Data Augmentation for Sparse Multidimensional Learning Performance Data Using Generative AI

• URL: http://arxiv.org/abs/2409.15631v1
• Date: Tue, 24 Sep 2024 00:25:07 GMT
• Title: Data Augmentation for Sparse Multidimensional Learning Performance Data Using Generative AI
• Authors: Liang Zhang, Jionghao Lin, John Sabatini, Conrad Borchers, Daniel Weitekamp, Meng Cao, John Hollander, Xiangen Hu, Arthur C. Graesser,
• Abstract summary: Learning performance data describe correct and incorrect answers or problem-solving attempts in adaptive learning. Learning performance data tend to be highly sparse (80%(sim)90% missing observations) in most real-world applications due to adaptive item selection. This article proposes a systematic framework for augmenting learner data to address data sparsity in learning performance data.
• Score: 17.242331892899543
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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\%\(\sim\)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.

Related papers

• Generative Adversarial Networks for Imputing Sparse Learning Performance [3.0350058108125646]
  This paper proposes using the Generative Adversarial Imputation Networks (GAIN) framework to impute sparse learning performance data. Our customized GAIN-based method computational process imputes sparse data in a 3D tensor space. This finding enhances comprehensive learning data modeling and analytics in AI-based education.
  arXiv  Detail & Related papers  (2024-07-26T17:09:48Z)
• Enhancing Deep Knowledge Tracing via Diffusion Models for Personalized Adaptive Learning [1.2248793682283963]
  This study aims to tackle data shortage issues in student learning records to enhance DKT performance for personalized adaptive learning (PAL) It employs TabDDPM, a diffusion model, to generate synthetic educational records to augment training data for enhancing DKT. The experimental results demonstrate that the AI-generated data by TabDDPM significantly improves DKT performance.
  arXiv  Detail & Related papers  (2024-04-25T00:23:20Z)
• 3DG: A Framework for Using Generative AI for Handling Sparse Learner Performance Data From Intelligent Tutoring Systems [22.70004627901319]
  We introduce the 3DG framework (3-Dimensional tensor for Densification and Generation), a novel approach combining tensor factorization with advanced generative models. The framework effectively generated scalable, personalized simulations of learning performance.
  arXiv  Detail & Related papers  (2024-01-29T22:34:01Z)
• Learn to Unlearn for Deep Neural Networks: Minimizing Unlearning Interference with Gradient Projection [56.292071534857946]
  Recent data-privacy laws have sparked interest in machine unlearning. Challenge is to discard information about the forget'' data without altering knowledge about remaining dataset. We adopt a projected-gradient based learning method, named as Projected-Gradient Unlearning (PGU) We provide empirically evidence to demonstrate that our unlearning method can produce models that behave similar to models retrained from scratch across various metrics even when the training dataset is no longer accessible.
  arXiv  Detail & Related papers  (2023-12-07T07:17:24Z)
• Robust Learning with Progressive Data Expansion Against Spurious Correlation [65.83104529677234]
  We study the learning process of a two-layer nonlinear convolutional neural network in the presence of spurious features. Our analysis suggests that imbalanced data groups and easily learnable spurious features can lead to the dominance of spurious features during the learning process. We propose a new training algorithm called PDE that efficiently enhances the model's robustness for a better worst-group performance.
  arXiv  Detail & Related papers  (2023-06-08T05:44:06Z)
• Automatic Data Augmentation via Invariance-Constrained Learning [94.27081585149836]
  Underlying data structures are often exploited to improve the solution of learning tasks. Data augmentation induces these symmetries during training by applying multiple transformations to the input data. This work tackles these issues by automatically adapting the data augmentation while solving the learning task.
  arXiv  Detail & Related papers  (2022-09-29T18:11:01Z)
• Benchmarking Learning Efficiency in Deep Reservoir Computing [23.753943709362794]
  We introduce a benchmark of increasingly difficult tasks together with a data efficiency metric to measure how quickly machine learning models learn from training data. We compare the learning speed of some established sequential supervised models, such as RNNs, LSTMs, or Transformers, with relatively less known alternative models based on reservoir computing.
  arXiv  Detail & Related papers  (2022-09-29T08:16:52Z)
• An Empirical Investigation of Commonsense Self-Supervision with Knowledge Graphs [67.23285413610243]
  Self-supervision based on the information extracted from large knowledge graphs has been shown to improve the generalization of language models. We study the effect of knowledge sampling strategies and sizes that can be used to generate synthetic data for adapting language models.
  arXiv  Detail & Related papers  (2022-05-21T19:49:04Z)
• Training Efficiency and Robustness in Deep Learning [2.6451769337566406]
  We study approaches to improve the training efficiency and robustness of deep learning models. We find that prioritizing learning on more informative training data increases convergence speed and improves generalization performance on test data. We show that a redundancy-aware modification to the sampling of training data improves the training speed and develops an efficient method for detecting the diversity of training signal.
  arXiv  Detail & Related papers  (2021-12-02T17:11:33Z)
• Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach [80.8446673089281]
  We propose a new learning paradigm with graph representation and learning. Our framework contains two modules: 1) a backbone network (e.g., feedforward neural nets) as a lower model takes features as input and outputs predicted labels; 2) a graph neural network as an upper model learns to extrapolate embeddings for new features via message passing over a feature-data graph built from observed data.
  arXiv  Detail & Related papers  (2021-10-09T09:02:45Z)
• Omni-supervised Facial Expression Recognition via Distilled Data [120.11782405714234]
  We propose omni-supervised learning to exploit reliable samples in a large amount of unlabeled data for network training. We experimentally verify that the new dataset can significantly improve the ability of the learned FER model. To tackle this, we propose to apply a dataset distillation strategy to compress the created dataset into several informative class-wise images.
  arXiv  Detail & Related papers  (2020-05-18T09:36:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.