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Mar, 2024
通过有监督的预训练和重要性机制微调提高低资源的知识追踪任务
Improving Low-Resource Knowledge Tracing Tasks by Supervised Pre-training and Importance Mechanism Fine-tuning
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Hengyuan Zhang, Zitao Liu, Shuyan Huang, Chenming Shang, Bojun Zhan...
TL;DR
本文提出了一种名为 LoReKT 的低资源知识追踪框架,旨在通过从丰富资源的 KT 数据集中学习可转移的参数和表示,在预训练阶段学习效果,并在后续适应低资源 KT 数据集时提供帮助。
Abstract
knowledge tracing
(KT) aims to estimate student's knowledge mastery based on their historical interactions. Recently, the
deep learning
based KT (DLKT) approaches have achieved impressive performance in the KT ta
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