MOOCon: A Framework for Semi-supervised Concept Extraction from MOOC Content

Recent years have witnessed the rapid development of Massive Open Online Courses (MOOCs). MOOC platforms not only offer a one-stop learning setting, but also aggregate a large number of courses with various kinds of textual content, e.g. video subtitles, quizzes and forum content. MOOCs are also regarded as a large-scale ‘knowledge base’ which covers various domains. However, all the contents generated by instructors and learners are unstructured. In order to process the data to be structured for further knowledge management and mining, the first step could be concept extraction. In this paper, we expect to utilize human knowledge through labeling data, and propose a framework for concept extraction based on machine learning methods. The framework is flexible to support semi-supervised learning, in order to alleviate human effort of labeling training data. Also course-agnostic features are designed for modeling cross-domain data. Experimental results demonstrate that only 10% labeled data can lead to acceptable performance, and the semi-supervised learning method is comparable to the supervised version under the consistent framework. We find the textual contents of various forms, i.e. subtitles, PPTs and questions, should be separately processed due to their formal difference. At last we evaluate a new task: identifying needs of concept comprehension. Our framework can work well in doing identification on forum content while learning a model from subtitles.