  As Massive Open Online Courses (MOOCs) become increasingly popular, it is
promising to automatically provide extracurricular knowledge for MOOC users.
Suffering from semantic drifts and lack of knowledge guidance, existing methods
can not effectively expand course concepts in complex MOOC environments. In
this paper, we first build a novel boundary during searching for new concepts
via external knowledge base and then utilize heterogeneous features to verify
the high-quality results. In addition, to involve human efforts in our model,
we design an interactive optimization mechanism based on a game. Our
experiments on the four datasets from Coursera and XuetangX show that the
proposed method achieves significant improvements(+0.19 by MAP) over existing
methods. The source code and datasets have been published.
