Knowledge Tracking Model Based on Learning Process

• 作者: Wei Li Xiaodong Yan Yu Zou
• 来源期刊: 电脑和通信(英文)
• 学科: 文学
• 关键词: Knowledge Tracking Deep Learning Memory Augmented Neural Network
• 年，卷（期）: dnhtxyw_2020,（10）
• 页码范围: 7-17
• 页数: 11页
• 分类号: H31

摘要

<div style="text-align:justify;"> Knowledge tracking model has been a research hotspot in the field of educational data mining for a long time. Knowledge tracking can automatically discover students’ weak knowledge points, which helps to improve students’ self-motivation in learning and realize personalized guidance. The existing KT model has some shortcomings, such as the limitation of the calculation of knowledge growth, and the imperfect forgetting mechanism of the model. To this end, we proposed a new knowledge tracking model based on learning process (LPKT), LPKT applies the idea of Memory Augmented Neural Net-work(MANN).When we model the learning process of students, two additional important factors are considered. One is to consider the current state of knowledge of the students when updating the dynamic matrix of the neural network, and the other is to improve the forgetting mechanism of the model. In this paper we verified the effectiveness and superiority of LPKT through comparative experiments, and proved that the model can improve the effect of knowledge tracking and make the process of deep knowledge tracking easier to understand. </div>