MOOCCube: A Large-scale Data Repository for NLP Applications in MOOCs

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 3135–3142 July 5 - 10, 2020. c⃝2020 Association for Computational Linguistics 3135 MOOCCube: A Large-scale Data Repository for NLP Applications in MOOCs Jifan Yu1∗, Gan Luo1∗, Tong Xiao1, Qingyang Zhong1, Yuquan Wang1, Wenzheng Feng1, Junyi Luo1, Chenyu Wang1, Lei Hou1,2,3, Juanzi Li1,2,3†, Zhiyuan Liu1,2,3, Jie Tang1,2,3 1Dept. of Computer SCi.& Tech., Tsinghua University, China 100084 2KIRC, Institute for Artificial Intelligence, Tsinghua University, China 100084 3Beijing National Research Center for Information Science and Technology, China 100084 {yujf18,luog18,zhongqy16,wangyuqu19,fwz17}@mails.tsinghua.edu.cn {houlei,lijuanzi,liuzy,jietang}@tsinghua.edu.cn Abstract The prosperity of Massive Open Online Courses (MOOCs) provides fodder for many NLP and AI research for education applica- tions, e.g., course concept extraction, prerequi- site relation discovery, etc. However, the pub- licly 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 be- haviors 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. 1 Introduction Massive open online courses (MOOCs) boom swiftly in recent years and have provided conve- nient education for over 100 million users world- wide (Shah, 2019). As a multi-media, large-scale online interactive system, MOOC is an excellent platform for advanced application research (Vol- ery and Lord, 2000). Since MOOC is committed to helping students learn implicit knowledge con- cepts from diverse courses, many efforts from NLP and AI raise topics to build novel applications for assistance. From extracting course concepts and their prerequisite relations (Pan et al., 2017b; Roy et al., 2019; Li et al., 2019) to analyzing student behaviors (Zhang et al., 2019; Feng et al., 2019), MOOC-related topics, tasks, and methods snowball in recent years. Despite the plentiful research interests, the re- source from real MOOCs is still impoverished. ∗Equal Contribution. †Corresponding author. Most of the publicly available datasets are de- signed for a specific task or method, e.g., Zhang et al.(2019) build a MOOC enrollment dataset for course recommendation and (Yu et al., 2019) is only for course concept expansion, which merely contains a subset of MOOC elements. Conse- quently, they are not feasible enough to support ideas that demand more types of information. Moreover, these datasets only contain a small size of specific entities or relation instances, e.g., pre- requisite relation of TutorialBank (Fabbri et al., 2018) only has 794 cases, making it insufficient for advanced models (such as graph neural networks). Therefore, we present MOOCCube, a data repos- itory that integrates courses, concepts, student behaviors, relationships, and external resources. Compared with existing education-related datasets, MOOCCube maintains the following advantages: • Large-scale: MOOCCube contains over 700 MOOC courses, 38k videos, 200k students, and 100k concepts with 300k relation instances, which provide sufficient resources for models that require large-scale data. • High-coverage: Obtained from real …

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