3DG: A Framework for Using Generative AI for Handling Sparse Learner
  Performance Data From Intelligent Tutoring Systems
Liang Zhang Jionghao Lin Conrad Borchers Meng Cao Xiangen Hu
Learning performance data (e.g., quiz scores and attempts) is significant for
understanding learner engagement and knowledge mastery level. However, the
learning performance data collected from Intelligent Tutoring Systems (ITSs)
often suffers from sparsity, impacting the accuracy of learner modeling and
knowledge assessments. To address this, we introduce the 3DG framework
(3-Dimensional tensor for Densification and Generation), a novel approach
combining tensor factorization with advanced generative models, including
Generative Adversarial Network (GAN) and Generative Pre-trained Transformer
(GPT), for enhanced data imputation and augmentation. The framework operates by
first representing the data as a three-dimensional tensor, capturing dimensions
of learners, questions, and attempts. It then densifies the data through tensor
factorization and augments it using Generative AI models, tailored to
individual learning patterns identified via clustering. Applied to data from an
AutoTutor lesson by the Center for the Study of Adult Literacy (CSAL), the 3DG
framework effectively generated scalable, personalized simulations of learning
performance. Comparative analysis revealed GAN's superior reliability over
GPT-4 in this context, underscoring its potential in addressing data sparsity
challenges in ITSs and contributing to the advancement of personalized
educational technology.