A Survey of Knowledge Graph Approaches and Applications in Education

Author:

Qu Kechen1,Li Kam Cheong2,Wong Billy T. M.2ORCID,Wu Manfred M. F.2,Liu Mengjin2

Affiliation:

1. Credit Bank Department, The Open University of China, 75 Fuxing Road, Beijing 100039, China

2. Institute for Research in Open and Innovative Education, Hong Kong Metropolitan University, Homantin, Kowloon, Hong Kong, China

Abstract

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.

Funder

Hong Kong Metropolitan University

Publisher

MDPI AG

Reference73 articles.

1. Kejriwal, M. (2022). Knowledge graphs: A practical review of the research landscape. Information, 13.

2. Knowledge graphs;Hogan;ACM Comput. Surv.,2021

3. A survey on knowledge graphs: Representation, acquisition, and applications;Ji;IEEE Trans. Neural Netw. Learn. Syst.,2021

4. Tiddi, I., Lécué, F., and Hitzler, P. (2020). Knowledge Graphs for Explainable Artificial Intelligence: Foundations, Applications and Challenges, IOS Press.

5. Singhal, A. (2024, May 25). Introducing the Knowledge Graph: Things, Not Strings. Official Google Blog. Available online: https://blog.google/products/search/introducing-knowledge-graph-things-not/.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3