research-article

Recommendation System for Privacy-Preserving Education Technologies

Authors:

Xiufang Yin Academic Editor:

Konstantinos DemertzisAuthors Info & Claims

Computational Intelligence and Neuroscience, Volume 2022

https://doi.org/10.1155/2022/3502992

Published: 01 January 2022 Publication History

Abstract

Considering the priority for personalized and fully customized learning systems, the innovative computational intelligent systems for personalized educational technologies are the timeliest research area. Since the machine learning models reflect the data over which they were trained, data that have privacy and other sensitivities associated with the education abilities of learners, which can be vulnerable. This work proposes a recommendation system for privacy-preserving education technologies that uses machine learning and differential privacy to overcome this issue. Specifically, each student is automatically classified on their skills in a category using a directed acyclic graph method. In the next step, the model uses differential privacy which is the technology that enables a facility for the purpose of obtaining useful information from databases containing individuals’ personal information without divulging sensitive identification about each individual. In addition, an intelligent recommendation mechanism based on collaborative filtering offers personalized real-time data for the users’ privacy.

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Cited By

Zhong ZGuo HQian K(2024)Deciphering the impact of machine learning on education: Insights from a bibliometric analysis using bibliometrix R-packageEducation and Information Technologies10.1007/s10639-024-12734-829:16(21995-22022)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1007/s10639-024-12734-8

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Published In

cover image Computational Intelligence and Neuroscience

Computational Intelligence and Neuroscience Volume 2022, Issue

2022

32389 pages

ISSN:1687-5265

EISSN:1687-5273

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Copyright © 2022 Shasha Xu and Xiufang Yin.

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Publisher

Hindawi Limited

London, United Kingdom

Publication History

Published: 01 January 2022

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Cited By

Zhong ZGuo HQian K(2024)Deciphering the impact of machine learning on education: Insights from a bibliometric analysis using bibliometrix R-packageEducation and Information Technologies10.1007/s10639-024-12734-829:16(21995-22022)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1007/s10639-024-12734-8

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