二十一世纪素质教育研究热点回溯与展望——基于Citespace的可视化研究
Hot Spots Retrospection and Prospect on Quality Education Research of 21st Century——Visual Analyze Based on Citespace
季俊杰 1龙丹妮1
作者信息
- 1. 江西财经大学 财税与公共管理学院,南昌 330013
- 折叠
摘要
以 2003-2022年知网数据库中 735篇核心期刊论文为对象,采用Citespace软件进行知识图谱、共线图谱、关键词聚类、时间线图谱等可视化分析后发现,21世纪以来,我国素质教育研究的视角和作者来源日趋多元,经历了快速发展、深化发展和曲折发展 3个阶段,并始终聚焦于教育理念、教育对象、教育教学内容 3个方面.由于教育理念热点频出和素质教育实践受阻,近年来相关文献发表数量有所下滑.在新时代背景下,素质教育研究应与立德树人、大思政教育、五育融合、双减政策等诸项理念与工作紧密结合,着力提升学生的政治素质、综合素质和身心素质,同时顺应国家教育数字化战略行动,研究推动素质教育数字增能,以促进素质教育研究的再次繁荣.
Abstract
Based on 735 core journal papers in the China National Knowledge Infrastructure database from 2003 to 2022,Citespace software was used to carry out visual analysis of knowledge graph,collinear graph,keyword clusteringand timeline graph.The reslut shows that since the 21st century,the perspectives and authors of quality education research in China have become increasingly diverse.It has experienced the rapid development stage,deepening stage and transformation stage,emphatically focuses on three aspects,i.e.education concept,education object and education teaching content.Due to the frequent occurrence of hot issues in education and the obstruction of quality education practice,the number of relevant papers has declined.Under the background of the new era,the research on quality education should be combined with the establish morality and cultivate talent education idea,ideological and political education,integration of five dominating education,and the dual reduction policy,focusing on improving students'political quality,comprehensive quality,and physical and mental quality.At the same time,adapting to the national digitalization education strategy action,we should research on digital empowerment for promoting quality education to promote the digital enhancement of quality education.
关键词
素质教育/Citespace/研究热点/教育数字化/双减政策Key words
quality education/Citespace/research hotspots/digital education/double reduction plan引用本文复制引用
基金项目
江西省教育科学规划课题(2022)(22YB063)
江西省教育教学改革项目(2022)(JXJG22422)
出版年
2024
NETL
NSTL
万方数据