Leveraging Neural Network-Based Model for Context Classification of Classroom Dialogue Text

Abstract

Classroom dialogue is a common strategy for teaching and learning. Technology-assisted classroom dialogue has drawn increasing attentions, where the classification of classroom dialogue is one of active research topics. However, existing studies mainly paid much attention to dialogue manners rather than dialogue contexts. This paper conducts a deep learning-based experiment on the classification of classroom dialogue context in text format. A hybrid neural network-based model namely CNN-BiLSTM-Attention is proposed for context classification of classroom dialogue text. The hybrid model consists of a Convolutional Neural Network (CNN) and a Bidirectional Long Short-Term Memory Network (BiLSTM) by leveraging an attention mechanism. The CNN-BiLSTM-Attention model is able to capture and learn both the local and global features of classroom dialogue texts for learning semantic information of dialogue contexts. To test the effectiveness of the model, an annotated classroom dialogue text dataset is built based on a well-established coding framework through collecting 155 lessons in Chinese language. Compared with eleven baseline methods, including commonly-used machine learning models and deep learning models, the evaluation results demonstrate that the CNN-BiLSTM-Attention model achieves the best performance with an overall F1-score of 0.7006.