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Predicting Student Performance: A Statistical and Data Mining Approach
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 63 - Number 8 |
| Year of Publication: 2013 |
| Authors: V. Ramesh, P. Parkavi, K. Ramar |
10.5120/10489-5242
|
V. Ramesh, P. Parkavi, K. Ramar . Predicting Student Performance: A Statistical and Data Mining Approach. International Journal of Computer Applications. 63, 8 ( February 2013), 35-39. DOI=10.5120/10489-5242
Abstract
Predicting the performance of a student is a great concern to the higher education managements. The scope of this paper is to identify the factors influencing the performance of students in final examinations and find out a suitable data mining algorithm to predict the grade of students so as to a give timely and an appropriate warning to students those who are at risk. In the present investigation, a survey cum experimental methodology was adopted to generate a database and it was constructed from a primary and a secondary source. The obtained results from hypothesis testing reveals that type of school is not influence student performance and parents' occupation plays a major role in predicting grades. This work will help the educational institutions to identify the students who are at risk and to and provide better additional training for the weak students.
References
- M. Ramaswami and R. Bhaskaran, "A CHAID Based Performance Prediction Model in Educational Data Mining", International Journal of Computer Science Issues Vol. 7, Issue 1, No. 1, January 2010.
- Nguyen Thai-Nghe, Andre Busche, and Lars Schmidt-Thieme, "Improving Academic Performance Prediction by Dealing with Class Imbalance", 2009 Ninth International Conference on Intelligent Systems Design and Applications.
- L. Arockiam, S. Charles, I. Carol, P. Bastin Thiyagaraj, S. Yosuva, V. Arulkumar, "Deriving Association between Urban and Rural Students Programming Skills", International Journal on Computer Science and Engineering Vol. 02, No. 03, 2010, 687-690
- P. Cortez, and A. Silva, "Using Data Mining To Predict Secondary School Student Performance", In EUROSIS, A. Brito and J. Teixeira (Eds. ), 2008, pp. 5-12.
- S. B. Kotsiantis, C. J. Pierrakeas, and P. E. Pintelas, "Preventing student dropout in distance learning using machine learning tachniques", In proceedings of 7th International Conference on Knowledge-Based Intelligent Information and Engineering Systems (KES 2003), pp. 267- 274, 2003. ISBN 3-540-40803-7.
- Kalles D. , Pierrakeas C. , Analyzing student performance in distance learning with genetic algorithms and decision trees, Hellenic Open University, Patras, Greece,2004.
- Woodman, R. (2001). Investigation of factors that influence student retention and success rate on Open University courses in the East Anglia region. M. Sc. Dissertation, Sheffield Hallam University, UK.
- Vandamme, J. -P. , Meskens, N. , & Superby, J. -F. (2007). Predicting academic performance by data mining methods. Education Economics, 15(4), 405-419.
- B. Minaei-Bidgoli, G. Kortemeyer, and W. F. Punch, "Enhancing Online Learning Performance: An Application of Data Mining Method", In proceedings of The 7th IASTED International Conference on Computers and Advanced Technology in Education (CATE 2004), Kauai, Hawaii, USA, pp. 173-8, August 2004.
- Larose, D. T. , "Discovering Knowledge in Data: An Introduction to Data Mining", ISBN 0-471-66657-2, John Wiley & Sons, Inc, 2005.
- Chapman, P. , Clinton, J. , Kerber, R. , Khabaza, T. ,Reinartz, T. , Shearer, C. and Wirth, R. . "CRISP-DM 1. 0 : Step-by-step data mining guide, NCR Systems Engineering Copenhagen (USA and Denmark), DaimlerChrysler AG (Germany), SPSS Inc. (USA) and OHRA Verzekeringenen Bank Group B. V (The Netherlands), 2000".
- Z. N. Khan, "Scholastic Achievement of Higher Secondary Students in Science Stream", Journal of Social Sciences, Vol. 1, No. 2, 2005, pp. 84-87.
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