Recommendation System for Privacy-Preserving Education Technologies

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.

Artificial intelligence in personalised learning: a bibliometric analysis

“…The relevant studies concentrate on the automated personalisation process. Learning systems have been proposed which could classify students based on their skills and knowledge so that personalised learning content can be provided (Tang, 2022; Xu, 2022). The highly cited references in this cluster are in relation to the application of AI for personalisation of learning experience (Chen et al , 2020a), the theory of cognitive organisation and development (Demetriou et al , 2011) and differentiated instructions (Cha and Ahn, 2014).…”

The Information Age for Education via Artificial Intelligence and Machine Learning: A Bibliometric and Systematic Literature Analysis

“…According to Singh [11], new possibilities and limitations in higher education are modifying institutional governance and design. The objective of Xu's research [12] is to satisfy educational needs by examining the use of algorithms in both conventional classrooms and e-learning situations. The sentence [13] acknowledges the benefits of machine learning in predicting academic occurrences as well as its limitations, such as mistake risks and data complexity.…”

“…For instance, [10] demonstrates the availability and interpretability of data in prediction tasks. New possibilities and restrictions in higher education are reshaping institutional governance and design, discusses the use of algorithms in conventional classrooms and e-learning to satisfy a range of educational needs [11,12]. In addition to recognizing the advantages of machine learning in forecasting academic events, Onyema [13] also acknowledges its limitations, including mistake risks and data complexities.…”