@inproceedings{pan-etal-2017-prerequisite,
title = "Prerequisite Relation Learning for Concepts in {MOOC}s",
author = "Pan, Liangming and
Li, Chengjiang and
Li, Juanzi and
Tang, Jie",
editor = "Barzilay, Regina and
Kan, Min-Yen",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P17-1133",
doi = "10.18653/v1/P17-1133",
pages = "1447--1456",
abstract = "What prerequisite knowledge should students achieve a level of mastery before moving forward to learn subsequent coursewares? We study the extent to which the prerequisite relation between knowledge concepts in Massive Open Online Courses (MOOCs) can be inferred automatically. In particular, what kinds of information can be leverage to uncover the potential prerequisite relation between knowledge concepts. We first propose a representation learning-based method for learning latent representations of course concepts, and then investigate how different features capture the prerequisite relations between concepts. Our experiments on three datasets form Coursera show that the proposed method achieves significant improvements (+5.9-48.0{\%} by F1-score) comparing with existing methods.",
}
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%0 Conference Proceedings
%T Prerequisite Relation Learning for Concepts in MOOCs
%A Pan, Liangming
%A Li, Chengjiang
%A Li, Juanzi
%A Tang, Jie
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F pan-etal-2017-prerequisite
%X What prerequisite knowledge should students achieve a level of mastery before moving forward to learn subsequent coursewares? We study the extent to which the prerequisite relation between knowledge concepts in Massive Open Online Courses (MOOCs) can be inferred automatically. In particular, what kinds of information can be leverage to uncover the potential prerequisite relation between knowledge concepts. We first propose a representation learning-based method for learning latent representations of course concepts, and then investigate how different features capture the prerequisite relations between concepts. Our experiments on three datasets form Coursera show that the proposed method achieves significant improvements (+5.9-48.0% by F1-score) comparing with existing methods.
%R 10.18653/v1/P17-1133
%U https://aclanthology.org/P17-1133
%U https://doi.org/10.18653/v1/P17-1133
%P 1447-1456
Markdown (Informal)
[Prerequisite Relation Learning for Concepts in MOOCs](https://aclanthology.org/P17-1133) (Pan et al., ACL 2017)
ACL
- Liangming Pan, Chengjiang Li, Juanzi Li, and Jie Tang. 2017. Prerequisite Relation Learning for Concepts in MOOCs. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1447–1456, Vancouver, Canada. Association for Computational Linguistics.