A personalized recommendation system with combinational algorithm for online learning

被引：0
作者：
Jun Xiao
Minjuan Wang
Bingqian Jiang
Junli Li
机构：
[1] Open University of Shanghai,Shanghai Engineering Research Center of Open Distance Education
[2] Shanghai International Studies University,College of International Education
[3] San Diego State University,Learning Design and Technology
来源：
Journal of Ambient Intelligence and Humanized Computing | 2018年 / 9卷
关键词：
Combinational algorithm; Knowledge and data technology; Intelligent learning systems; Personalized recommendation;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
With the fast development of online and mobile technologies, individualized or personalized learning is becoming increasingly important. Online courses especially Massive Open Online Courses (MOOCs) often have students from many countries, with different prior knowledge, expectations, and skills. They in particular could benefit from learning materials or learning systems that are customized to meet their needs. On this note, this paper suggests a personalized recommendation system for learners in online courses. The system recommends learning resources such as relevant courses to learners enrolled in formal online courses, by using a combination of association rules, content filtering, and collaborative filtering. Pilot testing of this system in the Shanghai Lifelong Learning Network, a platform for free and open education, indicates that this recommendation system can improve the utilization rate of educational resources and also promote the learning autonomy and efficiency of students.
引用
收藏
页码：667 / 677
页数：10
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