Student modeling, the task of inferring a student's learning characteristics through their interactions with coursework, is a fundamental issue in intelligent education. Although the recent attempts from knowledge tracing and cognitive diagnosis propose several promising directions for improving the usability and effectiveness of current models, the existing public datasets are still insufficient to meet the need for these potential solutions due to their ignorance of complete exercising contexts, fine-grained concepts, and cognitive labels. In this paper, we present MoocRadar, a fine-grained, multi-aspect knowledge repository consisting of 2,513 exercise questions, 5,600 knowledge concepts, and over 12 million behavioral records. Specifically, we propose a framework to guarantee a high-quality and comprehensive annotation of fine-grained concepts and cognitive labels. The statistical and experimental results indicate that our dataset provides the basis for the future improvements of existing methods. Moreover, to support the convenient usage for researchers, we release a set of tools for data querying, model adaption, and even the extension of our repository, which are now available at https://github.com/THU-KEG/MOOC-Radar.

本文介绍MoocRadar，一个由2513道练习题，5600个知识点和超过1200万行为记录组成的细粒度、多方面知识库，包括高质量和全面注释的细粒度概念和认知标签的框架，结果表明这种数据集为现有方法的未来改进提供了基础，并发布了一组可供研究人员使用的数据查询、模型适应和扩展工具。

MoocRadar：用于MOOC中的细粒度和多方面知识库，以提高认知学习者建模