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.
翻译:学生的成绩预测是了解学生需求、提供适当的学习机会/资源和发展教学质量的关键研究问题。然而,传统的机器学习方法未能产生稳定和准确的预测结果。在本论文中,我们提出了一种基于图表的混合机学习方法,目的是通过多种方法的共识来提高单一机器学习方法的稳定性。具体地说,我们利用监督的预测方法和不受监督的集群方法,建立一种互动方法,在双边图表中传播,并接近于更稳定和准确的预测结果。广泛的实验表明我们所建议的方法在预测更准确的学生成绩方面的有效性。具体地说,我们的模型在预测准确性方面比最好的传统机器学习算法高出14.8%。