摘要
随着在线学习的大规模普及,面对海量学习资源,结合学习者的认知诊断分析、学习兴趣和偏好挖掘的学习资源推荐研究已受到广泛关注。基于CiteSpace进行计量学分析,对个性化在线学习资源推荐现有研究进行综述,分析发文量变化态势、高发文量权威期刊、作者共现图谱和高产机构等,从关键词共现图谱和共被引文献聚类图谱入手,阐述将传统算法与深度学习、知识图谱、认知诊断等相结合研究学习资源混合推荐的热点,归纳开展支持知识迁移、碎片化资源整合和动态推荐等方面的在线学习资源推荐研究趋势。最后,指出未来研究方向,以期为推进在线学习资源推荐研究提供参考与启发。
With the large-scale popularization of online learning,facing the massive learning resources,the online learning resources recommendation by combining the learners’cognitive diagnosis analysis,and data mining of learning interests and preferences,has become an important research focus.Based on the bibliometrics analysis via the CiteSpace,the research progress on the personalized online learning resources recommendation is reviewed.The basic research situation is introduced according to the publications amount variation,the key journals,the author’s co-occurrence map and affiliations.From the aspects of the co-occurrence map of keywords and co-cited literature cluster map,the hot spots of online learning resources hybrid recommendation are elaborated in detail by combing the traditional algorithms with deep learning,knowledge mapping and cognitive diagnosis.Based on it,the research trends are generalized by supporting the knowledge transfer,fragmented resources integration and dynamic recommendation.Finally,the main directions of future research are pointed out,which could provide important references for promoting the research of online learning resource recommendation.
作者
马华
李京泽
唐文胜
黄卓轩
MA Hua;LI Jing-ze;TANG Wen-sheng;HUANG Zhuo-xuan(College of Information Science and Engineering,Hunan Normal University,Changsha 410081,China;Tencent Cloud Computing(Changsha)Co.,Ltd,Changsha 410221,China)
出处
《软件导刊》
2023年第1期29-37,共9页
Software Guide
基金
国家自然科学基金项目(62077014)。
