Course Concept Expansion in MOOCs with External Knowledge and Interactive Game

TL;DR

This paper introduces a novel method for expanding course concepts in MOOCs by leveraging external knowledge bases and an interactive game mechanism, improving concept quality and relevance.

Contribution

It proposes a new boundary-based concept expansion approach combined with heterogeneous features and an interactive game for human-in-the-loop optimization.

Findings

Achieved significant improvement in MAP (+0.19) over existing methods.

Effectively verified high-quality concepts using heterogeneous features.

Demonstrated robustness across four datasets from Coursera and XuetangX.

Abstract

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.