.png){kind=icon}
返回
Knowledge structures construction and learning paths recommendation based on formal contexts
.png){kind=icon}
PRE
AI
DOI：10.1007/s13042-023-01985-5.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Knowledge structure is a valid characteristic for assessing individuals' knowledge and guiding their future learning. Based on formal context, there are methods to construct a knowledge structure delineated by skill function and find learning paths. However, the skill functions considered in these methods are well-formed and the skills are independent. To overcome these limitations, this paper transforms a general skill function into a formal context to extend the methods of constructing knowledge structures and recommending learning paths. The construction method of knowledge structures is discussed based on the quasi-order relation among skills, which is suitable for the situation that skills are not independent. The recommendation method of learning paths is investigated based on the subsequent states and learning complexity of skills, which can realize personalized learning. Moreover, simulation experiments are performed on five data sets and show that it is necessary to consider the quasi-order relation among skills and that the methods proposed for finding and recommending learning paths are feasible.
Keyword:
Skill function
Formal context
Quasi-order relation
Knowledge structure
Subsequent state
Learning path
期刊
IF:
2.7
论文数:
3.2K
被引数:
5.6K
机构
引用论文
.png){kind=icon}
An ab-initio based semi-empirical thermal conductivity model for multiphase uranium-zirconium alloys
PREAI
分享收藏
查看更多内容