Concept Extraction and Prerequisite Relation Learning from Educational Data


  • Weiming Lu Zhejiang University
  • Yangfan Zhou Zhejiang University
  • Jiale Yu Zhejiang University
  • Chenhao Jia Zhejiang University



Prerequisite relations among concepts are crucial for educational applications. However, it is difficult to automatically extract domain-specific concepts and learn the prerequisite relations among them without labeled data.

In this paper, we first extract high-quality phrases from a set of educational data, and identify the domain-specific concepts by a graph based ranking method. Then, we propose an iterative prerequisite relation learning framework, called iPRL, which combines a learning based model and recovery based model to leverage both concept pair features and dependencies among learning materials. In experiments, we evaluated our approach on two real-world datasets Textbook Dataset and MOOC Dataset, and validated that our approach can achieve better performance than existing methods. Finally, we also illustrate some examples of our approach.




How to Cite

Lu, W., Zhou, Y., Yu, J., & Jia, C. (2019). Concept Extraction and Prerequisite Relation Learning from Educational Data. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 9678-9685.



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