Improving University Faculty Evaluations via multi-view Knowledge Graph

doi:10.1016/j.future.2020.11.021

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室

	Improving University Faculty Evaluations via multi-view Knowledge Graph
	Lin, Qika 1; Zhu, Yifan 1; Lu, Hao 1,2; Shi, Kaize 1; Niu, Zhendong 1,3
刊名	FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
	2021-04-01
卷号	117 页码:181-192
关键词	University faculty evaluation Knowledge graph Academic development prediction E-learning
ISSN号	0167-739X
DOI	10.1016/j.future.2020.11.021
通讯作者	Niu, Zhendong(zniu@bit.edu.cn)
英文摘要	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.
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]
WOS关键词	RISING STAR PREDICTION ; ALTMETRICS ; IMPACT
WOS研究方向	Computer Science
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000612106900015
资助机构	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)
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/43205]
专题	自动化研究所_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.