Learning performance data (e.g., quiz scores and attempts) is significant for understanding learner engagement and knowledge mastery level. However, the learning performance data collected from Intelligent Tutoring Systems (ITSs) often suffers from sparsity, impacting the accuracy of learner modeling and knowledge assessments. To address this, we introduce the 3DG framework (3-Dimensional tensor for Densification and Generation), a novel approach combining tensor factorization with advanced generative models, including Generative Adversarial Network (GAN) and Generative Pre-trained Transformer (GPT), for enhanced data imputation and augmentation. The framework operates by first representing the data as a three-dimensional tensor, capturing dimensions of learners, questions, and attempts. It then densifies the data through tensor factorization and augments it using Generative AI models, tailored to individual learning patterns identified via clustering. Applied to data from an AutoTutor lesson by the Center for the Study of Adult Literacy (CSAL), the 3DG framework effectively generated scalable, personalized simulations of learning performance. Comparative analysis revealed GAN's superior reliability over GPT-4 in this context, underscoring its potential in addressing data sparsity challenges in ITSs and contributing to the advancement of personalized educational technology.
MC-CPO: Mastery-Conditioned Constrained Policy Optimization for Pedagogically Safe Intelligent Tutoring Systems
School of Computing Sciences（计算科学学院）；Computer Engineering, University of Southern Mississippi, Hattiesburg, MS 39406, USA（计算机工程，密西西比大学，哈特斯伯格，MS 39406，USA）
AI in Education needs interpretable machine learning: Lessons from Open Learner Modelling
University of British Columbia（不列颠哥伦比亚大学）；University College London（伦敦大学学院）
底层人工智能表示的可解释性是开放式学习者建模（OLM）的一个关键存在理由-智能教学系统（ITS）研究的一个分支。OLM提供了“开放”学习者认知和情感的AI模型的工具，以支持人类的学习和教学。ITS（也称为AI in Education）三十多年的研究产生了重要的工作，它告诉我们如何将AI用于教育以达到最佳效果，以及通过OLM研究，使其可解释和可解释以利于学习的必要考虑因素是什么。我们认为，这项工作可以为可解释的人工智能框架提供一个有价值的起点，因此与基于知识和机器学习系统在教育以外的其他高风险环境中的应用有关。
Generative Data Imputation for Sparse Learner Performance Data Using Generative Adversarial Imputation Networks
Institute for Intelligent Systems, University of Memphis（孟菲斯大学智能系统研究所）；Faculty of Education, The University of Hong Kong（香港大学教育学院）；Human-Computer Interaction Institute, Carnegie Mellon University（卡内基梅隆大学人机交互研究所）；Centre for Learning Analytics, Faculty of Information Technology, Monash University（莫纳什大学信息技术学院学习分析中心）；Educational Testing Service（教育考试服务中心）；Department of Psychology and Counseling, Arkansas State University（阿肯色州立大学心理学与咨询系）；Department of Applied Social Sciences, Hong Kong Polytechnic University（香港理工大学应用社会科学系）
Data Augmentation for Sparse Multidimensional Learning Performance Data Using Generative AI
Institute for Intelligent Systems, University of Memphis（孟菲斯大学智能系统研究所）；Arkansas State University（阿肯色州立大学）；Human-Computer Interaction Institute, Carnegie Mellon University（卡内基梅隆大学人机交互研究所）；Faculty of Education, The University of Hong Kong（香港大学教育学院）；Centre for Learning Analytics, Faculty of Information Technology, Monash University（莫纳什大学信息技术学院学习分析中心）；Department of Applied Social Sciences, Hong Kong Polytechnic University（香港理工大学应用社会科学系）
Generative Adversarial Networks for Imputing Sparse Learning Performance
Institute for Intelligent Systems, University of Memphis（孟菲斯大学智能系统研究所）；Department of Electrical and Computer Engineering, University of Memphis（孟菲斯大学电气与计算机工程系）；Human-Computer Interaction Institute, Carnegie Mellon University（卡内基梅隆大学人机交互研究所）；Centre for Learning Analytics, Monash University（莫纳什大学学习分析中心）；Department of Applied Social Sciences, Hong Kong Polytechnic University（香港理工大学应用社会科学系）
AlignKT: Explicitly Modeling Knowledge State for Knowledge Tracing with Ideal State Alignment
School of Computer Science South China Normal University Guangzhou, China（计算机科学学院 华南师范大学 广州, 中国）；School of Artificial Intelligence South China Normal University Foshan, China（人工智能学院 华南师范大学 广州, 中国）
