Qianxi Wu , Weidong Ji , Guohui Zhou , Yingchun Yang
{"title":"Graph knowledge tracing in cognitive situation: Validation of classic assertions in cognitive psychology","authors":"Qianxi Wu , Weidong Ji , Guohui Zhou , Yingchun Yang","doi":"10.1016/j.knosys.2025.113281","DOIUrl":null,"url":null,"abstract":"<div><div>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 <em>cognitive situation</em> 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, <em>Transformer</em>-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.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"315 ","pages":"Article 113281"},"PeriodicalIF":7.2000,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Knowledge-Based Systems","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0950705125003284","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
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.
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.