关键词: 教育数据挖掘, 知识追踪, 深度学习, 个性化推荐, 记忆增强神经网络

Abstract:

Knowledge Tracking（KT） model is a hot topic in the field of educational data mining. KT can find student’s weak knowledge points and recommend the best learning path and exercises to students. The existing KT model is only applicable to the modeling of a single knowledge point and does not consider the problem of students’ mastery speed. Mastery Speed Knowledge Tracing（MSKT） model is proposed. MSKT borrows from the idea of Memory Augmented Neural Network（MANN） and the advantages of Dynamic Key-Value Memory Network model（DKVMN）, and uses the current memory contents when calculating delete vectors and add vectors. Experiments show the effectiveness and superiority of MSKT model, and similar exercises can be found automatically.

Key words: educational data mining, knowledge tracing, deep learning, personalized recommendation, Memory Augmented Neural Network