A game theoretical model for a collaborative e-learning platform on privacy awareness
Nowadays, with the increasing use of digital technologies, especially for teenagers, privacy education plays an important role in their lives. While several e-learning platforms for privacy awareness training have been implemented, they are typically based on traditional learning techniques. In particular, these platforms do not allow students to cooperate and share knowledge with each other in order to achieve mutual benefits and improve learning outcomes.  In other words, they lack student-student interaction. Recent research on learning methods shows that the collaboration among students can result in better learning outcomes compared to other learning approaches.  Motivated by the above-mentioned facts, and since privacy domain is strongly linked to the social lives of teens, there is a pressing need for providing a collaborative learning platform for teaching privacy, and at the same time, allows students to share knowledge, interact with each other, solve quizzes collaboratively, and discuss privacy issues and situations.  For this purpose, this work proposes “Teens-online”, a collaborative e-learning platform for privacy awareness. The curriculum provided in this platform is based on the Personal Data Protection Competency Framework for School Students.  Moreover, the proposed platform is equipped with a partner-matching mechanism based on matching game theory. This mechanism guarantees a stable student-student matching according to a student's need (behavior and/or knowledge). Thus, mutual benefits will be attained by minimizing the chances of cooperating with incompatible students.  Experimental results show that the average learning-related utility obtained by applying the proposed partner-matching algorithm is much higher than the average utility obtained using other matching mechanisms. The results also suggest that by adopting the proposed approach, each student can be paired with their optimal partners, which in turn helps them reach their highest learning outcomes.
Collaborative learning Game theory Privacy education E-learning Stable matching problem