A Self-Training Framework for Automatic Identification of Exploratory Dialogue

摘要

The dramatic increase in online learning materials over the last decade has made it difficult for individuals to locate information they need. Until now, researchers in the field of Learning Analytics have had to rely on the use of manual approaches to identify exploratory dialogue. This type of dialogue is desirable in online learning environments, since training learners to use it has been shown to improve learning outcomes. In this paper, we frame the problem of exploratory dialogue detection as a binary classification task, classifying a given contribution to an online dialogue as exploratory or non-exploratory. We propose a self-training framework to identify exploratory dialogue. This framework combines cue-phrase matching and K-nearest neighbour (KNN) based instance selection, employing both discourse and topical features for classification. To do this, we first built a corpus from transcripts of synchronous online chat recorded at The Open University annual Learning and Technology Conference in June 2010. Experimental results from this corpus show that our proposed framework outperforms several competitive baselines.