Random Articles
Reseach Article
Attributes Selection for Predicting Students’ Academic Performance using Education Data Mining and Artificial Neural Network
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 86 - Number 10 |
| Year of Publication: 2014 |
| Authors: Suchita Borkar, K. Rajeswari |
10.5120/15022-3310
|
Suchita Borkar, K. Rajeswari . Attributes Selection for Predicting Students’ Academic Performance using Education Data Mining and Artificial Neural Network. International Journal of Computer Applications. 86, 10 ( January 2014), 25-29. DOI=10.5120/15022-3310
Abstract
Education Data mining plays an important role in predicting students' performance,. It is a very promising discipline which has an imperative impact. In this paper students' performance is evaluated and some attributes are selected which generate rules by means of association rule mining. . Artificial neural network checks accuracy of the results. A Multi-Layer Perceptron Neural Network is employed for selection of interesting features using 10 – fold cross validation. The artificial neural network selects 5 out of 8 attributes based on the accuracy obtained for correctly classified data. It is observed that in association rule mining important rules are generated using these selected attributes. The Experiment is conducted using Weka and real time data set available in the college premises.
References
- Ali Buldua, Kerem Üçgün,. Data mining application on students' data. Procedia Social and Behavioral Sciences 2 5251–5259, 2010.
- Singh, Randhir. An Empirical Study of Applications of Data Mining Techniques for Predicting Student Performance in Higher Education, 2013.
- Baha Sen, Emine Ucar. Evaluating the achievements of computer engineering department of distance education students with data mining methods. Procedia Technology 1 262 – 267, 2012.
- Baradwaj, Brijesh Kumar, and Saurabh Pal. Mining Educational Data to Analyze Students' Performance. Arxiv preprint arxiv: 1201. 3417, 2012.
- Castro, Félix, et al. Applying data mining techniques to e-learning problems. Evolution of teaching and learning paradigms in intelligent environment. Springer Berlin Heidelberg, 183-221, 2007.
- Huebner, Richard A. "A survey of educational. "
- Ramaswami, M. , and R. Bhaskaran. A CHAID based performance prediction model in educational data mining. Arxiv preprint arxiv: 1002. 1144, 2010.
- Kumar, Varun, and Anupama Chadha. Mining Association Rules in Student's Assessment Data. International Journal of Computer Science Issues 9. 5: 211-216, 2012.
- Cristo´bal Romero, Sebastia´n Ventura, Enrique Garc?´a, 2007. Data mining in course management systems: Moodle casestudy and tutorial. Received 5 March 2007; received in revised form 19 May 2007; accepted 25 May 2007.
- http://en. wikipedia. org/wiki/Weka
- Anwar, M. A. , and Naseer Ahmed. Knowledge Mining in Supervised and Unsupervised ssessment Data of Students' Performance. " 2011 2nd International Conference on Networking and Information Technology IPCSIT vol. Vol. 17. 2011.
- http://statistics. about. com/od/Formulas/ss/Correlation-Coefficient. htm
- Jiawei Han and Micheline Kamber, "datamining Concepts and Techniques", Elsevier Second Edition
- RAJESWARI, K. , and V. VAITHIYANATHAN. "ATTRIBUTE SELECTION USING ARTIFICIAL NEURAL NETWORKS–A CASE STUDY OF ISCHEMIC HEART. " Journal of Theoretical and Applied Information Technology 46. 1 (2012).
- Borkar, Suchita, and K. Rajeswari. "Predicting Students Academic Performance Using Education Data Mining. " (2013).
Index Terms
Keywords