Attention-Based Character-Word Hybrid Neural Networks With Semantic and Structural Information for Identifying of Urgent Posts in MOOC Discussion Forums

Abstract

The MOOC Discussion Forum is the place where students and teachers communicate, often plagued by information overload and confusion. Posts that students used to express confusion and demanded teachers' attention are most likely to be overwhelmed by the amount of noise in the forum. Therefore, how to pay attention to urgent posts in time has become a critical problem to be solved. In this paper, we present a new hybrid neural network for identifying “urgent” posts that require immediate attention from instructors. We proposed a semantic and structure extraction part including convolutional neural network (CNN) and gated recurrent unit (GRU), which can simultaneously learn the semantic information and structural information of sentences. In addition, Due to a lot of noise such as spelling mistakes and emoticons in the forum comment text, we propose to use Character-level Convolutional Networks (Char-CNN) to capture these special information. Finally, the semantic and structural information learned by the semantic and structural extraction part is merged with the character information learned by Char-CNN, and the attention mechanism to learn their weights, the final representation of the sentence can be obtained. In our experiments, we achieve urgent posts classification with a micro F-score of 91.8%, 91.3% and 88.4% on the Stanford MOOCPosts dataset, outperforming the state-of-the-art approach by 1.8%, 2.4% and 1.5% respectively. This work can help teachers prioritize their responses and better manage numerous posts. Teachers can answer learner questions in a timely manner and help reduce dropout rates and improve completion rates.