Using Topic Models to Bridge Coding Schemes of Differing Granularity

InProceedings

Proceedings of Educational Data Mining, 2010

2010 2010

While Intelligent Tutoring Systems (ITSs) are often informed by the data extracted from tutoring corpora, coding schemes can be time consuming to implement. Therefore, an automatic classifier may make for quicker classifications. Dialogue from expert tutoring sessions were analyzed using a topic model to investigate how topics mapped on to pre-existing coding schemes of different granularities. These topics were then used to predict the classification of words into moves and modes. Ultimately, it was found that a decision tree algorithm outperformed several other algorithms in this classification task. Improvements to the classifier are discussed.

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