在线学习资源推荐综述

Survey of online learning resource recommendation

近年来越来越多的学校广泛使用网络在线授课,然而互联网中海量的学习资源令学习者难以抉择。因此,研究在线学习资源推荐并为学习者进行个性化推荐非常重要,这可以帮助学习者快速获取其所需的优质学习资源。针对在线学习资源推荐的研究现状,从以下5个方面进行分析总结。首先,总结了目前国内外在线教育平台在学习资源推荐方面的工作;其次,分析和探讨了以知识点习题、学习路径、学习视频和学习课程为学习资源推荐目标的4种算法;接着,分别从学习者和学习资源的角度出发,以具体的算法为例,详述了常用的基于学习者画像、基于学习者行为和基于学习资源本体的3种学习资源推荐算法;此外,总结了公开的在线学习资源数据集;最后,分析了学习资源推荐系统目前存在的问题和未来的发展方向。

In recent years,more and more schools tend to use online education widely.However,learners are hard to search for their needs from the massive learning resources in the Internet.Therefore,it is very important to research the online learning resource recommendation and perform personalized recommendations for learners,so as to help learners obtain the high-quality learning resources they need quickly.The research status of online learning resource recommendation was analyzed and summarized from the following five aspects.Firstly,the current work of domestic and international online education platforms in learning resource recommendation was summed up.Secondly,four types of algorithms were analyzed and discussed:using knowledge point exercises,learning paths,learning videos and learning courses as learning resource recommendation targets respectively.Thirdly,from the perspectives of learners and learning resources,using the specific algorithms as examples,three learning resource recommendation algorithms based on learners’portraits,learners’behaviors and learning resource ontologies were introduced in detail respectively.Moreover,the public online learning resource datasets were listed.Finally,the current challenges and future research directions were analyzed.