Towards a robust incomplete data handling approach to effective educational data classification in an academic credit system

Abstract

Educational data classification is an educational data mining task which classifies our students based on their study performance. Although many data classification techniques and methods are nowadays available, educational data classification is full of challenges emergent in an academic credit system. One of the challenges often encountered in educational data classification is data incompleteness to early identify in-trouble students. Hence, we aim at a robust approach for this inevitable challenging problem. Different from the existing works on incomplete data handling, our work explores the semantics of incomplete data in the education domain on the application side and the two-phase characteristics of the classification task on the technical side. As a result from an empirical study on real educational data sets with different percentages of incomplete data, it is found that the robust approaches with incomplete data handling based on their semantics in relation to class information can enhance the effectiveness of educational data classifiers.