CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Early Prediction of Students Performance using Machine Learning Techniques

by Anal Acharya, Devadatta Sinha
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 107 - Number 1
Year of Publication: 2014
Authors: Anal Acharya, Devadatta Sinha
10.5120/18717-9939

Anal Acharya, Devadatta Sinha . Early Prediction of Students Performance using Machine Learning Techniques. International Journal of Computer Applications. 107, 1 ( December 2014), 37-43. DOI=10.5120/18717-9939

@article{ 10.5120/18717-9939,
author = { Anal Acharya, Devadatta Sinha },
title = { Early Prediction of Students Performance using Machine Learning Techniques },
journal = { International Journal of Computer Applications },
issue_date = { December 2014 },
volume = { 107 },
number = { 1 },
month = { December },
year = { 2014 },
issn = { 0975-8887 },
pages = { 37-43 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume107/number1/18717-9939/ },
doi = { 10.5120/18717-9939 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:39:57.153663+05:30
%A Anal Acharya
%A Devadatta Sinha
%T Early Prediction of Students Performance using Machine Learning Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 107
%N 1
%P 37-43
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In recent years Educational Data Mining (EDM) has emerged as a new field of research due to the development of several statistical approaches to explore data in educational context. One such application of EDM is early prediction of student results. This is necessary in higher education for identifying the "weak" students so that some form of remediation may be organized for them. In this paper a set of attributes are first defined for a group of students majoring in Computer Science in some undergraduate colleges in Kolkata. Since the numbers of attributes are reasonably high, feature selection algorithms are applied on the data set to reduce the number of features. Five classes of Machine Learning Algorithm (MLA) are then applied on this data set and it was found that the best results were obtained with the decision tree class of algorithms. It was also found that the prediction results obtained with this model are comparable with other previously developed models.

References
  1. Romero, C. and Ventura, S. 2007, Educational data mining: A survey from 1995 to 2005, Expert Systems with Applications, 135–146.
  2. Castro, F. , Vellido, T. , Àngela Nebot, and Mugica F. , Applying Data Mining Techniques to e-Learning Problems.
  3. Nguyen, N. N. , Janeck, P, Haddawy, P. , 2007, A Comparative Analysis of Techniques for Predicting Academic Performance, 37th ASEE/IEEE Frontiers in Education Conference.
  4. Kotsiantis, S. , Piarrekeas, C. , Pintelas, P. ,2007. Predicting Students' performance in Distance Learning using Machine Learning Techniques, Applied Artificial Intelligence, 18:411-426.
  5. Ramaswami, M. , Bhaskaran, R. ,2010, A CHAID Based Performance Prediction Model in Educational Data Mining, International Journal of Computer Science Issues, Vol. 7, Issue 1, No. 1.
  6. Minei-Bidgoli, B. , Kashy, D. , Kortemeyer G. , Punch W F, 2003. Predicting Student Performance: An Application of Data Mining Methods with the Educational Web-Based System LON-CAPA, 33rd ASEE/IEEE Frontiers in Education Conference,.
  7. Kovacic, Z. J. ,2007, Early Prediction of Student Success: Mining Students Enrolment Data, Informing Science & IT Education Conference .
  8. Karamouzis, S. , and Vrettos, A, 2008, An Artificial Neural Network for Predicting Student Graduation Outcomes, Proceedings of the World Congress on Engineering and Computer Science , San Francisco, USA.
  9. Oladukun, V. O. , Adebanjo, A. T. , Charles-Obawa, O. E. , Predicting Students' Academic Performance using Artificial Neural Network: A Case Study of an Engineering Course.
  10. Dash, M. , Liu H. ,1997, Feature Selection for Classification, Intelligent Data Analysis, 131–156.
  11. Kotsiantis S. B. , 2007, Supervised Machine Learning: A Review of Classification Techniques, Informatica 31, 249-268.
  12. Haan, J. , Kamber M. , Data Mining-Concepts and Techniques, Third Edition, Elsivier.
  13. Ali, S. , Smith K. ,2006, On learning algorithm selection for classification, Applied Soft Computing 6, 119–138.
  14. Domingos, P,. A Few Useful Things to Know about Machine Learning,.
  15. Livieris, E. , Drakopoulou, E. , Pintelas, P. , Predicting students' performance using artificial neural networks.
  16. Stefanowski, J. , An Experimental Study of Methods Combining Multiple Classifiers - Diversified both by Feature Selection and Bootstrap Sampling.
  17. Zhao, Y. , and Zhang, Y. ,Comparison of decision tree methods for finding active objects
  18. Acharya, A. , Sinha, D. , 2014, Application of Feature Selection Methods in Educational Data Mining , International Journal of Computer Applications 103(2):34-38.
  19. WEKA Manual for Version 3-6-10.
Index Terms

Computer Science
Information Sciences

Keywords

Educational Data Mining College Education Machine Learning Result Prediction Kappa Statistic F-Measure WEKA.

Powered by PhDFocusTM