A Stable Personalised Partner Selection for Collaborative Privacy Education

Privacy education is becoming increasingly important these days, especially for young people. While several e-learning platforms for privacy awareness training have been implemented, they are typically based on traditional learning techniques. More specifically, they do not allow students to cooperate and share knowledge in order to achieve mutual benefits and improve learning outcomes. In this paper, we propose a collaborative e-learning platform for privacy education, which can provide a stable personalized partner selection mechanism using game theory. The proposed mechanism guarantees a stable student-student matching according to students' preferences (behavior and/or knowledge). Experimental results show the effectiveness of the proposed model in terms of achieving students' satisfaction compared to other existing partner selection models. The results also suggest that the proposed approach allows us to achieve better learning outcomes in privacy education.