具有外部知识和互动游戏的MOOC课程概念扩展 (Course Concept Expansion in MOOCs with External Knowledge and Interactive Game)

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.

翻译：随着大规模开放在线课程(MOOCs)越来越受欢迎,它有望自动为MOOC用户提供课外知识。由于语义漂移和缺乏知识指导,现有方法无法在复杂的MOOC环境中有效地扩展课程概念。在本文中,我们首先在通过外部知识库寻找新概念时建立新的界限,然后利用多种特征来核实高质量的结果。此外,为了将人类的努力纳入我们的模型,我们还设计了一个基于游戏的互动式优化机制。我们对Couralesra和XueltangX的四套数据集的实验表明,拟议方法比现有方法(MAP+0.19)取得了显著改进。源代码和数据集已经公布。