.png){kind=icon}
返回
An improved constrained learning path adaptation problem based on genetic algorithm
.png){kind=icon}
PRE
AI
DOI：10.1080/10494820.2021.1937659.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Adaptive learning has garnered researchers' interest. The main issue within this field is how to select appropriate learning objects (LOs) based on learners' requirements and context, and how to combine the selected LOs to form what is known as an adaptive learning path. Heuristic and metaheuristic approaches have achieved significant progress on personalized and adaptive recommendations, but the operators of some heuristic algorithms are often fixed which decreases the algorithms' extendibility. This paper reviews existing works and proposes an innovative approach. We model the proposed approach as a constraints satisfaction problem, and an improved genetic algorithm named adaptive genetic algorithm is proposed to solve it. The proposed solution does not only reduce the search space size and increase search efficiency but also it is more explicit in finding the best composition of LOs for a specific learner. As a result, the best personalized adaptive learning resources combination will be found in lesser time.
Keyword:
Learning path adaptation
learner's context
genetic algorithm
evolutionary algorithms
intelligent learning environments
CSP
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
IF:
5.3
论文数:
2.8K
被引数:
9.0K
机构
引用论文
PC2PSO: personalized e-course composition based on Particle Swarm Optimization.png)
APPLIED INTELLIGENCE
APPLIED INTELLIGENCEIF3.5
PREAI
分享收藏
分享收藏分享收藏
查看更多内容