Graph-based Ensemble Machine Learning for Student Performance Prediction
This work addresses the need for more reliable student performance prediction to enhance educational support and teaching quality, representing an incremental improvement over existing methods.
The paper tackled the problem of unstable and inaccurate student performance prediction by proposing a graph-based ensemble machine learning method, which improved prediction accuracy by up to 14.8% compared to traditional algorithms.
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.