多因素感知的双注意力知识追踪
Multi-Factors Aware Dual-Attentional Knowledge Tracing
AI总结：
针对知识追踪中因素分析忽视最近练习影响和问题信息的问题，提出多因素感知双注意力模型MF-DAKT，通过新因素、预训练和双注意力机制提升预测性能，并在真实数据集上验证优于现有方法。
摘要：
随着个性化学习需求的日益增长，知识追踪变得重要，它基于学生的历史练习来追踪学生的知识状态。因素分析方法主要使用两类分别与学生和问题相关的因素来建模学生的知识状态。这些方法使用学生的总尝试次数来建模学生的学习进度，并且几乎不强调最近相关练习的影响。此外，当前的因素分析方法忽略了问题中包含的丰富信息。在本文中，我们提出了多因素感知的双注意力模型（MF-DAKT），该模型丰富了问题表示，并基于双注意力机制利用多种因素来建模学生的学习进度。更具体地，我们提出了一种新颖的与学生相关的因素，该因素记录了学生最近对相关概念的尝试，以强调最近练习的影响。为了丰富问题表示，我们使用预训练方法融合两类问题信息，包括问题的关系和难度水平。我们还添加了关于问题难度水平的正则化项，以在预测学生表现的过程中限制预训练的问题表示进行微调。此外，我们应用双注意力机制来区分不同练习记录中因素和因素交互对最终预测的贡献。最后，我们在几个真实世界数据集上进行了实验，结果表明MF-DAKT可以优于现有的知识追踪方法。我们还进行了多项研究以验证MF-DAKT每个组件的效果。
英文摘要：
With the increasing demands of personalized learning, knowledge tracing has become important which traces students' knowledge states based on their historical practices. Factor analysis methods mainly use two kinds of factors which are separately related to students and questions to model students' knowledge states. These methods use the total number of attempts of students to model students' learning progress and hardly highlight the impact of the most recent relevant practices. Besides, current factor analysis methods ignore rich information contained in questions. In this paper, we propose Multi-Factors Aware Dual-Attentional model (MF-DAKT) which enriches question representations and utilizes multiple factors to model students' learning progress based on a dual-attentional mechanism. More specifically, we propose a novel student-related factor which records the most recent attempts on relevant concepts of students to highlight the impact of recent exercises. To enrich questions representations, we use a pre-training method to incorporate two kinds of question information including questions' relation and difficulty level. We also add a regularization term about questions' difficulty level to restrict pre-trained question representations to fine-tuning during the process of predicting students' performance. Moreover, we apply a dual-attentional mechanism to differentiate contributions of factors and factor interactions to final prediction in different practice records. At last, we conduct experiments on several real-world datasets and results show that MF-DAKT can outperform existing knowledge tracing methods. We also conduct several studies to validate the effects of each component of MF-DAKT.
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。
