Generative Data Imputation for Sparse Learner Performance Data Using Generative Adversarial Imputation Networks

• URL: http://arxiv.org/abs/2503.18982v2
• Date: Sun, 13 Apr 2025 21:04:27 GMT
• Title: Generative Data Imputation for Sparse Learner Performance Data Using Generative Adversarial Imputation Networks
• Authors: Liang Zhang, Jionghao Lin, John Sabatini, Diego Zapata-Rivera, Carol Forsyth, Yang Jiang, John Hollander, Xiangen Hu, Arthur C. Graesser,
• Abstract summary: Missing responses due to skips or incomplete attempts create data sparsity.<n>We propose a generative imputation approach using Generative Adrial Imputation Networks (GAIN)<n>Our method features a three-dimensional (3D) framework (learners, questions, and attempts), flexibly accommodating various sparsity levels.
• Score: 3.0800525961862992
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Learner performance data collected by Intelligent Tutoring Systems (ITSs), such as responses to questions, is essential for modeling and predicting learners' knowledge states. However, missing responses due to skips or incomplete attempts create data sparsity, challenging accurate assessment and personalized instruction. To address this, we propose a generative imputation approach using Generative Adversarial Imputation Networks (GAIN). Our method features a three-dimensional (3D) framework (learners, questions, and attempts), flexibly accommodating various sparsity levels. Enhanced by convolutional neural networks and optimized with a least squares loss function, the GAIN-based method aligns input and output dimensions to question-attempt matrices along the learners' dimension. Extensive experiments using datasets from AutoTutor Adult Reading Comprehension (ARC), ASSISTments, and MATHia demonstrate that our approach significantly outperforms tensor factorization and alternative GAN methods in imputation accuracy across different attempt scenarios. Bayesian Knowledge Tracing (BKT) further validates the effectiveness of the imputed data by estimating learning parameters: initial knowledge (P(L0)), learning rate (P(T)), guess rate (P(G)), and slip rate (P(S)). Results indicate the imputed data enhances model fit and closely mirrors original distributions, capturing underlying learning behaviors reliably. Kullback-Leibler (KL) divergence assessments confirm minimal divergence, showing the imputed data preserves essential learning characteristics effectively. These findings underscore GAIN's capability as a robust imputation tool in ITSs, alleviating data sparsity and supporting adaptive, individualized instruction, ultimately leading to more precise and responsive learner assessments and improved educational outcomes.

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