Abstract
In contrast to pedagogies like evidence-based teaching, personalized adaptive learning (PAL) distinguishes itself by closely monitoring the progress of individual students and tailoring the learning path to their unique knowledge and requirements. A crucial technique for effective PAL implementation is knowledge tracing, which models students' evolving knowledge to predict their future performance. Based on these predictions, personalized recommendations for resources and learning paths can be made to meet individual needs. Recent advancements in deep learning have successfully enhanced knowledge tracking through Deep Knowledge Tracing (DKT). This paper introduces generative AI models to further enhance DKT. Generative AI models, rooted in deep learning, are trained to generate synthetic data, addressing data scarcity challenges in various applications across fields such as natural language processing (NLP) and computer vision (CV). This study aims to tackle data shortage issues in student learning records to enhance DKT performance for PAL. Specifically, it employs TabDDPM, a diffusion model, to generate synthetic educational records to augment training data for enhancing DKT. The proposed method's effectiveness is validated through extensive experiments on ASSISTments datasets. The experimental results demonstrate that the AI-generated data by TabDDPM significantly improves DKT performance, particularly in scenarios with small data for training and large data for testing.
Abstract (translated)
相比之下，基于证据的教学方法等教育方法，自适应个性化学习（PAL）通过密切监控每个学生的进步，将学习路径定制为他们独特的知识和需求而脱颖而出。在有效实施PAL的关键技术是知识追踪，它将学生的不断发展的知识建模为预测未来的表现。根据这些预测，可以针对个人需求定制资源和学习路径。在深度学习方面的最新进展通过深度知识追踪（DKT）成功地增强了知识追踪。本文介绍了一些生成人工智能（生成式AI）模型，以进一步增强DKT。基于深度学习的生成式AI模型被训练生成合成数据，解决各种应用领域中数据稀缺的问题，如自然语言处理（NLP）和计算机视觉（CV）。本研究旨在通过解决学生学习记录中的数据稀缺问题来提高PAL对DKT的性能。具体来说，它采用TabDDPM（扩散模型）生成合成教育记录来补充训练数据，以增强DKT。该方法的有效性通过ASSSI-T数据集的广泛实验来验证。实验结果表明，由TabDDPM生成的数据在训练和测试场景中的效果都显著提高DKT性能，尤其是在训练数据较小，测试数据较大的情况下。
URL
https://arxiv.org/abs/2405.05134
