Parallel-education-blockchain driven smart education : challenges and issues |
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Author: | Gong, Xiaoyan1,2; Liu, Xiwei1,2; Jing, Sifeng1,2; |
Organizations: |
1The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences Beijing, China 100190 2Institute of Smart Education Systems, Qingdao Academy of Intelligent Industries 3The Cloud Computing Center, Chinese Academy of Sciences Dongguan, China
4The Center for Ubiquitous Computing, University of Oulu, Oulu, Finland
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Format: | article |
Version: | accepted version |
Access: | open |
Online Access: | PDF Full Text (PDF, 0.5 MB) |
Persistent link: | http://urn.fi/urn:nbn:fi-fe202003238727 |
Language: | English |
Published: |
Institute of Electrical and Electronics Engineers,
2018
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Publish Date: | 2020-03-23 |
Description: |
AbstractRecently education blockchain driven smart education has become focus of attention, and related system frameworks and key technologies are presented. However, problems of difficult to model, difficult to experiment, and difficult to optimize in education blockchain need to be further solved, and driving mechanisms, application scenarios and other issues need further analysis. This paper first introduces education blockchain, challenges and issues, then based on introduction of parallel intelligence theory and parallel blockchain, it proposes parallel education blockchain, and its driven mechanism, function distribution, data transfer, application scenarios and related issues are elaborated; At last, several questions are raised for discussion. see all
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ISBN: | 978-1-7281-1312-8 |
ISBN Print: | 978-1-7281-1313-5 |
Pages: | 2390 - 2395 |
DOI: | 10.1109/CAC.2018.8623198 |
OADOI: | https://oadoi.org/10.1109/CAC.2018.8623198 |
Host publication: |
2018 Chinese Automation Congress (CAC) |
Conference: |
Chinese Automation Congress |
Type of Publication: |
A4 Article in conference proceedings |
Field of Science: |
213 Electronic, automation and communications engineering, electronics |
Subjects: | |
Funding: |
We would like to acknowledge support in part from the National Natural Science Foundation of China under Grants 61773382, 61773381, 61533019 and 61304201, Chinese Guangdong’s S&T project (2017B090912001, 2015B010103001, 2016B090910001), Dongguan’s Innovation Talents Project (Gang Xiong). 2017 Special Cooperative Project of Hubei Province and Chinese Academy of Sciences. |
Copyright information: |
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