本文提出了一种基于自注意力机制的知识追踪模型 Self Attentive Knowledge Tracing (SAKT)。其本质是用Transformer的 encoder 部分来做序列任务。 任务描述 利用学生的交互序列X=(x1,x2,…,xt)其中xt=(et,rt)预测学生下次的表现即预测p(rt+1=1∣et+1,X) 方法 这里首先列举使用到的符号 模型架构如下 输入编码...
1 简介 本文根据2019年《A Self-Attentive model for Knowledge Tracing》翻译终结。 SAKT:self attentive knowledge tracing. 知识追踪(Knowledge tracing)的任务是模拟每个学生在一系列学习活动中对知识概念的掌握情况。最近几年,基于RNN(Recurrent Neural Networks)方法,如 Deep Knowledge Tracing (DKT) 和Dynamic Key...
In this paper, we propose a novel Sequential Self-Attentive model for Knowledge Tracing (SSAKT). SSAKT utilizes question information based on Multidimensional Item Response Theory (MIRT) which can capture the relations between questions and skills. Then SSAKT uses a self-attention layer to capture...
With the ongoing development of online education platforms, knowledge tracing (KT) has become a critical task that can help online education platforms provide personalized education. KT aims to find out students' knowledge states and predict whether students can correctly answer the question according ...
The task of knowledge tracing involves establishing a model that characterizes the mastery level of individual students in relation to knowledge concepts (KCs), as they engage with a sequence of learning activities. Each student's knowledge is modelled by estimating the student's performance on the...
Knowledge Tracing aims to model a student's knowledge state from her past learning interactions and predict her performance in future. Although structures such as positional encoding or forgetting gate have already been used in Knowledge Tracing models, positional information with great potential is not...