3DG: A Framework for Using Generative AI for Handling Sparse Learner Performance Data From Intelligent Tutoring Systems

Understand

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.