Heterogeneous Graph Neural Networks for Concept Prerequisite Relation Learning in Educational Data
Chenghao Jia, Yongliang Shen, Yechun Tang, Lu Sun, Weiming Lu
Abstract
Prerequisite relations among concepts are crucial for educational applications, such as curriculum planning and intelligent tutoring. In this paper, we propose a novel concept prerequisite relation learning approach, named CPRL, which combines both concept representation learned from a heterogeneous graph and concept pairwise features. Furthermore, we extend CPRL under weakly supervised settings to make our method more practical, including learning prerequisite relations from learning object dependencies and generating training data with data programming. Our experiments on four datasets show that the proposed approach achieves the state-of-the-art results comparing with existing methods.- Anthology ID:
- 2021.naacl-main.164
- Volume:
- Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Month:
- June
- Year:
- 2021
- Address:
- Online
- Editors:
- Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, Yichao Zhou
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2036–2047
- Language:
- URL:
- https://aclanthology.org/2021.naacl-main.164
- DOI:
- 10.18653/v1/2021.naacl-main.164
- Cite (ACL):
- Chenghao Jia, Yongliang Shen, Yechun Tang, Lu Sun, and Weiming Lu. 2021. Heterogeneous Graph Neural Networks for Concept Prerequisite Relation Learning in Educational Data. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2036–2047, Online. Association for Computational Linguistics.
- Cite (Informal):
- Heterogeneous Graph Neural Networks for Concept Prerequisite Relation Learning in Educational Data (Jia et al., NAACL 2021)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/2021.naacl-main.164.pdf
- Data
- LectureBank