A Comparative Analysis of Classification Algorithms on Students’ Performance


  • Nilar Aye University of Computer Studies, Yangon




Data Mining, Error Measurement, Accuracy, Naive Bayesian, BayesNet, J48, Decision Stump


Recently educational system, many features control a student’s performance. Students should be well stimulated to study their education. Motivation leads to interest, interest leads to success in their lives. Appropriate assessment of abilities encourages the students to do better in their education. Data mining is to find out patterns by analyzing a large dataset and apply those patterns to predict the possibility of the future events. Data mining is a very critical field in educational area and it provides high potential for the schools and universities. In data mining, there are various classification techniques with various levels of accuracy. This paper focuses to make comparative evaluation of four classifiers such as J48, Naive Bayesian, Bayesian Network and Decision Stump by using WEKA tool.  This study is to investigate and identify the best classification technique to analyze and predict the students’ performance of University of Jordan.


(1) A. Dinesh Kumar, V.Radhika, "Mining Educational Data to Predicting Higher Secondary Students Performance", International Journal of Computational Intelligence and Informatics, Vol.6: No.2, September 2016, ISSN:2349-6363,pp:124-129

(2) A. K. Pandey and D. S. Rajpoot, “A Comparative Study of Classification Techniques by utilizing WEKA,” IEEE, pp. 219–224,

(3) Abdul Hamid andAmin, “AComparative Analysis of Classification Algorithms for Students College Enrollment Approval Using Data Mining”, Workshop on Interaction Design in Educational Environments,ISBN: 978-1-4503-3034-3,2014

(4) Agnik Dey, Abhirup Khasnabis, Ajeet Kumar, "Prediction and Analysis of Student Performance by Data Mining in WEKA", RCC Institute of Information Technology

(5) Amjad Abu Saa, "Educational Data Mining & Students' Performance Prediction", (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 7, No.5, 2016

(6) Anoopkumar M, A.M.J.Md. Zubair Rahman, "Model of Tuned J48 Classification and Analysis of Performance Prediction in Educational Data Mining", International Journal of Applied Engineering Research ISSN 0973-4562 Volumn 13, Number 20 (2018) pp.14717-14727

(7) Elaf Abu Amrieh, Thair Hamtini and Ibrahim Aljarah, "Mining Educational Data to Predict Student's academic Performance using Ensemble Methods", International Journal of Database Theory and Application, Vol.9, No.8 (2016), pp.119-136, ISSN:2005-4270-IJDTA

(8) Hilal Almarabeh, "Analysis of Students' Performance by Using Different Data Mining Classifiers", I.J.Modern Education and Computer Science, 2017, 8, 9-15

(9) Jai Ruby, Dr.K.David, "Predicting the Performance of Students in Higher Education using Data Mining Classification Algorithms- A Case Study", International Journal for Research in Applied Science & Engineering Technology (IJRASET), Volun 2, Issue XI, November 2014, ISSN:2321-9653

(10) Keshav Singh Rawat, "Comparative Analysis of Data Mining Techniques, Tools and Machine Learning Algorithms for Efficient Data Analytics", IOSR Journal of Computer Engineering (IOSR-JCE), e-ISSN:2278-0661,p-ISSN:2278-8727, Volumn 19, Issue 4, Ver.III (Jul.-Aug.2017),PP 56-61

(11) Kishansingh Rajput, Bhavesh A. Oza, "A Comparative Study of Classification Techniques in Data Mining", 2017 IJCRT, Volumn 5, Issue 3 September 2017,2017 IJCRT,ISSN:2320-2882

(12) Kotsiantis, S., C. Pierrakeas, P.Pintelas, "Prediction of Student’s Performance in Distance Learning Using Machine Learning Techniques", Applied Artificial Intelligence,Vol.18,2004,No5,411-426

(13) Margaret H. Danham,S. Sridhar, "Data mining, Introductory and Advanced Topics”, Person education , 1st ed., 2006

(14) Md. N. Amin, Md. A. Habib, “Comparison of Different Classification Techniques using WEKA for Hematological Data”, American Journal of Engineering Research, vol. 4, PP 55-61, No. 3(2015).

(15) Nguyen N., Paul J., &Peter H., "A Comparative Analysis of Techniques for Predicting Academic Performance", In Proceedings of the 37th ASEE/IEEE Frontiers in Education Conference. pp. 7-12, 2007

(16) R. Sumithaand Vinothkumar, “Predictionof Students Outcome Using Data Mining Techniques”, International Journal of Scientific Engineering and Applied Science,Volume-2, Issue-6, June 2016

(17) Ricardo Mendes And Joao P.Vilela, "Privacy- Preserving Data Mining: Methods, Metrics, and Applications”, IEEE, 2017.

(18) S.K Yadav, B. Bharadwaj, and S. Pal, 2012, "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.

(19) Sharon Carl, Glaston D'souza, Linet Varghese, "Implementation of Classification Algorithms and their Comparison for Educational Dataset", IJISET- International Journal of Innovative Science, Engineering & Technology, Vol. 3, Issue 3, March 2016, ISSN 2384-7968

(20) Sohajbir Singh Ubha, Gaganpreet Kaur Bhalla, "Data Mining for Prediction of Students' Performance in the Secondary Schools of the State of Punjab", International Journal of Innovative Research in Computer and Communication Engineering, Vol. 4, Issue 8, August 2016,ISSNV(Online): 2320-9801, ISSN(Print):2320-9798

(21) Sweta V. Parmar, Lokesh Kumar ,Sharma, "Comparative Study of Supervised Learning for Student Performance Evaluation", International Journal of Computer Engineering & Technology (IJCET), Volumn 9, Issue 2, March-april 2018, pp:32-38, Artical ID:IJCET_09_02_003

(22) Trilok Chand Sharma and Manoj Jain, “WEKA Approach for Comparative Study of Classification Algorithm”, International Journal of Advanced Research in Computer and Communication Engineering,Vol. 2, Issue 4, April 2013.

(23) Wikipedia contributors, “C4.5_algorithm,” Wikipedia, The Free Encyclopedia. Wikimedia Foundation, 28-Jan-2015.

(24) Wikipedia contributors, “Decision_stump,” Wikipedia, The Free Encyclopedia. Wikimedia Foundation, 23-Aug-2012




How to Cite

Aye, N. (2020). A Comparative Analysis of Classification Algorithms on Students’ Performance. Transactions on Networks and Communications, 8(2), 20–34. https://doi.org/10.14738/tnc.82.8267