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AI enhanced adaptive tutoring system by generating individualized questions and answers based on cognitive diagnostic assessment

人工智能通过基于认知诊断评估生成个性化问题和答案来增强自适应辅导系统

基本信息

批准号：
20J15339
负责人：
GAN Wenbin
金额：
$ 1.34万
依托单位：
National Institute of Information and Communications Technology
依托单位国家：
日本
项目类别：
Grant-in-Aid for JSPS Fellows
财政年份：
2020
资助国家：
日本
起止时间：
2020-04-24 至 2022-03-31
项目状态：
已结题

项目摘要

This year I continue the work on learner's knowledge assessment (LKA). I have further explored the research of fine-grained assessment and interpretability. Improved on my previous work [BESC’20], I propose a novel model that can not only output the learners’ fine-grained knowledge states but also the item characteristics, enabling the interpretability. Extensive model analyses conducted from six perspectives on five real-world datasets validate its superiority. This work has been published in a top journal [Neurocomputing].Another work solves the fundamental issues of data sparseness and information loss while improving the model performance. It has explored to incorporate the knowledge structure (KS) into the LKA to potentially resolve the above issues. This work automatically generates the KS from the learning logs and proposes a novel graph model with the attention mechanism. Extensive experiments show the effectiveness. This work has been published in a top journal [IJIS].The above work stimulates a new idea of multimodal learning analysis. I have published a review paper about the empirical evidence on the usage of multimodal analysis to provide insights for smarter education. I also participated in a work published in [ICCE’21], in which a graph-based method is proposed for LKA.I also finished my doctoral thesis, in which I summarize my PhD works. Overall, it proposes a general framework for dynamic LKA by integrating both learner and domain modeling. Based on this framework, it proposes three approaches, each addressing one specific issue in existing studies.

今年，我继续学习者知识评估（LKA）的工作。我进一步探讨了细粒度评估和可解释性的研究。改进我以前的工作[BESC'20]，我提出了一个新的模型，不仅可以输出学习者的细粒度的知识状态，而且项目的特征，使可解释性。从六个角度对五个真实世界数据集进行了广泛的模型分析，验证了其优越性。这项工作已经发表在顶级期刊[Neurocomputing]上。另一项工作解决了数据稀疏和信息丢失的基本问题，同时提高了模型性能。它已探索将知识结构（KS）纳入LKA，以潜在地解决上述问题。本文从学习日志中自动生成知识库，并提出了一种新的具有注意力机制的图模型。大量的实验证明了该方法的有效性。本文的研究成果已发表在国际权威期刊《IJIS》上，为多模态学习分析提供了一种新的思路。我发表了一篇关于使用多模态分析的经验证据的评论文章，为更智能的教育提供见解。我还参与了发表在[ICCE'21]上的一项工作，其中提出了一种基于图的LKA方法。我还完成了我的博士论文，其中总结了我的博士工作。总体而言，它提出了一个通用的框架，动态LKA集成学习者和领域建模。基于这一框架，它提出了三种方法，每一种方法解决现有研究中的一个具体问题。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Knowledge structure enhanced graph representation learning model for attentive knowledge tracing

DOI：
10.1002/int.22763
发表时间：
2021-11
期刊：
International Journal of Intelligent Systems
影响因子：
7
作者：
Wenbin Gan;Yuan Sun;Yi Sun
通讯作者：
Wenbin Gan;Yuan Sun;Yi Sun

Modeling learner’s dynamic knowledge construction procedure and cognitive item difficulty for knowledge tracing

DOI：
10.1007/s10489-020-01756-7
发表时间：
2020-07
期刊：
Applied Intelligence
影响因子：
5.3
作者：
Wenbin Gan;Yuan Sun;Xian Peng;Yi Sun
通讯作者：
Wenbin Gan;Yuan Sun;Xian Peng;Yi Sun

Improving Knowledge Tracing through Embedding based on Metapath

DOI：
发表时间：
2021
期刊：
2022 4th International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM)
影响因子：
0
作者：
Chong Jiang;Wenbin Gan;Guiping Su;Yuan Sun;Yi Sun
通讯作者：
Chong Jiang;Wenbin Gan;Guiping Su;Yuan Sun;Yi Sun

Knowledge Interaction Enhanced Knowledge Tracing for Learner Performance Prediction

DOI：
10.1109/besc51023.2020.9348285
发表时间：
2020-11
期刊：
2020 7th International Conference on Behavioural and Social Computing (BESC)
影响因子：
0
作者：
Wenbin Gan;Yuan Sun;Yi Sun
通讯作者：
Wenbin Gan;Yuan Sun;Yi Sun

Knowledge interaction enhanced sequential modeling for interpretable learner knowledge diagnosis in intelligent tutoring systems