DGEKT: A Dual Graph Ensemble Learning Method for Knowledge Tracing

IF 5.4 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Chaoran Cui, Yumo Yao, Chunyun Zhang, Hebo Ma, Yuling Ma, Zhaochun Ren, Chen Zhang, James Ko
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引用次数: 0

Abstract

Knowledge tracing aims to trace students’ evolving knowledge states by predicting their future performance on concept-related exercises. Recently, some graph-based models have been developed to incorporate the relationships between exercises to improve knowledge tracing, but only a single type of relationship information is generally explored. In this paper, we present a novel Dual Graph Ensemble learning method for Knowledge Tracing (DGEKT), which establishes a dual graph structure of students’ learning interactions to capture the heterogeneous exercise-concept associations and interaction transitions by hypergraph modeling and directed graph modeling, respectively. To combine the dual graph models, we introduce the technique of online knowledge distillation. This choice arises from the observation that, while the knowledge tracing model is designed to predict students’ responses to the exercises related to different concepts, it is optimized merely with respect to the prediction accuracy on a single exercise at each step. With online knowledge distillation, the dual graph models are adaptively combined to form a stronger ensemble teacher model, which provides its predictions on all exercises as extra supervision for better modeling ability. In the experiments, we compare DGEKT against eight knowledge tracing baselines on three benchmark datasets, and the results demonstrate that DGEKT achieves state-of-the-art performance.

DGEKT:知识追踪的双图集合学习法
知识追踪的目的是通过预测学生未来在与概念相关的练习中的表现来追踪他们不断变化的知识状态。最近,一些基于图的模型被开发出来,以结合练习之间的关系来改进知识追踪,但一般只探讨单一类型的关系信息。本文提出了一种新颖的知识追踪双图集合学习方法(DGEKT),通过超图建模和有向图建模,分别建立学生学习互动的双图结构,以捕捉异质的练习-概念关联和互动转换。为了结合双图模型,我们引入了在线知识提炼技术。我们之所以选择这种方法,是因为我们发现,虽然知识追踪模型旨在预测学生对不同概念相关练习的反应,但它仅仅是针对每一步单个练习的预测准确性进行了优化。通过在线知识提炼,双图模型被自适应地组合在一起,形成一个更强的集合教师模型,它对所有练习的预测作为额外的监督,以获得更好的建模能力。在实验中,我们将 DGEKT 与三个基准数据集上的八个知识追踪基线进行了比较,结果表明 DGEKT 达到了最先进的性能。
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来源期刊
ACM Transactions on Information Systems
ACM Transactions on Information Systems 工程技术-计算机:信息系统
CiteScore
9.40
自引率
14.30%
发文量
165
审稿时长
>12 weeks
期刊介绍: The ACM Transactions on Information Systems (TOIS) publishes papers on information retrieval (such as search engines, recommender systems) that contain: new principled information retrieval models or algorithms with sound empirical validation; observational, experimental and/or theoretical studies yielding new insights into information retrieval or information seeking; accounts of applications of existing information retrieval techniques that shed light on the strengths and weaknesses of the techniques; formalization of new information retrieval or information seeking tasks and of methods for evaluating the performance on those tasks; development of content (text, image, speech, video, etc) analysis methods to support information retrieval and information seeking; development of computational models of user information preferences and interaction behaviors; creation and analysis of evaluation methodologies for information retrieval and information seeking; or surveys of existing work that propose a significant synthesis. The information retrieval scope of ACM Transactions on Information Systems (TOIS) appeals to industry practitioners for its wealth of creative ideas, and to academic researchers for its descriptions of their colleagues'' work.
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