摘要
从学习者角度出发,立足于学习者个体的静态数据和学习中产生的动态学习行为数据,采用动态数据为主、静态数据为辅的获取方式确立学习者特征模型。参照Hadoop中的数据处理技术,从数据收集层、数据存储层、数据分析层和呈现层构建学习者个性化学习资源推送框架。最后,结合个性化资源推送框架,对个性化资源推送服务的实现途径进行分析,提出基于资源最邻近、基于相似用户矩阵库、基于特征匹配、基于学习者反馈和基于内容等混合推荐方法实现个性化学习资源的推送,以期对个性化学习资源推送的研究提供指导。
A learner characteristics model was established in this study based chiefly on dynamic data from the ongoing learning behaviors and secondarily on learners' personalized static data.A design framework of personalized resources recommendation system was constructed with reference to Hadoop data processing technology covering data collection,data storage,data analysis and data presentation.An integrated recommendation approach was suggested with reference to proximity of resources,matrix library of similar users,feature matching,learner feedback and contents.
引文
贺超凯,吴蒙.2016.ed X平台教育大数据的学习行为分析与预测[J].中国远程教育(6):54-59.
金慧,胡盈滢,宋蕾.2017.技术促进教育创新--新媒体联盟《地平线报告》(2017高等教育版)解读[J].远程教育杂志(02):3-8.
李宝,张文兰.2015.智慧教育环境下学习资源推送服务模型的构建[J].远程教育杂志(3):41-47.
牟智佳.2014.电子书包中基于教育大数据的个性化学习评价模型与系统设计[J].远程教育杂志(5):90-96.
裴莹,付世秋,吴峰.2017.我国教育大数据研究热点及存在问题的可视化分析[J].中国远程教育(5):1-8.
孙洪涛,郑勤华.2016.教育大数据的核心技术、应用现状与发展趋势[J].远程教育杂志(5):41-49.
吴青,罗儒国.2014.基于网络学习行为的学习风格挖掘[J].现代远距离教育(1):54-62.
杨现民,唐斯斯,李冀红.2016.发展教育大数据:内涵、价值和挑战[J].现代远程教育研究(1):50-61.
余正涛,高盛祥,纪鹏程.2004.RDAQAS中问句相似度计算方法研究[J].昆明理工大学学报(理工版)(2):40-44,71.
Han,J.,&Fu,Y.1994.Mining multiple-level association rules in large databases.IEEE Transactions on Knowledge and Data Engineering,11(5):798-804.
Jia-Liang Xu,&Jiao Guo.2013.Study of Personalized Service About WAP Based on Recursive Learning.2012 International Conference on Information Technology and Management Science(ICITMS 2012)Proceedings,443-449.
Kambatlaa,K.,Kollias,G.,Kumarc,V.,&Gramaa,A.2014.Trends in big data analytics.Journal of Parallel and Distributed Computing,74(7):2561-2573.
Noe,R.A.,Tews,M.J.,&Marand,A.D.2013.Individual differences and informal learning in the workplace.Journal of Vocational Behavior,83(3):327-335.
Ocepek,U.,Bosnic,Z.,Serbec,I.N.,&Rugelj,J.2013.Exploring the relation between learning style models and preferred multimedia types.Computers&Education,69(11):343-355.
Philip Chen,C.L.,&Chun-Yang,Zhang.2014.Data-intensive applications,challenges,techniques and technologies:A survey on Big Data.Information Sciences,275:314-317.
Rishi,D.,&Ruttun,R.D.2012.Macredie.The effects of individual differences and visual instructional aids on disorientation,learning performance and attitudes in a Hypermedia Learning System.Computers in Human Behavior,28(4):2182-2198.
Sarwar,B.M.,Karypis,G.,Konstan,J.A.,&Riedl,J.T.2001.Item based collaborative filteringrecommendation algorithms.In Proceedings of the 10th international world wide web conference of ACM,285-295.
Gomez,S.,Zervas,P.,Sampson,D.G.,&Fabregat,R.2014.Contextaware adaptive and personalized mobile learning delivery supported by Uo Lm P.Journal of King Saud University-Computer and Information Sciences,26(1):47-61.
Shu-Lin Wang,&Chun-Yi Wu.2011.Application of context-aware and personalized recommendation to implement an adaptive ubiquitous learning system.Expert Systems with Applications,38(9):10831-10838.
Siemens,G.,&Baker,R.S.J.D.2012.Learning Analytics and Educational Data Mining:Towards Communication and Collaboration.In B.S.Simon.Proceedings of the Second International Conference on Learning Analytics&Knowledge.New York:ACM,252-254.
Young-Duk Seo,Young-Gab Kim,Euijong Lee,Doo-Kwon Baik.2017.Personalized recommender system based on friendship strength in social network services.Expert Systems With Applications,69:135-148.