摘要
在线学习作为一种新型的学习方式,能够为学习者提供个性化的学习支持。有效推荐个性化学习路径是学习服务研究中的重点问题。文章结合大数据背景下个性化学习的特征,建立学习者模型,通过数据挖掘技术深入分析学习者的学习行为信息以及知识之间的关系,结合基于内容的推荐和协同过滤的推荐方式,设计个性化学习路径推荐的具体方案,为解决在线学习过程中学习者面临的“信息过载”和“知识迷航”问题提供参考和借鉴。
As a new learning mode, on-line learning can provide personalized services. Effectively recommending personalized learning paths is a problem to be studied. By considering characteristics of personalized, this article establishes a learner model to analyze the learner's learning data and the relationship between knowledge and learning behavior through data mining technology. By combining personalized and content-based recommendations, the specific implementation plan of the learning path recommendation is presented to provide reference and suggestion for solving the problems of “information overload” and “knowledge loss” faced by learners in the online learning process.
作者
杨淼
董永权
胡玥
Yang Miao;Dong Yongquan;Hu Yue(School of Wisdom Education , Jiangsu Normal University, Xuzhou, Jiangsu 221100)
出处
《上海教育评估研究》
2019年第5期58-61,共4页
Shanghai Journal of Educational Evaluation
基金
江苏师范大学研究生创新项目“基于知识分类理论的高校混合学习有效性实证研究”(2018YXJ691)
关键词
学习者模型
数据挖掘
个性化
学习路径推荐
Learner model
Data mining
Personalization
Learning path recommendation
