@inproceedings{yu-etal-2020-mooccube,
title = "{MOOCC}ube: A Large-scale Data Repository for {NLP} Applications in {MOOC}s",
author = "Yu, Jifan and
Luo, Gan and
Xiao, Tong and
Zhong, Qingyang and
Wang, Yuquan and
Feng, Wenzheng and
Luo, Junyi and
Wang, Chenyu and
Hou, Lei and
Li, Juanzi and
Liu, Zhiyuan and
Tang, Jie",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.285",
doi = "10.18653/v1/2020.acl-main.285",
pages = "3135--3142",
abstract = "The prosperity of Massive Open Online Courses (MOOCs) provides fodder for many NLP and AI research for education applications, e.g., course concept extraction, prerequisite relation discovery, etc. However, the publicly available datasets of MOOC are limited in size with few types of data, which hinders advanced models and novel attempts in related topics. Therefore, we present MOOCCube, a large-scale data repository of over 700 MOOC courses, 100k concepts, 8 million student behaviors with an external resource. Moreover, we conduct a prerequisite discovery task as an example application to show the potential of MOOCCube in facilitating relevant research. The data repository is now available at \url{http://moocdata.cn/data/MOOCCube}.",
}
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<abstract>The prosperity of Massive Open Online Courses (MOOCs) provides fodder for many NLP and AI research for education applications, e.g., course concept extraction, prerequisite relation discovery, etc. However, the publicly available datasets of MOOC are limited in size with few types of data, which hinders advanced models and novel attempts in related topics. Therefore, we present MOOCCube, a large-scale data repository of over 700 MOOC courses, 100k concepts, 8 million student behaviors with an external resource. Moreover, we conduct a prerequisite discovery task as an example application to show the potential of MOOCCube in facilitating relevant research. The data repository is now available at http://moocdata.cn/data/MOOCCube.</abstract>
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%0 Conference Proceedings
%T MOOCCube: A Large-scale Data Repository for NLP Applications in MOOCs
%A Yu, Jifan
%A Luo, Gan
%A Xiao, Tong
%A Zhong, Qingyang
%A Wang, Yuquan
%A Feng, Wenzheng
%A Luo, Junyi
%A Wang, Chenyu
%A Hou, Lei
%A Li, Juanzi
%A Liu, Zhiyuan
%A Tang, Jie
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F yu-etal-2020-mooccube
%X The prosperity of Massive Open Online Courses (MOOCs) provides fodder for many NLP and AI research for education applications, e.g., course concept extraction, prerequisite relation discovery, etc. However, the publicly available datasets of MOOC are limited in size with few types of data, which hinders advanced models and novel attempts in related topics. Therefore, we present MOOCCube, a large-scale data repository of over 700 MOOC courses, 100k concepts, 8 million student behaviors with an external resource. Moreover, we conduct a prerequisite discovery task as an example application to show the potential of MOOCCube in facilitating relevant research. The data repository is now available at http://moocdata.cn/data/MOOCCube.
%R 10.18653/v1/2020.acl-main.285
%U https://aclanthology.org/2020.acl-main.285
%U https://doi.org/10.18653/v1/2020.acl-main.285
%P 3135-3142
Markdown (Informal)
[MOOCCube: A Large-scale Data Repository for NLP Applications in MOOCs](https://aclanthology.org/2020.acl-main.285) (Yu et al., ACL 2020)
ACL
- Jifan Yu, Gan Luo, Tong Xiao, Qingyang Zhong, Yuquan Wang, Wenzheng Feng, Junyi Luo, Chenyu Wang, Lei Hou, Juanzi Li, Zhiyuan Liu, and Jie Tang. 2020. MOOCCube: A Large-scale Data Repository for NLP Applications in MOOCs. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 3135–3142, Online. Association for Computational Linguistics.