Machine Learning
Assessing agreement on classification tasks: the kappa statistic
Computational Linguistics
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Is “exploratory talk” productive talk?
Learning with computers
Dialogue act modeling for automatic tagging and recognition of conversational speech
Computational Linguistics
Learning from labeled features using generalized expectation criteria
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Computers & Education - Methodological issue in researching CSCL
Learning analytics to identify exploratory dialogue within synchronous text chat
Proceedings of the 1st International Conference on Learning Analytics and Knowledge
Social learning analytics: five approaches
Proceedings of the 2nd International Conference on Learning Analytics and Knowledge
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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.