Imbalanced educational data classification: an effective approach with resampling and random forest

Educational data mining is emerging in the data mining research arena. Despite an applied field of data mining techniques and methods, educational data mining is full of challenges that have not been completely resolved. Especially data classification in an academic credit system is a very tough task which must deal with imbalanced issues and missing data on the technical side and tackle the flexibility of the education system leading to the heterogeneity of data on the practical side. In this paper, we present our approach with a hybrid resampling scheme and random forest for the imbalanced educational data classification task with multiple classes based on student's performance. The proposed approach has not yet been available in educational data mining. Besides, it has been extensively proved in our empirical study to be effective for student's final study status prediction and usable in a knowledge-driven educational decision support system.