Inferring Prerequisite Relationships Among Learning Resources for HPC Education

Abstract

For online HPC (High-Performance Computing) education, identifying the prerequisite relationships among different HPC learning resources is important for generating learning paths and personalized course recommendations for students. Previously, such prerequisite relationships were often created by domain experts manually. In this paper, we propose a novel framework to infer both concept and learning resource prerequisite relationships and integrate such a framework into an online HPC education platform. We first construct a bi-directional long short-term (BiLSTM) neural network with attention mechanism to automatically mine the latent semantic features from the formal description of concepts. Then, we input two kinds of features into a fully connected neural network for classification and obtain the relationships among concepts. Considering the asymmetry and directivity of the prerequisite relation, we adopt a network embedding model to learn the vector representations of each concept as the prerequisite and subsequent roles. The concept vectors are weighted and summed to generate the feature vectors of learning resources. For a given pair of learning resources, their feature vectors are input into a classifier that outputs the result of relation classification. Finally, we conduct a series of experiments on two large benchmark datasets. The experimental results show that our method outperforms existing methods with significant improvements.