  One of the important measures of quality of education is the performance of
students in the academic settings. Nowadays, abundant data is stored in
educational institutions about students which can help to discover insight on
how students are learning and how to improve their performance ahead of time
using data mining techniques. In this paper, we developed a student performance
prediction model that predicts the performance of high school students for the
next semester for five courses. We modeled our prediction system as a
multi-label classification task and used support vector machine (SVM), Random
Forest (RF), K-nearest Neighbors (KNN), and Mult-layer perceptron (MLP) as
base-classifiers to train our model. We further improved the performance of the
prediction model using state-of-the-art partitioning schemes to divide the
label space into smaller spaces and use Label Powerset (LP) transformation
method to transform each labelset into a multi-class classification task. The
proposed model achieved better performance in terms of different evaluation
metrics when compared to other multi-label learning tasks such as binary
relevance and classifier chains.
