Abstract
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
Abstract (translated)
知识追踪（KT）涉及根据学生与智能辅导系统（ITS）的互动来预测他们的知识状态。其中一个重要挑战是冷启动问题，即在只有少量交互数据的情况下准确预测新学生的知识状态。不同于以往的研究通常使用所有学生初期互动的数据训练KT模型，并在其后续互动中进行测试，我们的方法仅利用过去学生的历史数据训练模型，并且完全在全新的未见过的学生上评估其性能。我们研究了冷启动对三种KT模型的影响：深度知识追踪（DKT）、动态键值记忆网络（DKVMN）和自注意力知识追踪（SAKT），并使用ASSISTments 2009、2015 和 2017 数据集进行实验。 结果显示，在初始的冷启动条件下，所有模型均表现不佳，但随着互动次数增加，它们的表现逐渐改善。其中，SAKT 显示出更高的初始准确性，但仍存在局限性。这些发现强调了开发能够有效推广到新学习者并且在少量数据（few-shot）甚至零样本（zero-shot）情景下也能稳健工作的知识追踪模型的重要性。
URL
https://arxiv.org/abs/2505.21517
