Classroom Utterance Analysis and Visualization Using a Generative Deep Neural Networks for Dialogue Model

Sakuei Onishi, Tomohiko Yasumori, Hiromitsu Shiina — Tue, 29 Oct 2024 13:40:14 +0000

In elementary school and other classes at different levels, teachers have less time for reflection, which can involve self-assessment and classroom observation. Never-theless, reflection activities are becoming increasingly important. Unfortunately, few studies support teachers ’reflection, and studies that analyze classroom utterances treat them as text and do not consider them as dialogues. However, in the field of dialogue response generation, some dialogue models using neural networks have been proposed. In this study, we propose a dialogue model that considers the domain of the class and extracts similar utterances. The proposed model considers the domain of ele-mentary school classes, a domain in which speakers can be classified, and incorporates a method to abstract the characteristics of speakers by clustering. The proposed model can be constructed with a relatively small number of parameters. We also developed a system to visualize the classification probabilities analyzed using the proposed dia-logue model. The developed visualization system was evaluated by experts and was found to visually recognize the classification bias of an utterance and to confirm the quality of the utterance by the similarity of the utterances.

International Journal of Smart Computing and Artificial Intelligence

Classroom Utterance Analysis and Visualization Using a Generative Deep Neural Networks for Dialogue Model