Improving University Faculty Evaluations via multi-view Knowledge Graph
文献类型：期刊论文
| 作者 | Lin, Qika1; Zhu, Yifan1; Lu, Hao1,3 ; Shi, Kaize1; Niu, Zhendong1,2
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| 刊名 | FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
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| 出版日期 | 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. |
| WOS关键词 | RISING STAR PREDICTION ; ALTMETRICS ; IMPACT |
| 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研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000612106900015 |
| 出版者 | ELSEVIER |
| 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) |
| 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.Univ Pittsburgh, Sch Comp & Informat, Pittsburgh, PA 15260 USA 3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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