  Student performance prediction is a critical research problem to understand
the students' needs, present proper learning opportunities/resources, and
develop the teaching quality. However, traditional machine learning methods
fail to produce stable and accurate prediction results. In this paper, we
propose a graph-based ensemble machine learning method that aims to improve the
stability of single machine learning methods via the consensus of multiple
methods. To be specific, we leverage both supervised prediction methods and
unsupervised clustering methods, build an iterative approach that propagates in
a bipartite graph as well as converges to more stable and accurate prediction
results. Extensive experiments demonstrate the effectiveness of our proposed
method in predicting more accurate student performance. Specifically, our model
outperforms the best traditional machine learning algorithms by up to 14.8% in
prediction accuracy.
