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Attribute Selection Methods with Classification Techniques in Educational Data Mining to Predict Student’s Performance: A Survey

Priyanka D. Vaghasiya, Sahista Machchhar

Abstract


In recent year, huge amount of data related to education and particularly of students are stored in Database. To deal with these much data research communities showing greatest interest in data mining in Educational field. The goal of any educational organization is to increase the academic performance of students and ultimately progress of institute. To extract knowledge hidden within the dataset by means of analytical method is not easy. So the data mining techniques helps to transform knowledge into some human understable form. This paper provides literature available for educational data mining, what are the attributes which may affect the student’s performance, how attribute selection methods are useful to select best attribute from available attributes, different data mining techniques used to know student’s academic performance.

Keywords


Attribute Selection Methods, Data Mining, Educational Data Mining, Prediction.

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References


U. Fayyad and R. Uthurusamy, “Data mining and knowledge discovery in databases,” Commun. ACM, vol. 39, pp. 24–27, 1996.

M. Ramaswami and R. Bhaskaran, “A Study on Feature Selection Techniques in Educational Data Mining,” M K Uni Tamilnadu, vol.1,no.1, December 2009.

B. Jantawan and Cheng-Fa Tasai, “A Comparison of Filter and Wrapper Approaches with Data Mining Techniques for Categorical Variables Selection,” vol. 2, no.6, IJIRCCE, pp. 4501-4508, June 2014.

Suchita Borkar and K.Rajeswari, “Attributes Selection for Predicting Students’ Academic Performance using Education Data Mining and Artificial Neural Network,” IJCA, vol. 86, pp. 25-29, January 2014.

Carlos Marquez-Vera, Cristobal Romero Morales and Sebastian Ventura Soto, “Predicting School Failure and Dropout by Using Data Mining Techniques,” IEEE journal of latin-american learning technologies, vol. 8, no.1, pp.1- 7, February 2013.

Mark A. Hall and Geoffrey Holmes, “Benchmarking Attribute Selection Techniques for Discrete Class Data Mining,” IEEE Transactions on knowledge and data engineering, vol.15, no.6, pp.1437-1447, December 2003.

Megha Aggrawal, “Performance Analysis of Different Feature Selection Methods in Intrusion Detection,” IEEE Journal of Scientific & Technology Research, vol.2, no.6, pp.225-231, June 2013.

Ellen Pitt and Richi Nayak, “The Use of Various Data Mining and Feature Selection Methods in the Analysis of a Population Survey Dataset,” AIDM, vol.84, June 2007.

César Vialardi, Jorge Chue, Juan Pablo Peche, et.al. “A data mining approach to guide students through the enrollment process based on academic performance,” Springer Science, pp.217-248, March 2011.

Ritu Ganda and Vijay Chahar, “A Comparative Study on Feature Selection Using Data Mining Tools,” IJARCSSE, pp.26-33, Sept 2013.

Komal S.S. and Prof. B Supriya Reddy, “A Review: Mining Eucational Data to Forecast Failure of Engineering Students,” IJARCSSE, pp.628-635, December 2013.

L.P. Dringus and T. Ellis, “Using data mining as a strategy for assessing asynchronous discussion forums,” Computers & Education, vol.45, no.1, pp.141–160, 2005.

Igor Kononenko, “Estimating attributes: Analysis and extensions of RELIEF,” Springer Link, vol.784, no.1994, pp.171-182, 2005.

Lei Yu and Huan Liu, “Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution,” ICML, 2003.

Asha Gowda K., A.S.Manjunath and M>A.Jayaram, “Comparative study of attribute selection using gain ratio and Correlation based Feature Selection,” IJITKM, vol.2, no.2, pp.271-277, 2010.


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