Educational Knowledge Graph:Research Progress and Future Development—Analysis of Articles Published in Core Chinese Journals from 2013 to 2023
The deep integration of knowledge graphs with education has promoted the development of smart education.However,there is a lack of literature on educational knowledge graphs currently,necessitating its improvement with regard to research normativity and content perspective.Four conclusions are presented from a systematic literature review of 55 important Chinese journal articles from the previous decade.First,the development of educational knowledge graphs requires five key technologies:ontology construction,knowledge extraction,knowledge representation,knowledge fusion,and knowledge reasoning.Deep learning methods are becoming a popular research topic in this context.Second,in the context of applicability,the educational knowledge graphs cover six application scenarios:personalized learning recommendations,intelligent Question-Answering(Q&A),teaching resource management,intelligent search,intelligent learning diagnosis,and classroom teaching analysis,and the horizon of applications is continuously expanding.Third,regarding application effects,the educational knowledge graphs promote personalized learning and fragmented ubiquitous learning of students while improving their learning performance as well as professionalism of teachers.Fourth,the education knowledge graphs suffer from several problems and challenges,such as single data modality,lack of quality datasets,low level of automation and borderline technology,high level of difficulty in knowledge modeling,insufficient competence care,lack of interoperability standards,and low rate of educational adoption.Hence,for further insight into the study,future research should refine the theory and establish standards,optimize techniques,achieve accurate modeling,and strengthen applications and lifting effects.
educational knowledge graphconstruction techniqueapplication scenarioapplication effectproblem and challenge
国家科技期刊平台
NSTL
万方数据
