  The increasing adoption of technology to augment or even replace traditional
face-to-face learning has led to the development of a myriad of tools and
platforms aimed at engaging the students and facilitating the teacher's ability
to present new information. The IMapBook project aims at improving the literacy
and reading comprehension skills of elementary school-aged children by
presenting them with interactive e-books and letting them take part in
moderated book discussions. This study aims to develop and illustrate a machine
learning-based approach to message classification that could be used to
automatically notify the discussion moderator of a possible need for an
intervention and also to collect other useful information about the ongoing
discussion. We aim to predict whether a message posted in the discussion is
relevant to the discussed book, whether the message is a statement, a question,
or an answer, and in which broad category it can be classified. We
incrementally enrich our used feature subsets and compare them using standard
classification algorithms as well as the novel Feature stacking method. We use
standard classification performance metrics as well as the Bayesian correlated
t-test to show that the use of described methods in discussion moderation is
feasible. Moving forward, we seek to attain better performance by focusing on
extracting more of the significant information found in the strong temporal
interdependence of the messages.
