LFKT:学习与遗忘融合的深度知识追踪模型

知识追踪任务旨在根据学生历史学习行为实时追踪学生知识水平变化，并且预测学生在未来学习表现.在学生学习过程中，学习行为与遗忘行为相互交织，学生的遗忘行为对知识追踪影响很大.为了准确建模知识追踪中学习与遗忘行为，本文提出了一个兼顾学习与遗忘行为的深度知识追踪模型LFKT.LFKT模型综合考虑了四个影响知识遗忘因素，包括学生重复学习知识点的间隔时间、重复学习知识点的次数、顺序学习间隔时间以及学生对于知识点的掌握程度.结合遗忘因素，LFKT采用深度神经网络，利用学生答题结果作为知识追踪过程中知识掌握程度的间接反馈，建模融合学习与遗忘行为的知识追踪模型.通过在真实在线教育数据集上的实验，与当前知识追踪模型相比，LFKT可以更好地追踪学生知识掌握状态，并具有较好的预测性能.

LFKT: Deep Knowledge Tracing Model with Learning and Forgetting Behavior Merging

The knowledge tracing task is designed to track changes of students'' knowledge in real time based on their historical learning behaviors and to predict their future performance in learning. In the learning process, learning behaviors are intertwined with forgetting behaviors, and students'' forgetting behaviors have a great impact on knowledge tracing. In order to accurately model the learning and forgetting behaviors in knowledge tracing, a deep knowledge tracing model LFKT that combines learning and forgetting behaviors is proposed in this paper. To model such two behaviors, the LFKT model takes into account four factors that affect knowledge forgetting, including the interval between students'' repeated learning of knowledge points, the number of repeated learning of knowledge points, the interval between sequential learning, and the understanding degree of knowledge points. The model uses a deep neural network to predict knowledge status with indirect feedbacks on students'' understanding of knowledge according to students'' answers. With the experiments on the real datasets of online education, LFKT shows better performance of knowledge tracing and prediction in comparison with the traditional approaches.