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Erschienen in:
2017 | OriginalPaper | Buchkapitel
Topic Modeling for Learner Question and Answer Analytics
verfasst von : Kyungrog Kim, Hye Jin Song, Nammee Moon
Erschienen in: Advanced Multimedia and Ubiquitous Engineering
Verlag: Springer Singapore
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Abstract
There is increasing interest in text analysis based on unstructured data such as articles and comments, questions and answers. This is because they can be used to identify, evaluate, predict, and recommend features from unstructured text data, which is the opinion of people. The same holds true for TEL, where the MOOC service has evolved to automate debating, questioning and answering services based on the teaching-learning support system in order to generate question topics and to automatically classify the topics relevant to new questions based on question and answer data accumulated in the system. To that end, the present study proposes an LDA-based topic modeling. The proposed method enables the generation of a dictionary of question topics and the automatic classification of topics relevant to new questions.