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A Comparative Study of Classification Algorithms in EDM using 2 Level Classification for Predicting Student’s Performance

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Ankita Katare, Shubha Dubey

Ankita Katare and Shubha Dubey. A Comparative Study of Classification Algorithms in EDM using 2 Level Classification for Predicting Student’s Performance. International Journal of Computer Applications 165(9):35-40, May 2017. BibTeX

	author = {Ankita Katare and Shubha Dubey},
	title = {A Comparative Study of Classification Algorithms in EDM using 2 Level Classification for Predicting Student’s Performance},
	journal = {International Journal of Computer Applications},
	issue_date = {May 2017},
	volume = {165},
	number = {9},
	month = {May},
	year = {2017},
	issn = {0975-8887},
	pages = {35-40},
	numpages = {6},
	url = {},
	doi = {10.5120/ijca2017914023},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


In higher education the performance of students is a most challenge work day by day in academic as well as in other curricular activities. As they all know that internet technology is growing as much as faster, but the learning approach of students are not up to the mark. The emerging research community which helps to find the solution to the said problem is Educational Data Mining. In present scenario, the huge students' data is stored in educational database. That type of database contains widely open or secret information to improve student performance. In our proposed work, we will have tested it on reputed dataset, which can be downloaded from a well known organization UCI repository and dataset name is student-mat.csv. This work has been investigated the process of classification of plethora of student’s data. Classification plot data into pre-determined groups of classes. It is often mentioned to as supervised learning because the classes are determined before analyzing the data. The work will to be divided into two parts. The first part will be the entropy based feature selection, after that classification process has to be performed. For the classification, we would have used 2 level classification method i.e, SVM and KNN. Later than observe the performance prediction of students based on parameters like accuracy, sensitivity, specificity of proposed method and is to be compared with some previous methods results.


  1. Abeer Badr El Din Ahmed, Ibrahim Sayed Elaraby.”Data Mining: A prediction for Student's Performance Using Classification Method’’ World Journal of Computer Application and Technology, 2(2):pp. 43-47, 2014.
  2. Brijesh Kumar Baradwaj, Saurabh Pal.” Mining Educational Data to Analyze Students‟ Performance” (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 2, No. 6, 2011.
  3. Ahmed Mueen, Bassam Zafar, Umar Manzoor.” Modeling and Predicting Student’s Academic Performance Using Data Mining Techniques” I.J. Modern Education and Computer Science, 11, pp.36-42, November 2016.
  4. S. Indhu Priya, P. Devaki.” Evaluating Students Performance in Placements Activity” International Journal of Innovations & Advancement in Computer Science (IJIACS) ISSN 2347 – 8616 Volume 6, Issue 1,January 2017.
  5. Ruhi R. Kabra, R. S. Bichkar.” Student’s Performance Prediction Using Genetic Algorithm” International Journal of Computer Engineering and Applications, Volume VI, Issue III, June 2014.
  6. Shiwani Rana,Roopali Garg.” Evaluation of Student’s Performance of an Institute Using Clustering Algorithms” International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 5,pp. 3605-3609,2016.
  7. Hashmia Hamsa, Simi Indiradevi, Jubilant J.Kizhakkethottam.”Student Academic Performance Prediction Model Using Decision tree and Fuzzy Genetic Algorithm ” Global Colloquium in Recent Advancement and Effectual Researches in Engineering, Science and Technology (RAEREST), Procedia Technology 25, pp.326 – 332,2016.
  8. C. Anuradha, T. Velmurugan.” A Comparative Analysis on the Evaluation of Classification Algorithms in the Prediction of Students Performance” Indian Journal of Science and Technology, Vol 8(15), DOI: 10.17485/ijst/2015/v8i15/74555, July 2015.
  9. Amirah Mohamed Shahiri, Wahidah Husain, Nur’aini Abdul Rashid.” A Review on Predicting Student’s Performance using Data Mining Techniques” The Third Information Systems International Conference,Procedia Computer Science 72, pp.414 – 422,2015.
  10. Xin chen, Mihaela Vorvoreanu, Krishna Madhavan.“Mining Social Media Data for Understanding Students Learning Experiences” IEEE Transactions on Learning technologies,Vol.7,No.3,September 2014.
  11. Pratiyush Guleria, Niveditta Thakur, Manu Sood. “Predicting Student performance Using Decision Tree Classifiers and information Gain” International Conference on Parallel, Distributed and Grid Computing, DOI: 10.1109/PDGC.2014.7030728,February 2015.
  12. Kiran parmar, Dinesh kumar Vaghela, Priyanka Sharma. “Performance Prediction of Students Using Distributed Data Mining” IEEE Sponsored 2nd International Conference on Innovations in Information Embedded and Communication Systems, DOI: 10.1109/ICIIECS.2015.7192860 ,August 2015.
  13. Alana Morais, Joseana M.F.R. Araujo, Evandro B.Costa. “Monitoring Students Performance Using Data Clustering and Predictive Modelling” Frontiers in Education Conference, DOI: 10.1109/FIE.2014.7044401 , February 2015.
  14. Ruchi Jain.” Application of KNN-Genetic Algorithm for Analysing Student Learning in Educational Data Mining paradigm” International Journal of Innovation Research in Computer and Communication Engineering,Vol.4,Issue 6,pp.10319-10323,June 2016.
  15. Susan Bergin, Aidan Mooney, John Ghent, Keith Quille.” Using Machine Learning Techniques to Predict Introductory Programming Performance” International Journal of Computer Science and Software Engineering (IJCSSE), Volume 4, Issue 12, pp.323-328,December 2015.
  16. Student Performance Data Set
  17. Surjeet Kumar Yadav, Brijesh Bharadwaj, Saurabh 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.
  18. Guleria Pratiyush, Sood Manu.” Classifying Educational Data Using Support Vector Machines:A Supervised Data Mining Technique” Indian Journal of Science and Technology, Vol 9(34), DOI: 10.17485/ijst/2016/v9i34/100206, September 2016.


Data Mining, EDM, Classification Algorithms, Entropy, Performance Prediction.