  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 behaviors of students in the learning
process. In this EDM, feature selection is to be made for the generation of
subset of candidate variables. 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. In this connection, the present study is devoted
not only to investigate the most relevant subset features with minimum
cardinality for achieving high predictive performance by adopting various
filtered feature selection techniques in data mining but also to evaluate the
goodness of subsets with different cardinalities and the quality of six
filtered feature selection algorithms in terms of F-measure value and Receiver
Operating Characteristics (ROC) value, generated by the NaiveBayes algorithm as
base-line classifier method. The comparative study carried out by us on six
filter feature section algorithms reveals the best method, as well as optimal
dimensionality of the feature subset. Benchmarking of filter feature selection
method is subsequently carried out by deploying different classifier models.
The result of the present study effectively supports the well known fact of
increase in the predictive accuracy with the existence of minimum number of
features. The expected outcomes show a reduction in computational time and
constructional cost in both training and classification phases of the student
performance model.
