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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2021.naacl-main.164.pdf
Video:
 https://aclanthology.org/2021.naacl-main.164.mp4
Data
LectureBank