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.
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Notes
- 1.
AutoTutor Moodel Website: https://sites.autotutor.org/; Adult Literacy and Adult Education Website: https://adulted.autotutor.org/.
- 2.
ASSISTments Website: https://new.assistments.org/.
- 3.
MATHia Website: https://www.carnegielearning.com/solutions/math/mathia/.
- 4.
Assistments 2008–2009: https://pslcdatashop.web.cmu.edu/DatasetInfo?datasetId=388.
- 5.
- 6.
MATHia 2019–2020: https://pslcdatashop.web.cmu.edu/Project?id=720.
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Acknowledgements
This paper is grateful to Prof. Philip I. Pavlik Jr. from the University of Memphis and Prof. Shaghayegh Sahebi from the University at Albany - SUNY for their invaluable assistance with tensor factorization in the early stages of this research. Additionally, we extend our thanks to Prof. Arthur C. Graesser, also from the University of Memphis, for his insightful communications that significantly enriched our understanding and inspired deeper analytical thinking.
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Zhang, L., Yeasin, M., Lin, J., Havugimana, F., Hu, X. (2025). Generative Adversarial Networks for Imputing Sparse Learning Performance. In: Antonacopoulos, A., Chaudhuri, S., Chellappa, R., Liu, CL., Bhattacharya, S., Pal, U. (eds) Pattern Recognition. ICPR 2024. Lecture Notes in Computer Science, vol 15306. Springer, Cham. https://doi.org/10.1007/978-3-031-78172-8_25
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