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.
Keywords: Educational data mining, Feature selection, Classification algorithm, ASSISTments Platform dataset.
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ABOUT THE AUTHORS
Hany M. Harb
Prof. Dr. Hany M Harb Computers and Systems Engineering Dept. Faculty of Engineering, Azhar University Cairo, Egypt 0020122-4142158
Malaka A. Moustafa
Assistant Lecturer at Alson Higher Institute of Tourism, Hotels and Computer, Nasr City, Cairo
Hany M. Harb
Prof. Dr. Hany M Harb Computers and Systems Engineering Dept. Faculty of Engineering, Azhar University Cairo, Egypt 0020122-4142158
Malaka A. Moustafa
Assistant Lecturer at Alson Higher Institute of Tourism, Hotels and Computer, Nasr City, Cairo
