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The increasing adoption of technology to augment or even replace traditionalface-to-face learning has led to the development of a myriad of tools andplatforms aimed at engaging the students and facilitating the teacher's abilityto present new information. The IMapBook project aims at improving the literacyand reading comprehension skills of elementary school-aged children bypresenting them with interactive e-books and letting them take part inmoderated book discussions. This study aims to develop and illustrate a machinelearning-based approach to message classification that could be used toautomatically notify the discussion moderator of a possible need for anintervention and also to collect other useful information about the ongoingdiscussion. We aim to predict whether a message posted in the discussion isrelevant to the discussed book, whether the message is a statement, a question,or an answer, and in which broad category it can be classified. Weincrementally enrich our used feature subsets and compare them using standardclassification algorithms as well as the novel Feature stacking method. We usestandard classification performance metrics as well as the Bayesian correlatedt-test to show that the use of described methods in discussion moderation isfeasible. Moving forward, we seek to attain better performance by focusing onextracting more of the significant information found in the strong temporalinterdependence of the messages.

Using Machine Learning and Natural Language Processing Techniques to Analyze and Support Moderation of Student Book Discussions