研究新学生知识追踪的冷启动问题，发现现有模型初始表现差但随互动提升。
Cold Start Problem: An Experimental Study of Knowledge Tracing Models with New Students
- 仅用老学生数据训练，测试新学生，模拟真实冷启动场景
- 三类模型初始准确率均低，随互动增加逐步改善
- SAKT初始表现最好但仍难应对零样本学习，适合少样本场景研究
知识追踪（KT）旨在基于学生与智能辅导系统（ITS）的交互预测其知识状态。核心挑战是冷启动问题：对仅有少量交互数据的新学生进行准确预测。与以往工作不同，本文训练模型仅使用历史学生数据，评估时完全针对全新学生。我们考察了三种KT模型——深度知识追踪（DKT）、动态键值记忆网络（DKVMN）和自注意知识追踪（SAKT）在ASSISTments 2009、2015和2017数据集上的冷启动表现。结果表明，所有模型在冷启动初期均表现不佳，但随着交互次数增加逐渐提升；其中SAKT初始准确率较高，仍存在局限性。研究强调需开发能有效泛化到新学习者的模型，尤其在少样本和零样本学习场景下。
原文摘要 · Abstract (English)
KnowledgeTracing (KT) involves predicting students' knowledge states based on their interactions with Intelligent Tutoring Systems (ITS). A key challenge is the cold start problem, accurately predicting knowledge for new students with minimal interaction data. Unlike prior work, which typically trains KT models on initial interactions of all students and tests on their subsequent interactions, our approach trains models solely using historical data from past students, evaluating their performance exclusively on entirely new students. We investigate cold start effects across three KT models: Deep Knowledge Tracing (DKT), Dynamic Key-Value Memory Networks (DKVMN), and Self-Attentive Knowledge Tracing (SAKT), using ASSISTments 2009, 2015, and 2017 datasets. Results indicate all models initially struggle under cold start conditions but progressively improve with more interactions; SAKT shows higher initial accuracy yet still faces limitations. These findings highlight the need for KT models that effectively generalize to new learners, emphasizing the importance of developing models robust in few-shot and zero-shot learning scenarios
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