Identification and classification of the most important moments in students' collaborative chats

Abstract

In this paper, we present an application for the automatic identification of the important moments that might occur during students' collaborative chats. The moments are detected based on the input received from the user, who may choose to perform an analysis on the topics that interest him/her. Moreover, the application offers various types of suggestive and intuitive graphics that aid the user in identification of such moments. There are two main aspects that are considered when identifying important moments: the concepts' frequency and distribution throughout the conversation and the chat tempo, which is analyzed for identifying intensively debated concepts. By the tempo of the chat we understand the rate at which the ideas are input by the chat participants, expressed by the utterances' timestamps.