.,” Mining Students' Data for Prediction Performance”, Advanced Computing & Communication Technologies (ACCT), 2014 Fourth International Conference on 8-9 Feb. 2014.
  12. B.K.Bhardwaj and S.Paul, “Mining Educational Data to Analyze Students Performance”, International Journal Advanced Computer Science and application Vol. 2 No. 6, 2011.
  13. Yohannes Kurniawan, Erwin Halim, “Use Data Warehouse and Data Mining to Predict Student Academic performance in Schools: A Case Study (Perspective Application and Benefits)”.
  14. B.K.Bhardwaj and S.Paul, “Mining Educational Data to Analyze Students Performance”, International Journal Advanced Computer Science and application Vol. 2 No. 6, 2011.
  15. Lewis, R.J. (200). An Introduction to Classification and Regression Tree (CART) Analysis. 2000 Annual Meeting of the Society for Academic Emergency Medicine, Francisco, California.
  16. Mehta, M., Agrawal, R., and Rissanen, J. (1996). SLIQ: A fast scalable classifier for data mining. In EDBT 96, Avignon, France
  17. Shafer, J., Agrawal, R., and Mehta, M. (1996). Sprint: A scalable parallel classifier for data mining. Proceedings of the 22nd international conference on very large data base. Mumbai (Bombay), India.

Publication Details

Published in : Volume 5 | Issue 1 | January-February 2019
Date of Publication : 2019-02-28
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 235-239
Manuscript Number : CSEIT195167
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Mayuri S. Dongre, Neha S.Vidya, Vanita D. Telrandhe, Shweta R. Chaudhari, Anup A. Umrikar, Prof. Prachiti V. Adghulkar, "A Review on Various Algorithms for Student Performance Prediction", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 1, pp.235-239, January-February-2019.
Journal URL : http://ijsrcseit.com/CSEIT195167

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