摘要
随着信息技术和网络教育的发展,学习资源呈现爆炸式增长,面对丰富的学习资源,学习者并不能在短时间内最大程度匹配到适合自己的学习资源。个性化学习资源推荐(Personalized Learning Resource Recommendation,PLRR)利用新一代信息技术,全面分析学习者特征、行为、目标等信息,从海量学习资源中筛选出符合其需求的资源,并以合适的方式呈现给学习者,以提高其学习效率和满意度。本文主要从PLRR基本框架、主要算法、面临的挑战和发展趋势进行阐述,旨在为相关研究者提供一个参考框架,促进PLRR领域交流和发展。
With the development of information technology and network education, learning resources show an explosive growth. Facing the rich learning resources, learners cannot match the most suitable learning resources for themselves in a short time. Personalized learning resource recommendation (PLRR) uses the new generation of information technology to comprehensively analyze the characteristics, behaviors, goals and other information of learners, filter out the resources that meet their needs and interests from the massive learning resources, and present them to learners in a suitable way, in order to improve their learning efficiency and satisfaction. This paper mainly elaborates on the basic framework, main methods and techniques, typical application scenarios, challenges and future directions of PLRR, aiming to provide a reference framework for relevant researchers and promote the communication and development of PLRR field.
作者
郭文静
GUO Wenjing(Zhejiang Fashion Institute of Technology,Ningbo Zhejiang 315000)
出处
《软件》
2023年第10期53-57,共5页
Software
基金
浙江纺织服装职业技术学院,基于知识图谱的在线学习资源推荐研究(2023-1B-002)。
关键词
学习资源
个性化
推荐
learning resources
personalization
recommendation
