Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

3-2013

Abstract

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.

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of 14th International Conference on Intelligent Text Processing and Computational Linguistics (CICLing 2013)

City or Country

Samos, Greece

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