A Comparative Study on Student Academic Performance Prediction Using ID3 and C4.5 Classification Algorithms
DOI:
https://doi.org/10.26438/ijcse/v8i4.106111Keywords:
ID3, Classification, PredictionAbstract
The ability to predict a student’s performance on a given concept is an important tool for the education institutions, as it allows them to understand the ability of students and derive important methods to enhance their knowledge levels. It is the responsibility of educational institutions to have an approximate prior knowledge of their students to predict their performance in future academics and to train them in various activities. It is used to identify bright students and also provides them an opportunity to pay attention to and improve the slow learners. For predicting the student academic performance a data mining technique under classification is used. I have analyzed the data set containing information about students, such as full name, Roll number, scores in board examinations of classes X and XII, Rank in Eamcet examinations, branch and admission type. ID3 and C4.5 classification algorithms are applied to predict the performance of newly admitted students in their future examinations. In this paper, the performance of ID3 and C4.5 algorithms are compared in terms of parameters like accuracy, error rate and the execution time and the experimental Results shown that C4.5 was found to be best in terms of execution time.
References
[1] Kalpesh Adhatrao, Aditya Gaykar, Amiraj Dhawan, Rohit Jha and Vipul Honrao “Predicting Students’ Performance using ID3 and C4.5 classification algorithms”, International Journal of Data Mining & Knowledge Management Process (IJDKP) Vol.3, No.5, pp.39-52,September 2013.
[2] Brijesh Kumar Bharadwaj and Saurabh Pal, “Mining Educational Data to Analyze Students Performance”, International Journal of Advances in Computer Science and Applications,Vol. 2(6) , pp. 63- 69, 2011.
[3] Surjeet Kumar Yadav, “Data Mining: A Prediction for Performance improvement of Engineering Students using Classification‖”, World of Computer Science and Information Technology Journal (WCSIT) ISSN: 2221- 0741 Vol. 2, No. 2, 51-56, 2012.
[4] Crist´obal Romero," Educational Data Mining: A Review of the State of the Art," IEEE Transactions On Systems, Man, and Cybernetics— Part C: Applications And Reviews, Vol. 40, No. 6, November 2010.
[5] Mining, H., Wenying, N. and Xu, L., (2009) “An improved decision tree classification algorithm based on ID3 and the application in score analysis”, Chinese -Control and Decision Conference (CCDC), pp 1876- 1879.
[6] Xiaoliang, Z., Jian, W., Hongcan Y., and Shangzhuo, W., (2009) “Research and Application of the improved Algorithm C4.5 on Decision Tree”, International Conference on Test and Measurement (ICTM), Vol. 2, pp 184-187.
[7] M. Mayilvaganan,D .Kalpanadevi," Comparison of Classification Techniques for predicting the Performance of Students Academic Environment," in International Conference on Communication and Network Technologies (ICCNT), 2014.
[8] R.S.J.D Baker and K.Yacef, “The State of Educational Data Mining in 2009: A Review and Future Visions” , Journal of Educational Data Mining, 1, Vol 1, No 1, 2009.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
