Improving University Faculty Evaluations via multi-view Knowledge Graph

doi:10.1016/j.future.2020.11.021

	Improving University Faculty Evaluations via multi-view Knowledge Graph
	Lin, Qika 1; Zhu, Yifan 1; Lu, Hao1,2 ; Shi, Kaize 1; Niu, Zhendong 1,3
发表期刊	FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
ISSN	0167-739X
	2021-04-01
卷号	117 页码:181-192
通讯作者	Niu, Zhendong(zniu@bit.edu.cn)
摘要	University faculties generate a large amount of heterogeneous data in e-learning environments that online systems and toolkits have made widely available in all aspects of teaching and scientific researching activities. How to use the data efficiently and scientifically for faculty evaluations has recently become an important issue in university performance systems. However, it is still a challenge to comprehensively assess faculty members using multi-source and multi-modal data due to the lack of uniform representations and evaluation processes. To this end, this paper proposes a novel University Faculty Evaluation System based on a multi-view Knowledge Graph (UFES-KG) that integrates heterogeneous faculty data. Relevant data, collected both on the Internet and through university-administered internal systems, includes faculty information such as scientific research papers, patents, funds, monographs, awards, professional activities and teaching performance. Furthermore, we construct entity representations through knowledge graph embedding methods to retain their semantic information. In addition, by integrating the academic development status of scholars in the previous three years as well as student evaluation data, this paper proposes an academic development factor (ADF) for making predictions about faculty academic development. The experimental results show that this factor is closely related to the features of the knowledge graph and student evaluations. In a certain case study, this factor is superior to the traditional h-index, g-index, and RG score. Intuitively and scientifically, this multi-view approach can improve evaluations of university faculties. (C) 2020 Elsevier B.V. All rights reserved.
关键词	University faculty evaluation Knowledge graph Academic development prediction E-learning
DOI	10.1016/j.future.2020.11.021
关键词[WOS]	RISING STAR PREDICTION ; ALTMETRICS ; IMPACT
收录类别	SCI
语种	英语
资助项目	National Key R&D Program of China[2019YFB1406302] ; National Natural Science Foundation of China[61370137] ; Ministry of Education-China Mobile Research Foundation Project[2016/2-7] ; Postgraduate Education Research Project of Beijing Institute of Technology, China[2017JYYJG-004] ; China's National Strategic Basic Research Program (973 Program)[2012CB720700]
项目资助者	National Key R&D Program of China ; National Natural Science Foundation of China ; Ministry of Education-China Mobile Research Foundation Project ; Postgraduate Education Research Project of Beijing Institute of Technology, China ; China's National Strategic Basic Research Program (973 Program)
WOS研究方向	Computer Science
WOS类目	Computer Science, Theory & Methods
WOS记录号	WOS:000612106900015
出版者	ELSEVIER
引用统计	被引频次：16[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/43206
专题	复杂系统管理与控制国家重点实验室
通讯作者	Niu, Zhendong
作者单位	1.Beijing Inst Technol, Sch Comp Sci & Technol, Beijing 100081, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 3.Univ Pittsburgh, Sch Comp & Informat, Pittsburgh, PA 15260 USA
推荐引用方式 GB/T 7714	Lin, Qika,Zhu, Yifan,Lu, Hao,et al. Improving University Faculty Evaluations via multi-view Knowledge Graph[J]. FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE,2021,117:181-192.
APA	Lin, Qika,Zhu, Yifan,Lu, Hao,Shi, Kaize,&Niu, Zhendong.(2021).Improving University Faculty Evaluations via multi-view Knowledge Graph.FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE,117,181-192.
MLA	Lin, Qika,et al."Improving University Faculty Evaluations via multi-view Knowledge Graph".FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 117(2021):181-192.