AAKT：通过交替自回归建模增强知识追踪
AAKT: Enhancing Knowledge Tracing with Alternate Autoregressive Modeling
AI总结：
本研究提出AAKT框架，将知识追踪视为生成过程，通过问题-响应交替序列的自回归编码表示知识状态，结合辅助信息，在四个数据集上AUC、ACC、RMSE均优于基线，验证了关键组件有效性。
摘要：
知识追踪（KT）旨在根据学生之前的练习和教育场景中的额外信息预测其未来表现。由于KT有助于在教育情境中提供个性化体验，因此受到了广泛关注。同时，对先前练习序列的自回归建模已被证明对该任务有效。知识追踪自回归建模的主要挑战之一是有效表示学习者在练习过程中的前（响应前）和后（响应后）状态。现有方法通常采用复杂的模型架构，利用问题和响应记录更新学习者状态。在本研究中，我们提出了一种新的知识追踪任务视角，将其视为生成过程，符合自回归模型的原理。我们证明，知识状态可以通过对问题-响应交替序列的自回归编码直接表示，模型通过分析历史交互在隐藏状态空间中生成最可能的表示。这种方法构成了我们框架的基础，称为交替自回归知识追踪（AAKT）。此外，我们通过辅助任务将补充教育信息（如问题相关技能）纳入框架，并将额外练习细节（如响应时间）作为额外输入。我们提出的框架使用自然语言生成（NLG）中的先进自回归技术进行训练和预测。在四个真实世界KT数据集上的实证评估表明，AAKT在AUC、ACC和RMSE方面始终优于所有基线模型。此外，广泛的消融研究和可视化分析验证了AAKT中关键组件的有效性。
英文摘要：
Knowledge Tracing (KT) aims to predict students' future performances based on their former exercises and additional information in educational settings. KT has received significant attention since it facilitates personalized experiences in educational situations. Simultaneously, the autoregressive modeling on the sequence of former exercises has been proven effective for this task. One of the primary challenges in autoregressive modeling for Knowledge Tracing is effectively representing the anterior (pre-response) and posterior (post-response) states of learners across exercises. Existing methods often employ complex model architectures to update learner states using question and response records. In this study, we propose a novel perspective on knowledge tracing task by treating it as a generative process, consistent with the principles of autoregressive models. We demonstrate that knowledge states can be directly represented through autoregressive encodings on a question-response alternate sequence, where model generate the most probable representation in hidden state space by analyzing history interactions. This approach underpins our framework, termed Alternate Autoregressive Knowledge Tracing (AAKT). Additionally, we incorporate supplementary educational information, such as question-related skills, into our framework through an auxiliary task, and include extra exercise details, like response time, as additional inputs. Our proposed framework is implemented using advanced autoregressive technologies from Natural Language Generation (NLG) for both training and prediction. Empirical evaluations on four real-world KT datasets indicate that AAKT consistently outperforms all baseline models in terms of AUC, ACC, and RMSE. Furthermore, extensive ablation studies and visualized analysis validate the effectiveness of key components in AAKT.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
