@inproceedings{jia-etal-2021-heterogeneous,
title = "Heterogeneous Graph Neural Networks for Concept Prerequisite Relation Learning in Educational Data",
author = "Jia, Chenghao and
Shen, Yongliang and
Tang, Yechun and
Sun, Lu and
Lu, Weiming",
editor = "Toutanova, Kristina and
Rumshisky, Anna and
Zettlemoyer, Luke and
Hakkani-Tur, Dilek and
Beltagy, Iz and
Bethard, Steven and
Cotterell, Ryan and
Chakraborty, Tanmoy and
Zhou, Yichao",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.164",
doi = "10.18653/v1/2021.naacl-main.164",
pages = "2036--2047",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Heterogeneous Graph Neural Networks for Concept Prerequisite Relation Learning in Educational Data
%A Jia, Chenghao
%A Shen, Yongliang
%A Tang, Yechun
%A Sun, Lu
%A Lu, Weiming
%Y Toutanova, Kristina
%Y Rumshisky, Anna
%Y Zettlemoyer, Luke
%Y Hakkani-Tur, Dilek
%Y Beltagy, Iz
%Y Bethard, Steven
%Y Cotterell, Ryan
%Y Chakraborty, Tanmoy
%Y Zhou, Yichao
%S Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F jia-etal-2021-heterogeneous
%X 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.
%R 10.18653/v1/2021.naacl-main.164
%U https://aclanthology.org/2021.naacl-main.164
%U https://doi.org/10.18653/v1/2021.naacl-main.164
%P 2036-2047
Markdown (Informal)
[Heterogeneous Graph Neural Networks for Concept Prerequisite Relation Learning in Educational Data](https://aclanthology.org/2021.naacl-main.164) (Jia et al., NAACL 2021)
ACL