Course Concept Expansion in MOOCs with External Knowledge and Interactive Game
This work addresses the challenge of providing extracurricular knowledge for MOOC users, which is incremental as it builds on existing methods with novel optimizations.
The paper tackles the problem of automatically expanding course concepts in MOOCs by using external knowledge and interactive game mechanisms to overcome semantic drifts and lack of guidance, achieving a significant improvement of +0.19 MAP over existing methods on datasets from Coursera and XuetangX.
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.