Generative Adversarial Networks for Imputing Sparse Learning Performance

TL;DR

This paper introduces a GAIN-based method with CNN enhancements to accurately impute sparse learning performance data in ITSs, improving assessment and personalization by reconstructing missing responses across learners, questions, and attempts.

Contribution

The paper presents a novel GAIN framework tailored for 3D tensor data in ITSs, incorporating CNNs and a least squares loss for improved data imputation accuracy.

Findings

GAIN outperforms tensor factorization methods in imputation accuracy

CNN enhancements improve the quality of data reconstruction

Method demonstrates robustness across six diverse ITS datasets

Abstract

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.