# Data Augmentation for Sparse Multidimensional Learning Performance Data Using Generative AI

arXiv: https://arxiv.org/abs/2409.15631 · HTML: https://mlsanity.com/paper/2409.15631/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 129 references that are in the map.
- First posted on arXiv 24 Sep 2024.

## Builds on (most cited first, 29 of 29)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Large Language Models](https://mlsanity.com/paper/2307.05782/) (2023)
- [GAIN: Missing Data Imputation using Generative Adversarial Nets](https://mlsanity.com/paper/1806.02920/) (2018)
- [TuckER: Tensor Factorization for Knowledge Graph Completion](https://mlsanity.com/paper/1901.09590/) (2019)
- [Data Augmentation Generative Adversarial Networks](https://mlsanity.com/paper/1711.04340/) (2017)
- [A Self-Attentive model for Knowledge Tracing](https://mlsanity.com/paper/1907.06837/) (2019)
- [Synthetic Data Augmentation using GAN for Improved Liver Lesion Classification](https://mlsanity.com/paper/1801.02385/) (2018)
- [BAGAN: Data Augmentation with Balancing GAN](https://mlsanity.com/paper/1803.09655/) (2018)
- [ChatGPT is not all you need. A State of the Art Review of large Generative AI models](https://mlsanity.com/paper/2301.04655/) (2023)
- [Knowledge Tracing: A Survey](https://mlsanity.com/paper/2201.06953/) (2022)
- [Model-Free Renewable Scenario Generation Using Generative Adversarial Networks](https://mlsanity.com/paper/1707.09676/) (2017)
- [Tensor Completion Algorithms in Big Data Analytics](https://mlsanity.com/paper/1711.10105/) (2017)
- [Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range](https://mlsanity.com/paper/1407.8038/) (2014)
- [Simultaneous Missing Value Imputation and Structure Learning with Groups](https://mlsanity.com/paper/2110.08223/) (2021)
- [Tag-Aware Ordinal Sparse Factor Analysis for Learning and Content Analytics](https://mlsanity.com/paper/1412.5967/) (2014)
- [Logistic Knowledge Tracing: A Constrained Framework for Learner Modeling](https://mlsanity.com/paper/2005.00869/) (2020)
- [A Prescriptive Learning Analytics Framework: Beyond Predictive Modelling and onto Explainable AI with Prescriptive Analytics and ChatGPT](https://mlsanity.com/paper/2208.14582/) (2022)
- [3DG: A Framework for Using Generative AI for Handling Sparse Learner Performance Data From Intelligent Tutoring Systems](https://mlsanity.com/paper/2402.01746/) (2024)
- [SPL: A Socratic Playground for Learning Powered by Large Language Model](https://mlsanity.com/paper/2406.13919/) (2024)
- [Predicting Learning Performance with Large Language Models: A Study in Adult Literacy](https://mlsanity.com/paper/2403.14668/) (2024)
- [Modeling Knowledge Acquisition from Multiple Learning Resource Types](https://mlsanity.com/paper/2006.13390/) (2020)
- [Knowledge Tracing for Complex Problem Solving: Granular Rank-Based Tensor Factorization](https://mlsanity.com/paper/2210.09013/) (2022)
- [Large Language Model-Driven Classroom Flipping: Empowering Student-Centric Peer Questioning with Flipped Interaction](https://mlsanity.com/paper/2311.14708/) (2023)
- [Quantized Matrix Completion for Personalized Learning](https://mlsanity.com/paper/1412.5968/) (2014)
- [Generative Adversarial Networks for Imputing Sparse Learning Performance](https://mlsanity.com/paper/2407.18875/) (2024)
- [An IoT system for a smart campus: Challenges and solutions illustrated over several real-world use cases](https://mlsanity.com/paper/2403.15395/) (2024)

## Built on by (most cited first, 3 of 3)

- [CoTAL: Human-in-the-Loop Prompt Engineering for Generalizable Formative Assessment Scoring and Feedback](https://mlsanity.com/paper/2504.02323/) (2025)
- [Generative Data Imputation for Sparse Learner Performance Data Using Generative Adversarial Imputation Networks](https://mlsanity.com/paper/2503.18982/) (2025)
- [Exploring Communication Strategies for Collaborative LLM Agents in Mathematical Problem-Solving](https://mlsanity.com/paper/2507.17753/) (2025)

## Abstract (arXiv)

Learning performance data describe correct and incorrect answers or problem-solving attempts in adaptive learning, such as in intelligent tutoring systems (ITSs). Learning performance data tend to be highly sparse (80%(~)90% missing observations) in most real-world applications due to adaptive item selection. This data sparsity presents challenges to using learner models to effectively predict future performance explore new hypotheses about learning. This article proposes a systematic framework for augmenting learner data to address data sparsity in learning performance data. First, learning performance is represented as a three-dimensional tensor of learners' questions, answers, and attempts, capturing longitudinal knowledge states during learning. Second, a tensor factorization method is used to impute missing values in sparse tensors of collected learner data, thereby grounding the imputation on knowledge tracing tasks that predict missing performance values based on real observations. Third, a module for generating patterns of learning is used. This study contrasts two forms of generative Artificial Intelligence (AI), including Generative Adversarial Networks (GANs) and Generate Pre-Trained Transformers (GPT) to generate data associated with different clusters of learner data. We tested this approach on an adult literacy dataset from AutoTutor lessons developed for Adult Reading Comprehension (ARC). We found that: (1) tensor factorization improved the performance in tracing and predicting knowledge mastery compared with other knowledge tracing techniques without data augmentation, showing higher relative fidelity for this imputation method, and (2) the GAN-based simulation showed greater overall stability and less statistical bias based on a divergence evaluation with varying simulation sample sizes compared to GPT.