教育知识图谱:研究进展与未来发展——基于2013—2023年中文核心期刊载文的分析  

Educational Knowledge Graph:Research Progress and Future Development--Analysis of Articles Published in Core Chinese Journals from 2013 to 2023

作　　者：李惠乾 钟柏昌 LI Huiqian;ZHONG Baichang(School of Information Technology in Education,South China Normal University,Guangzhou 510631,Guangdong,China)

机构地区：[1]华南师范大学教育信息技术学院,广东广州510631

出　　处：《计算机工程》2024年第7期1-12,共12页Computer Engineering

基　　金：国家社科基金教育学一般课题(BCA220219)。

摘　　要：知识图谱与教育教学的深度融合推动了智慧教育的发展。目前有关教育知识图谱的文献综述较为缺乏,有必要从研究规范性及内容视角方面进行补充完善。利用系统性文献综述法对近10年发表的55篇中文核心期刊文献进行统计分析后发现:在关键技术方面,教育知识图谱构建主要包含本体构建、知识抽取、知识表示、知识融合和知识推理5项技术,深度学习方法逐渐成为研究热点;在应用场景方面,教育知识图谱覆盖个性化学习推荐、智能问答(Q&A)、教学资源管理、智能搜索、智能学情诊断和课堂教学分析6类场景,应用的广度和深度不断拓展;在应用效果方面,教育知识图谱促进了学习者个性化学习和碎片化泛在学习,提升了学习者的学习绩效和教师的专业素养;在问题与挑战方面,教育知识图谱存在数据模态单一与缺乏优质数据集、自动化程度低与技术存在边界性、知识建模难度高与能力关照不足、缺乏互操作标准与教育应用率低等问题。后续研究将从完善理论与建立标准、优化技术与精准建模、强化应用与提升效果等方面进行深化。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.

关 键 词：教育知识图谱 构建技术 应用场景 应用效果 问题与挑战 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]