Graph knowledge tracing in cognitive situation: Validation of classic assertions in cognitive psychology

IF 7.6 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Knowledge-Based Systems Pub Date : 2025-04-22 Epub Date: 2025-03-08 DOI:10.1016/j.knosys.2025.113281
Qianxi Wu , Weidong Ji , Guohui Zhou , Yingchun Yang
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Abstract

Knowledge Tracing (KT) is a fundamental and challenging task in intelligent education, aiming to trace learners’ knowledge states and learning processes, providing better support and guidance for teaching and addressing mental factors. Previous KT tasks have focused on considering learners’ exposure to extrinsic environmental factors while ignoring the influence of intrinsic psychological factors. Moreover, previous methods have adopted a single perspective in modeling learners’ knowledge states, ignoring the diversity of states in the learning process. To address these issues, we define the concept of cognitive situation through the guidance of cognitive psychology theory to help to explain the extrinsic influence and intrinsic cognition of learners within complex learning environments. Moreover, we design a Cognitive Situation-based Graph KT (CSGKT) model to quantify learners’ influences in the cognitive process by modeling schemas capturing intrinsic characteristics and extrinsic factors through Hyper-Graph Neural Networks (HGNN). Second, we utilize a Directed Graph Convolutional Neural Network (DGCNN) to capture the correlation information between knowledge concepts and structure the learner’s cognitive activities and knowledge states, adding a detailed representation of multiple states of the learning process. In addition, we use the Erase-add Gate to filter out the knowledge states that do not match the learner’s current cognitive activities to stabilize the learner’s due cognition. In our experiments, we selected nine baseline models from three mainstream approaches, including sequence-based approaches, Transformer-based approaches, and complex structure-based approaches. The experimental results show that our models outperform these baseline models. At the same time, we also verify two classic assertions in cognitive psychology, namely, the “short-term memory forgetting of knowledge concepts is mainly caused by interference rather than memory trace fading” and the “cognitive imagery and perceptual function play an equivalent role in the cognitive process”, which further support the feasibility of the model.
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认知情境下的图谱知识追踪:认知心理学经典论断的验证
知识追踪(Knowledge Tracing, KT)是智能教育中一项基础性且具有挑战性的任务,旨在追踪学习者的知识状态和学习过程,为教学提供更好的支持和指导,解决心理因素。以往的KT任务侧重于考虑学习者接触外在环境因素,而忽略了内在心理因素的影响。此外,以往的方法在建模学习者的知识状态时采用了单一的视角,忽略了学习过程中状态的多样性。为了解决这些问题,我们在认知心理学理论的指导下定义了认知情境的概念,以帮助解释学习者在复杂学习环境中的外在影响和内在认知。此外,我们设计了一个基于认知情境的图KT (CSGKT)模型,通过超图神经网络(HGNN)建模捕捉内在特征和外在因素的图式,量化学习者在认知过程中的影响。其次,我们利用有向图卷积神经网络(DGCNN)捕获知识概念之间的相关信息,并构建学习者的认知活动和知识状态,添加学习过程的多个状态的详细表示。此外,我们使用了擦除-添加门来过滤掉与学习者当前认知活动不匹配的知识状态,以稳定学习者的应有认知。在我们的实验中,我们从三种主流方法中选择了9个基线模型,包括基于序列的方法、基于转换器的方法和基于复杂结构的方法。实验结果表明,我们的模型优于这些基线模型。同时,我们也验证了认知心理学的两个经典论断,即“知识概念的短期记忆遗忘主要是由干扰而不是记忆痕迹消退引起的”和“认知意象和知觉功能在认知过程中起着同等的作用”,进一步支持了模型的可行性。
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来源期刊
Knowledge-Based Systems
Knowledge-Based Systems 工程技术-计算机:人工智能
CiteScore
14.80
自引率
12.50%
发文量
1245
审稿时长
7.8 months
期刊介绍: Knowledge-Based Systems, an international and interdisciplinary journal in artificial intelligence, publishes original, innovative, and creative research results in the field. It focuses on knowledge-based and other artificial intelligence techniques-based systems. The journal aims to support human prediction and decision-making through data science and computation techniques, provide a balanced coverage of theory and practical study, and encourage the development and implementation of knowledge-based intelligence models, methods, systems, and software tools. Applications in business, government, education, engineering, and healthcare are emphasized.
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