Selecting Optimal Subset of Features for Student Performance Model

Educational data mining (EDM) is a new growing research area and the essence of data mining concepts are used in the educational field for the purpose of extracting useful information on the student behavior in the learning process. Classification methods like decision trees, rule mining, and Bayesian network, can be applied on the educational data for predicting the student behavior like performance in an examination. This prediction may help in student evaluation. As the feature selection influences the predictive accuracy of any performance model, it is essential to study elaborately the effectiveness of student performance model in connection with feature selection techniques. The main objective of this work is to achieve high predictive performance by adopting various feature selection techniques to increase the predictive accuracy with least number of features. The outcomes show a reduction in computational time and constructional cost in both training and classification phases of the student performance model.

[1]  Huan Liu,et al.  Toward integrating feature selection algorithms for classification and clustering , 2005, IEEE Transactions on Knowledge and Data Engineering.

[2]  G. Holmes,et al.  Developing innovative applications in agriculture using data mining , 1999 .

[3]  Qasem A. Al-Radaideh,et al.  Mining Student Data Using Decision Trees , 2006 .

[4]  Robert C. Holte,et al.  Very Simple Classification Rules Perform Well on Most Commonly Used Datasets , 1993, Machine Learning.

[5]  Andreas Fuchsberger,et al.  Computer Security: A Machine Learning Approach , 2008 .

[6]  อนิรุธ สืบสิงห์,et al.  Data Mining Practical Machine Learning Tools and Techniques , 2014 .

[7]  R. Bhaskaran,et al.  A CHAID Based Performance Prediction Model in Educational Data Mining , 2010, ArXiv.

[8]  Sri Ramakrishna,et al.  FEATURE SELECTION METHODS AND ALGORITHMS , 2011 .

[9]  Anongnart Srivihok,et al.  Comparisons of classifier algorithms: Bayesian network, C4.5, decision forest and NBTree for Course Registration Planning model of undergraduate students , 2008, 2008 IEEE International Conference on Systems, Man and Cybernetics.

[10]  Mofreh A. Hogo,et al.  Evaluation of E-Learners Behaviour using Different Fuzzy Clustering Models: A Comparative Study , 2010, ArXiv.

[11]  Saurabh Pal,et al.  Mining Educational Data to Analyze Students' Performance , 2012, ArXiv.

[12]  Hany M. Harb,et al.  Selecting Optimal Subset of Features for Intrusion Detection Systems , 2011 .

[13]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[14]  Didier Cottet,et al.  The performance prediction model , 2000 .

[15]  Alvaro Ortigosa,et al.  Recommendation in Higher Education Using Data Mining Techniques , 2009, EDM.

[16]  R. Bhaskaran,et al.  A Study on Feature Selection Techniques in Educational Data Mining , 2009, ArXiv.

[17]  L. Ladha,et al.  FEATURE SELECTION METHODS AND ALGORITHMS , 2011 .