Pedagogical Data Analysis Via Federated Learning Toward Education 4.0

Song Guo; Deze Zeng; Shifu Dong

doi:doi:10.11648/j.ajeit.20200402.13

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Pedagogical Data Analysis Via Federated Learning Toward Education 4.0

Song Guo, Deze Zeng, Shifu Dong

Published in American Journal of Education and Information Technology (Volume 4, Issue 2)

Received: 23 June 2020 Accepted: 15 July 2020 Published: 4 August 2020

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Abstract

Pedagogical data analysis has been recognized as one of the most important features in pursuing Education 4.0. The recent rapid development of ICT technologies benefits and revolutionizes pedagogical data analysis via the provisioning of many advanced technologies such as big data analysis and machine learning. Meanwhile, the privacy of the students become another concern and this makes the educational institutions reluctant to share their students' data, forming isolated data islands and hindering the realization of big educational data analysis. To tackle such challenge, in this paper, we propose a federated learning based education data analysis framework FEEDAN, via which education data analysis federations can be formed by a number of institutions. None of them needs to direct exchange their students' data with each other and they always keep the data in their own place to guarantee their students' privacy. We apply our framework to analyze two real education datasets via two different federated learning paradigms. The experiment results show that it not only guarantees the students' privacy but also indeed breaks the borders of data island by achieving a higher analysis quality. Our framework can much approach the performance of centralized analysis which needs to collect the data in a common place with the risk of privacy exposure.

Published in	American Journal of Education and Information Technology (Volume 4, Issue 2)
DOI	10.11648/j.ajeit.20200402.13
Page(s)	56-65
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2020. Published by Science Publishing Group

Keywords

Pedagogical Data Analytics, Federated Learning, Education 4.0

References

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APA Style

Song Guo, Deze Zeng, Shifu Dong. (2020). Pedagogical Data Analysis Via Federated Learning Toward Education 4.0. American Journal of Education and Information Technology, 4(2), 56-65. https://doi.org/10.11648/j.ajeit.20200402.13

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ACS Style

Song Guo; Deze Zeng; Shifu Dong. Pedagogical Data Analysis Via Federated Learning Toward Education 4.0. Am. J. Educ. Inf. Technol. 2020, 4(2), 56-65. doi: 10.11648/j.ajeit.20200402.13

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AMA Style

Song Guo, Deze Zeng, Shifu Dong. Pedagogical Data Analysis Via Federated Learning Toward Education 4.0. Am J Educ Inf Technol. 2020;4(2):56-65. doi: 10.11648/j.ajeit.20200402.13

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@article{10.11648/j.ajeit.20200402.13,
  author = {Song Guo and Deze Zeng and Shifu Dong},
  title = {Pedagogical Data Analysis Via Federated Learning Toward Education 4.0},
  journal = {American Journal of Education and Information Technology},
  volume = {4},
  number = {2},
  pages = {56-65},
  doi = {10.11648/j.ajeit.20200402.13},
  url = {https://doi.org/10.11648/j.ajeit.20200402.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajeit.20200402.13},
  abstract = {Pedagogical data analysis has been recognized as one of the most important features in pursuing Education 4.0. The recent rapid development of ICT technologies benefits and revolutionizes pedagogical data analysis via the provisioning of many advanced technologies such as big data analysis and machine learning. Meanwhile, the privacy of the students become another concern and this makes the educational institutions reluctant to share their students' data, forming isolated data islands and hindering the realization of big educational data analysis. To tackle such challenge, in this paper, we propose a federated learning based education data analysis framework FEEDAN, via which education data analysis federations can be formed by a number of institutions. None of them needs to direct exchange their students' data with each other and they always keep the data in their own place to guarantee their students' privacy. We apply our framework to analyze two real education datasets via two different federated learning paradigms. The experiment results show that it not only guarantees the students' privacy but also indeed breaks the borders of data island by achieving a higher analysis quality. Our framework can much approach the performance of centralized analysis which needs to collect the data in a common place with the risk of privacy exposure.},
 year = {2020}
}

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TY  - JOUR
T1  - Pedagogical Data Analysis Via Federated Learning Toward Education 4.0
AU  - Song Guo
AU  - Deze Zeng
AU  - Shifu Dong
Y1  - 2020/08/04
PY  - 2020
N1  - https://doi.org/10.11648/j.ajeit.20200402.13
DO  - 10.11648/j.ajeit.20200402.13
T2  - American Journal of Education and Information Technology
JF  - American Journal of Education and Information Technology
JO  - American Journal of Education and Information Technology
SP  - 56
EP  - 65
PB  - Science Publishing Group
SN  - 2994-712X
UR  - https://doi.org/10.11648/j.ajeit.20200402.13
AB  - Pedagogical data analysis has been recognized as one of the most important features in pursuing Education 4.0. The recent rapid development of ICT technologies benefits and revolutionizes pedagogical data analysis via the provisioning of many advanced technologies such as big data analysis and machine learning. Meanwhile, the privacy of the students become another concern and this makes the educational institutions reluctant to share their students' data, forming isolated data islands and hindering the realization of big educational data analysis. To tackle such challenge, in this paper, we propose a federated learning based education data analysis framework FEEDAN, via which education data analysis federations can be formed by a number of institutions. None of them needs to direct exchange their students' data with each other and they always keep the data in their own place to guarantee their students' privacy. We apply our framework to analyze two real education datasets via two different federated learning paradigms. The experiment results show that it not only guarantees the students' privacy but also indeed breaks the borders of data island by achieving a higher analysis quality. Our framework can much approach the performance of centralized analysis which needs to collect the data in a common place with the risk of privacy exposure.
VL  - 4
IS  - 2
ER  -

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Author Information

Song Guo

Department of Computing, The Hong Kong Polytechnic University, Hong Kong SAR, China
Deze Zeng

School of Computer Science, China University of Geosciences, Wuhan, China
Shifu Dong

School of Computer Science, China University of Geosciences, Wuhan, China

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Plain Text BibTeX RIS

APA Style

Song Guo, Deze Zeng, Shifu Dong. (2020). Pedagogical Data Analysis Via Federated Learning Toward Education 4.0. American Journal of Education and Information Technology, 4(2), 56-65. https://doi.org/10.11648/j.ajeit.20200402.13

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ACS Style

Song Guo; Deze Zeng; Shifu Dong. Pedagogical Data Analysis Via Federated Learning Toward Education 4.0. Am. J. Educ. Inf. Technol. 2020, 4(2), 56-65. doi: 10.11648/j.ajeit.20200402.13

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AMA Style

Song Guo, Deze Zeng, Shifu Dong. Pedagogical Data Analysis Via Federated Learning Toward Education 4.0. Am J Educ Inf Technol. 2020;4(2):56-65. doi: 10.11648/j.ajeit.20200402.13

Copy | Download

@article{10.11648/j.ajeit.20200402.13,
  author = {Song Guo and Deze Zeng and Shifu Dong},
  title = {Pedagogical Data Analysis Via Federated Learning Toward Education 4.0},
  journal = {American Journal of Education and Information Technology},
  volume = {4},
  number = {2},
  pages = {56-65},
  doi = {10.11648/j.ajeit.20200402.13},
  url = {https://doi.org/10.11648/j.ajeit.20200402.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajeit.20200402.13},
  abstract = {Pedagogical data analysis has been recognized as one of the most important features in pursuing Education 4.0. The recent rapid development of ICT technologies benefits and revolutionizes pedagogical data analysis via the provisioning of many advanced technologies such as big data analysis and machine learning. Meanwhile, the privacy of the students become another concern and this makes the educational institutions reluctant to share their students' data, forming isolated data islands and hindering the realization of big educational data analysis. To tackle such challenge, in this paper, we propose a federated learning based education data analysis framework FEEDAN, via which education data analysis federations can be formed by a number of institutions. None of them needs to direct exchange their students' data with each other and they always keep the data in their own place to guarantee their students' privacy. We apply our framework to analyze two real education datasets via two different federated learning paradigms. The experiment results show that it not only guarantees the students' privacy but also indeed breaks the borders of data island by achieving a higher analysis quality. Our framework can much approach the performance of centralized analysis which needs to collect the data in a common place with the risk of privacy exposure.},
 year = {2020}
}

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TY  - JOUR
T1  - Pedagogical Data Analysis Via Federated Learning Toward Education 4.0
AU  - Song Guo
AU  - Deze Zeng
AU  - Shifu Dong
Y1  - 2020/08/04
PY  - 2020
N1  - https://doi.org/10.11648/j.ajeit.20200402.13
DO  - 10.11648/j.ajeit.20200402.13
T2  - American Journal of Education and Information Technology
JF  - American Journal of Education and Information Technology
JO  - American Journal of Education and Information Technology
SP  - 56
EP  - 65
PB  - Science Publishing Group
SN  - 2994-712X
UR  - https://doi.org/10.11648/j.ajeit.20200402.13
AB  - Pedagogical data analysis has been recognized as one of the most important features in pursuing Education 4.0. The recent rapid development of ICT technologies benefits and revolutionizes pedagogical data analysis via the provisioning of many advanced technologies such as big data analysis and machine learning. Meanwhile, the privacy of the students become another concern and this makes the educational institutions reluctant to share their students' data, forming isolated data islands and hindering the realization of big educational data analysis. To tackle such challenge, in this paper, we propose a federated learning based education data analysis framework FEEDAN, via which education data analysis federations can be formed by a number of institutions. None of them needs to direct exchange their students' data with each other and they always keep the data in their own place to guarantee their students' privacy. We apply our framework to analyze two real education datasets via two different federated learning paradigms. The experiment results show that it not only guarantees the students' privacy but also indeed breaks the borders of data island by achieving a higher analysis quality. Our framework can much approach the performance of centralized analysis which needs to collect the data in a common place with the risk of privacy exposure.
VL  - 4
IS  - 2
ER  -

Copy | Download