What's in a Cluster? Automatically Detecting Interesting Interactions in Student E-Discussions

Students in classrooms are starting to use visual argumentation tools for e-discussions – a form of debate in which contributions are written into graphical shapes and linked to one another according to whether they, for instance, support or oppose one another. In order to moderate several simultaneous e-discussions effectively, teachers must be alerted regarding events of interest. We focused on the identification of clusters of contributions representing interaction patterns that are of pedagogical interest (e.g., a student clarifies his or her opinion and then gets feedback from other students). We designed an algorithm that takes an example cluster as input and uses inexact graph matching, text analysis, and machine learning classifiers to search for similar patterns in a given corpus. The method was evaluated on an annotated dataset of real e-discussions and was able to detect almost 80% of the annotated clusters while providing acceptable precision performance.