报告题目：MOOCCubeX: A Large Knowledge-centered Repository for Adaptive Learning in MOOCs

论文出处：CIKM ‘21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management .

作者：Jifan Yu^1,2，Yuquan Wang¹，Qingyang Zhong¹，Gan Luo¹，Yiming Mao¹，Kai Sun¹，Wenzheng Feng¹，Wei Xu¹，Shulin Cao¹，Kaisheng Zeng¹，Zijun Yao¹，Lei Hou¹，Yankai Lin²，Peng Li²，Jie Zhou²，Bin Xu¹，Juanzi Li¹，Jie Tang¹，Maosong Sun¹

单位：

¹Department of Computer Science and Technology, Tsinghua University, China.

²Pattern Recognition Center, WeChat AI, Tencent Inc., China.

报告人：李佳丽

报告时间：2022年9月12日 下午 2:00

报告地点：贵州大学北校区博学楼623-2室

报告内容摘要：

The prosperity of massive open online courses provides fodder for plentiful research efforts on adaptive learning. However, current open-access educational datasets are still far from sufficient to meet the need for various topics of adaptive learning. Existing released datasets often cover only small-scale data, lack fine-grained knowledge concepts. They are even difficult to curate and supplement due to platform limitations. In this work, we construct MOOCCubeX, a large, knowledge-centered repository consisting of 4, 216 courses, 230, 263 videos, 358, 265 exercises, 637, 572 fine-grained concepts and over 296 million behavioral data of 3, 330, 294 students, for supporting the research topics on adaptive learning in MOOCs.Licensed by XuetangX, one of the largest MOOC websites in China, we obtain abundant and diverse course resources and student behavioral data and are permitted to make subsequent periodic updates.We propose a framework to accomplish data processing, weakly supervised fine-grained concept graph mining, and data curation to improve usability and richness. Based on the fine-grained concepts, we re-organize the data from the knowledge perspective and acquire more external learning resources from the web. Our repository is now available at

https://github.com/THU-KEG/MOOCCubeX.