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Student modeling, the task of inferring a student's learning characteristicsthrough their interactions with coursework, is a fundamental issue inintelligent education. Although the recent attempts from knowledge tracing andcognitive diagnosis propose several promising directions for improving theusability and effectiveness of current models, the existing public datasets arestill insufficient to meet the need for these potential solutions due to theirignorance of complete exercising contexts, fine-grained concepts, and cognitivelabels. In this paper, we present MoocRadar, a fine-grained, multi-aspectknowledge repository consisting of 2,513 exercise questions, 5,600 knowledgeconcepts, and over 12 million behavioral records. Specifically, we propose aframework to guarantee a high-quality and comprehensive annotation offine-grained concepts and cognitive labels. The statistical and experimentalresults indicate that our dataset provides the basis for the futureimprovements of existing methods. Moreover, to support the convenient usage forresearchers, we release a set of tools for data querying, model adaption, andeven the extension of our repository, which are now available athttps://github.com/THU-KEG/MOOC-Radar.

MoocRadar: A Fine-grained and Multi-aspect Knowledge Repository for Improving Cognitive Student Modeling in MOOCs