.png){kind=icon}
返回
Exercise recommendation based on knowledge concept prediction
.png){kind=icon}
PRE
AI
DOI：10.1016/j.knosys.2020.106481.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Good recommendation for difficulty exercises can effectively help to point the students/users in the right direction, and potentially empower their learning interests. It is however challenging to select the exercises with reasonable difficulty for students as they have different learning status and the size of exercise bank is quite large. The classic collaborative filtering (CF) based recommendation methods rely heavily on the similarities among students or exercises, leading to recommend exercises with mismatched difficulty. This paper proposes a novel exercise recommendation method, which uses Recurrent Neural Networks (RNNs) to predict the coverage of knowledge concepts, and uses Deep Knowledge Tracing (DKT) to predict students' mastery level of knowledge concepts based on the student's exercise answer records. The predictive results are utilized to filter the exercises; therefore, a subset of exercise bank is generated. As such, a complete list of recommended exercises can be obtained by solving an optimization problem. Extensive experimental studies show that our proposed approach has advantages over some existing baseline methods, not only in terms of the evaluation of difficulty of recommended exercises, but also the diversity and novelty of the recommendation lists. (C) 2020 Elsevier B.V. All rights reserved.
Keyword:
Recommender system
Online learning
Recurrent Neural Networks
Deep Knowledge Tracing
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
K
IF:
7.6
论文数:
1.2W
被引数:
4.5W
机构
引用论文
Recommender systems in e-learning environments: a survey of the state-of-the-art and possible extensions电子学习环境中的推荐系统: 对最新技术和可能扩展的调查
.png){kind=icon}
PREAI
分享收藏A learning path recommendation model based on a multidimensional knowledge graph framework for e-learning基于多维知识图谱框架的e-learning学习路径推荐模型
PREAI
分享收藏
Dual-regularized matrix factorization with deep neural networks for recommender systems推荐系统中基于深度神经网络的双正则矩阵分解
PREAI
分享收藏
查看更多内容