Conference paper 2020

Toward sustainable learning economy through a block-chain based management system

CSEDU 2020 - Proceedings of the 12th International Conference on Computer Supported Education
Conference · Vol. 2 · pp. 430-437
Abstract

This paper proposes Sustainable Learning Economy (SLE) based on market principles. SLE will utilize the blockchain technology in order to let learners trade their learning results in cryptocurrencies, which in turn gives the learners a strong motivation to acquire the knowledge independently to gain their rewards. The main challenge in SLE has been guaranteeing learning quality; however, this could be resolved using competency-based learning (CBL), an efficient learning method to prioritize acquired knowledge over knowledge acquisition. Unfortunately, due to social, corporate, and educational demands, competency models require significant time, manpower, and expertise. While Conventional Competency Management Systems (CMS) reduces costs by providing an integrated environment for CBL operations, it is not able to reduce development costs. Therefore, to reduce development costs, this paper developed a Smart CMS, which harnessed concepts of distributed artificial intelligence, the power of internet resources, and network analysis technology to automatically develop competencies tailored to the purpose, strengths, and characteristics of individual learners. © © 2020 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

Keywords

Author Keywords

Blockchain Bipartite Graph Competency based Learning Distributed Artificial Intelligence Open Online Education

Index Keywords

E-learning Learning systems Commerce cost benefit analysis Management systems Efficient learning Blockchain Knowledge acquisition Cost reduction Competency management systems Development costs Distributed Artificial Intelligence Integrated environment Internet resources Learning quality
Author Affiliations
NPO CCC-TIES, Nara, Japan
Kyoto Women's University, Kyoto, Kyoto, Japan
Kumamoto University, Kumamoto, Kumamoto, Japan
Chiba University of Commerce, Ichikawa, Chiba, Japan
Funding & Acknowledgements
Japan Society for the Promotion of Science, KAKEN
Grant: 17H01844, JP7H01844
This work was supported in part by JSPS KAKENHI Grant Number JP7H01844 and NII Joint Research Grant and ROIS NII Open Collaborative Research 2019-10-1. The authors would also like to thank Enago (www.enago.jp) for checking the earlier version of English manuscript.
National Institute of Informatics, NII
This work was supported in part by JSPS KAKENHI Grant Number JP7H01844 and NII Joint Research Grant and ROIS NII Open Collaborative Research 2019-10-1. The authors would also like to thank Enago (www.enago.jp) for checking the earlier version of English manuscript.
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Document Identifiers
  • EID 2-s2.0-85091802578
  • Language English