Learning performance data, such as correct or incorrect responses to questions in Intelligent Tutoring Systems (ITSs) is crucial for tracking and assessing the learners' progress and mastery of knowledge. However, the issue of data sparsity, characterized by unexplored questions and missing attempts, hampers accurate assessment and the provision of tailored, personalized instruction within ITSs. This paper proposes using the Generative Adversarial Imputation Networks (GAIN) framework to impute sparse learning performance data, reconstructed into a three-dimensional (3D) tensor representation across the dimensions of learners, questions and attempts. Our customized GAIN-based method computational process imputes sparse data in a 3D tensor space, significantly enhanced by convolutional neural networks for its input and output layers. This adaptation also includes the use of a least squares loss function for optimization and aligns the shapes of the input and output with the dimensions of the questions-attempts matrices along the learners' dimension. Through extensive experiments on six datasets from various ITSs, including AutoTutor, ASSISTments and MATHia, we demonstrate that the GAIN approach generally outperforms existing methods such as tensor factorization and other generative adversarial network (GAN) based approaches in terms of imputation accuracy. This finding enhances comprehensive learning data modeling and analytics in AI-based education.

翻译：学习性能数据，如智能导学系统（ITSs）中学习者对问题的正确或错误回答，对于追踪和评估学习者的知识掌握进度至关重要。然而，数据稀疏性问题——表现为未探索的问题和缺失的作答尝试——阻碍了ITSs中准确评估和个性化教学的实施。本文提出使用生成对抗填补网络（GAIN）框架来填补稀疏的学习性能数据，这些数据被重构为跨学习者、问题和作答尝试三个维度的三维（3D）张量表示。我们定制的基于GAIN的计算方法在3D张量空间中填补稀疏数据，其输入和输出层通过卷积神经网络得到显著增强。该改进还包括使用最小二乘损失函数进行优化，并使输入和输出的形状沿学习者维度与问题-作答尝试矩阵的维度对齐。通过在多个ITSs（包括AutoTutor、ASSISTments和MATHia）的六个数据集上进行广泛实验，我们证明GAIN方法在填补准确性方面通常优于现有方法，如张量分解和其他基于生成对抗网络（GAN）的方法。这一发现增强了人工智能教育中综合学习数据建模与分析的能力。