# Graph-based Ensemble Machine Learning for Student Performance Prediction

arXiv: https://arxiv.org/abs/2112.07893 · HTML: https://mlsanity.com/paper/2112.07893/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 20 references that are in the map.
- First posted on arXiv 15 Dec 2021.

## Builds on (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [LSTM: A Search Space Odyssey](https://mlsanity.com/paper/1503.04069/) (2015)
- [Survey of resampling techniques for improving classification performance in unbalanced datasets](https://mlsanity.com/paper/1608.06048/) (2016)
- [Dataset Geography: Mapping Language Data to Language Users](https://mlsanity.com/paper/2112.03497/) (2021)

## Abstract (arXiv)

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.