LAK13 paper: An Evaluation of Learning Analytics to Identify Exploratory Dialogue in Online Discussions
Rebecca Ferguson did an outstanding job presenting our machine learning paper to a general audience at LAK13, reflecting the mutual learning between educational and computational researchers that had to take place in order to do this work…
Ferguson, R., Wei, Z., He, Y. and Buckingham Shum, S. (2013). An Evaluation of Learning Analytics to Identify Exploratory Dialogue in Online Discussions. Proc. LAK13: 3rd International Conference on Learning Analytics & Knowledge, 8-12 April 2013, Leuven, Belgium, pp. 99-103. (ACM: New York). DOI: http://dx.doi.org/10.1145/2090116.2090130 Open Access Eprint: http://oro.open.ac.uk/36664
Social learning analytics are concerned with the process of knowledge construction as learners build knowledge together in their social and cultural environments. One of the most important tools employed during this process is language. In this paper we take exploratory dialogue, a joint form of co-reasoning, to be an external indicator that learning is taking place. Using techniques developed within the field of computational linguistics, we build on previous work using cue phrases to identify exploratory dialogue within online discussion. Automatic detection of this type of dialogue is framed as a binary classification task that labels each contribution to an online discussion as exploratory or non-exploratory. We describe the development of a self-training framework that employs discourse features and topical features for classification by integrating both cue-phrase matching and k-nearest neighbour classification. Experiments with a corpus constructed from the archive of a two-day online conference show that our proposed framework outperforms other approaches. A classifier developed using the self-training framework is able to make useful distinctions between the learning dialogue taking place at different times within an online conference as well as between the contributions of individual participants.
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