Course Concept Extraction in MOOC via Explicit/Implicit Representation

Massive Open Online Courses(MOOCs) provide convenient access to knowledge for learners all over the world. Concept Extraction is a basic requirement in MOOCs. However, textual content in MOOCs, such as video subtitles and quizzes, are generally presented as semi-structured or unstructured format. Thus it is hard to extract important concepts with simple methods from MOOCs. In this paper, we design a graph-based propagation method to solve the concept extraction problem. Our method utilize textual and structured data on Wikipedia, to generate implicit and explicit representation for concepts respectively. Experiments show that our method outperforms alternative methods on Chinese dataset(+0.054-0.062 in terms of MAP).