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
this https URL

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

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

The goal of multi-label learning (MLL) is to associate a given instance with
its relevant labels from a set of concepts. Previous works of MLL mainly
focused on the setting where the concept set is assumed to be fixed, while many
real-world applications require introducing new concepts into the set to meet
new demands. One common need is to refine the original coarse concepts and
split them into finer-grained ones, where the refinement process typically
begins with limited labeled data for the finer-grained concepts. To address the
need, we formalize the problem into a special weakly supervised MLL problem to
not only learn the fine-grained concepts efficiently but also allow interactive
queries to strategically collect more informative annotations to further
improve the classifier. The key idea within our approach is to learn to assign
pseudo-labels to the unlabeled entries, and in turn leverage the pseudo-labels
to train the underlying classifier and to inform a better query strategy.
Experimental results demonstrate that our pseudo-label approach is able to
accurately recover the missing ground truth, boosting the prediction
performance significantly over the baseline methods and facilitating a
competitive active learning strategy.

本文介绍了一种弱监督的多标签学习方法，可以有效地学习到精细的概念，并通过伪标签来训练分类器和引导主动学习策略。实验结果表明，该方法能够精确地恢复缺失的地面真实值，并显著提高了预测性能。

多标记学习的主动细化：伪标注方法

Active Refinement for Multi-Label Learning: A Pseudo-Label Approach

Conceptual graphs, which is a particular type of Knowledge Graphs, play an
essential role in semantic search. Prior conceptual graph construction
approaches typically extract high-frequent, coarse-grained, and time-invariant
concepts from formal texts. In real applications, however, it is necessary to
extract less-frequent, fine-grained, and time-varying conceptual knowledge and
build taxonomy in an evolving manner. In this paper, we introduce an approach
to implementing and deploying the conceptual graph at Alibaba. Specifically, We
propose a framework called AliCG which is capable of a) extracting fine-grained
concepts by a novel bootstrapping with alignment consensus approach, b) mining
long-tail concepts with a novel low-resource phrase mining approach, c)
updating the graph dynamically via a concept distribution estimation method
based on implicit and explicit user behaviors. We have deployed the framework
at Alibaba UC Browser. Extensive offline evaluation as well as online A/B
testing demonstrate the efficacy of our approach.

本文介绍了一种名为 AliCG 的框架，它可以通过新颖的自举方法来提取细粒度的概念，使用低资源短语挖掘方法挖掘长尾概念，并利用基于用户行为的概念分布估计方法动态更新概念图。在阿里巴巴 UC 浏览器上对该框架进行了离线评估和在线 A/B 测试，证明了我们的方法的有效性。