计算机科学
知识管理
知识图
数据科学
知识工程
百科全书
情报检索
图书馆学
作者
Kechen Qu,Kam Cheong Li,Billy Tak Ming Wong,Manfred Man-fat Wu,Mengjin Liu
出处
期刊:Electronics
[Multidisciplinary Digital Publishing Institute]
日期:2024-06-28
卷期号:13 (13): 2537-2537
被引量:3
标识
DOI:10.3390/electronics13132537
摘要
This paper presents a comprehensive survey of knowledge graphs in education. It covers the patterns and prospects of research in this area. A total of 48 relevant publications between 2011 and 2023 were collected from the Web of Science, Scopus, and ProQuest for review. The findings reveal a sharp increase in recent years in the body of research into educational knowledge graphs which was mainly conducted from institutions in China. Most of the relevant research work adopted a quantitative method, such as performance evaluation, user surveys, and controlled experiments, to assess the effectiveness of knowledge graph approaches. The findings also suggest that knowledge graph approaches were primarily researched and implemented in higher education institutions, with a focus on computer science, mathematics, and engineering. The most frequently addressed objectives included enhancing knowledge representation and providing personal learning recommendations, and the most common applications were concept instruction and educational recommendations. Diverse data resources, such as course materials, student learning behaviours, and online encyclopaedia, were processed to implement knowledge graph approaches in different scenarios. Relevant technical means employed for the implementation of knowledge graphs dealt with the purposes of building knowledge ontology, achieving recommendations, and creating knowledge graphs. Various pedagogies such as personalised learning and collaborative learning are supported by the knowledge graph approaches. The findings also identified key limitations in the relevant work, including insufficient information for knowledge graph construction, difficulty in extending applications across subject areas, the restricted scale and scope of data resources, and the lack of comprehensive user feedback and evaluation processes.
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