Pedagogical Data Analysis via Federated Learning Toward Education 4.0

Song Guo, Deze Zeng, Shifu Dong

Research output: Journal article publicationJournal articleAcademic researchpeer-review

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.
Original languageEnglish
Pages (from-to)56-65
JournalAmerican Journal of Education and Information Technology
Volume4
Issue number2
Publication statusPublished - Dec 2020

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