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Generative Adversarial Networks for Imputing Sparse Learning Performance

Liang Zhang, Mohammed Yeasin, Jionghao Lin, Felix Havugimana, Xiangen Hu

TL;DR

It is demonstrated 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, which enhances comprehensive learning data modeling and analytics in AI-based education.

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.

Generative Adversarial Networks for Imputing Sparse Learning Performance

TL;DR

It is demonstrated 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, which enhances comprehensive learning data modeling and analytics in AI-based education.

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.
Paper Structure (14 sections, 1 equation, 5 figures, 1 table)

This paper contains 14 sections, 1 equation, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Addressing Data Sparsity in ITSs
  4. Generative AI Models for Educational Data Imputation
  5. Methods
  6. Dataset
  7. The 3-D Tensor Representation of Sparse Learning Performance
  8. The Proposed GAIN-based Imputation Architecture
  9. Baselines
  10. Experimental Setup and Evaluation
  11. Results and Discussion
  12. Limitations and Future Works
  13. Conclusion
  14. Acknowledgements

Figures (5)

  • Figure 1: The proposed GAIN-based imputation architecture for sparse learning performance yoon2018gain.
  • Figure 2: Data sparsity levels
  • Figure 3: RMSE performance in different models for all lessons dataset
  • Figure 4: Data sparsity levels
  • Figure 5: Spearman correlation of RMSE with varying attempts.