Graph convolutional networks for student answers assessment


Graph Convolutional Networks have achieved impressive results in multiple NLP tasks such as text classification. However, this approach has not been explored yet for the student answer assessment task. In this work, we propose to use Graph Convolutional Networks to automatically assess freely generated student answers within the context of dialogue-based intelligent tutoring systems. We convert this task to a node classification task. First, we build a DTGrade graph where each node represents the concatenation of the student answer and its corresponding reference answer whereas the edges represent the relatedness between nodes. Second, the DTGrade graph is fed to two layers of Graph Convolutional Networks. Finally, the output of the second layer is fed to a softmax layer. The empirical results showed that our model reached the state-of-the-art results by obtaining an accuracy of 73%.

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)