The week's pick
Random Articles
Reseach Article
Predicting Examination Results using Association Rule Mining
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 116 - Number 1 |
| Year of Publication: 2015 |
| Authors: Omprakash Chandrakar, Jatinderkumar R. Saini |
10.5120/20298-2330
|
Omprakash Chandrakar, Jatinderkumar R. Saini . Predicting Examination Results using Association Rule Mining. International Journal of Computer Applications. 116, 1 ( April 2015), 7-10. DOI=10.5120/20298-2330
Abstract
Higher education has changed a lot in the last decade. The use of various innovative techniques and technologies, especially ICT in teaching learning process is increasing day by day. Number of information systems has been developed and successfully implemented to support educational processes. These systems typically capture almost every data regarding a student, right from their enrollment into a course to graduation and placement. If these data are analyzed and visualize properly, can provide valuable knowledge that can be used to enhance their learning skill and to predict threats if any, well in advance so that appropriate measure can be taken to avoid it. Knowledge Discovery in Database (KDD) is usually referred as Data mining. It is a process of extracting new and potential useful information from large databases. Data mining tools are used to identify any pattern or predict future trends and behaviors. This enables decision maker to make proactive and knowledge-driven decisions. This paper presents an application of data mining in higher education. Association rule mining is applied to analyze the performance of students in their examinations and predicts the outcome of the forthcoming examination. This prediction allows student and teacher to identify the subjects which need more attention even before the commencement of semester.
References
- Vikas Sitaram Chomal, Dr. Jatinderkumar R. Saini, "A study and analysis of paradigm shifts in education triggered by technology", IJRESS Volume 3, Issue 1 (January 2013) ISSN: 2249-7382
- http://www. dkms. com/papers/dmkdd. pdf.
- I. H. Witten, E. Frank: Data Mining, Practical Machine Learning Tools and Techniques, 2nd ed. , Morgan Kaufmann Publishers, San Francisco, 2005.
- Nidhi Arora, JatinderKumar R. Saini, "A Fuzzy Probabilistic Neural Network for Student's Academic Performance Prediction", International Journal of Innovative Research in Science, Engineering and Technology, Vol. 2, Issue 9, September 2013 ISSN: 2319-875.
- Y. Ma, B. Liu, C. K. Wong, P. S. Yu, S. M. Lee, "Targeting the Right Students Using Data Mining", Proceedings of the 6th ACM SIGKDD international conference on knowledge discovery and data mining, Boston 2000.
- Nidhi Arora, Jatinderkumar R. Saini, "Predicting Student Academic Performance using Fuzzy ARTMAP Network", International Journal of Advances in Engineering Science and Technology, V3N3, Page no. 187-192, ISSN: 2319-1120
- Hideko Kitahama, "Data Mining through Cluster Analysis Evaluation on Internationalization of Universities in Japan".
- Jing Luan, PhD Chief Planning and Research Officer, Cabrillo College Founder, Knowledge Discovery Laboratories "Data Mining Applications in Higher Education".
- Baker, R. S. J. d. , Corbett, A. T. , Aleven, V. (2008) More Accurate Student Modeling Through Contextual Estimation of Slip and Guess Probabilities in Bayesian Knowledge Tracing. Proceedings of the 9th International Conference on Intelligent Tutoring Systems, 406-415.
- Thulasi Kumarthulasi. kumar@uni. edu, University of Northern Iowa " Theoretical Basis for Data Mining Approach to Higher Education Research".
- Amershi, S. , Conati, C. (2006) Automatic Recognition of Learner Groups in Exploratory Learning Environments. Proceedings of ITS 2006, 8th International Conference on Intelligent Tutoring Systems.
- Baker, R. S. J. d. , Corbett, A. T. , Roll, I. , Koedinger, K. R. (2008) Developing a Generalizable Detector of When Students Game the System. User Modeling and User-Adapted Interaction,18, 3, 287-314.
Index Terms
Keywords