面向智能教育的深度学习知识追踪研究进展

doi:10.19678/j.issn.1000-3428.0069653

计算机工程 ›› 2026, Vol. 52 ›› Issue (4): 39-61. doi: 10.19678/j.issn.1000-3428.0069653

面向智能教育的深度学习知识追踪研究进展

崔少国*(), 许松, 王名洋, 周粤

重庆师范大学计算机与信息科学学院, 重庆 401331

收稿日期:2024-03-25 修回日期:2024-09-29 出版日期:2026-04-15 发布日期:2026-04-08
通讯作者: 崔少国
作者简介:
崔少国(CCF高级会员)，男，教授、博士，主研方向为智慧教育、医疗图像、多模态谣言检测
许松，硕士研究生
王名洋，硕士研究生
周粤，硕士研究生
基金资助:
教育部人文社科规划基金(22YJA870005); 重庆市教委人文社科项目(23SKGH072); 重庆市社会科学规划项目(2022NDYB119); 重庆市自然科学基金面上项目(CSTB2022NSCQ-MSX1206); 重庆市技术预见与制度创新项目(CSTB2022TFII-OFX0042); 重庆市教委重点项目(KJZD-K202200510); 重庆师范大学研究生科研创新项目(YKC23022); 重庆师范大学研究生科研创新项目(YZH23006); 重庆师范大学人才基金(20XLB004)

Research Progress on Deep Learning Knowledge Tracing for Intelligent Education

CUI Shaoguo*(), XU Song, WANG Mingyang, ZHOU Yue

College of Computer and Information Science, Chongqing Normal University, Chongqing 401331, China

Received:2024-03-25 Revised:2024-09-29 Online:2026-04-15 Published:2026-04-08
Contact: CUI Shaoguo

摘要/Abstract

摘要：

随着教育数字化的不断推进, 智能教育得到了快速发展。知识追踪作为智能教育领域的核心研究任务之一, 旨在根据学生的历史学习数据捕获其对知识概念的掌握情况, 从而提供个性化的学习路径和资源, 实现人工智能(AI)辅助教育的目标。传统的知识追踪方法主要依赖于贝叶斯模型和逻辑模型, 尽管具有良好的科学解释性, 但在处理海量的教育数据时性能受限。深度学习技术凭借出色的特征提取能力和优异的性能优势, 尤其适用于从海量数据中捕获学习者的知识状态, 因此本文对智能教育领域的深度学习知识追踪研究进行了全面综述。首先, 介绍了智能教育场景中知识追踪的相关概念、研究背景和发展现状。接着, 分析近年来基于深度学习的知识追踪方法, 将其分为基于循环神经网络(RNN)、自注意力网络、记忆增强神经网络和图神经网络(GNN)4类, 并从学习者和习题特征的角度对这4类经典和主流方法的基本思路和算法流程进行归类整理。然后, 介绍了当前可供研究者使用的公开教育数据集, 并比较了不同方法在这些数据集上的性能。最后, 对面向智能教育的深度学习知识追踪做出了总结并探讨了该领域未来可能的研究方向。

关键词: 智能教育, 深度学习, 教育数字化, 知识追踪, 图神经网络

Abstract:

With the continuous advancement of education digitalization, intelligent education has developed rapidly. As a core research task in the field of intelligent education, Knowledge Tracing (KT) aims to capture students' mastery of knowledge concepts based on their historical learning data to provide personalized learning paths and resources to meet the objectives of Artificial Intelligence (AI)-assisted education. Traditional KT methods primarily rely mainly on Bayesian and logic models, which have good scientific explanatory properties but exhibit limited performance when processing massive amounts of educational data. Because of its excellent feature extraction ability and performance, deep learning technology is more suitable than traditional KT methods for capturing learners' knowledge status from massive data. Therefore, a comprehensive review of research on deep learning-based KT in the field of intelligent education is conducted. First, the relevant concepts, research backgrounds, and current development status of KT in intelligent education scenarios are introduced. The KT methods based on deep learning in recent years are then analyzed and divided into four categories: Recurrent Neural Network (RNN), self-attention network, memory-enhancing neural network, and Graph Neural Network (GNN). The basic ideas and algorithm processes of these four classical and mainstream methods are systematically classified and sorted in terms of learner and exercise characteristics. Subsequently, the public education datasets currently available to researchers are introduced, and the performance of different methods on these datasets is compared. Finally, this paper summarizes deep learning KT in intelligent education and discusses possible future research directions in this field.

Key words: intelligent education, deep learning, digitization of education, Knowledge Tracing (KT), Graph Neural Network (GNN)

崔少国, 许松, 王名洋, 周粤. 面向智能教育的深度学习知识追踪研究进展[J]. 计算机工程, 2026, 52(4): 39-61.

CUI Shaoguo, XU Song, WANG Mingyang, ZHOU Yue. Research Progress on Deep Learning Knowledge Tracing for Intelligent Education[J]. Computer Engineering, 2026, 52(4): 39-61.

https://www.ecice06.com/CN/Y2026/V52/I4/39

图/表 11

图1 知识追踪过程

Fig.1 Knowledge tracing process

图2 DKT模型结构

Fig.2 DKT model structure

图3 DKT-DSC模型结构

Fig.3 DKT-DSC model structure

图4 DKVMN模型结构

Fig.4 DKVMN model structure

图5 SAKT模型结构

Fig.5 SAKT model structure

图6 SAINT模型结构

Fig.6 SAINT model structure

图7 LANA-SRFE架构

Fig.7 LANA-SRFE architecture

图8 GKT模型结构

Fig.8 GKT model structure

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