Teens-Online: a Game Theory-Based Collaborative Platform for Privacy Education

• Published in: International journal of artificial intelligence in education Vol. 31; no. 4; pp. 726 - 768
• Main Authors: Yusri, Rita, Abusitta, Adel, Aïmeur, Esma
• Format: Journal Article
• Language: English
• Published: New York Springer New York 01.12.2021; Springer; Springer Nature B.V
• Subjects: A festschrift in honour of Jim Greer; Adolescents; Algorithms; Artificial Intelligence; Collaborative learning; Computer Science; Computers and Education; Cooperative Learning; Core curriculum; Education; Educational Environment; Educational Resources; Educational Technology; Electronic Learning; Game Theory; Internet; Knowledge; Learning activities; Matching; Online instruction; Peers; Personal information; Privacy; Reputation management; School environment; Students; Teaching Methods; Teenagers; User Interfaces and Human Computer Interaction; Collaborative learning. Game theory. Privacy education. E-learning. Stable matching problem
• ISSN: 1560-4292; 1560-4306
• DOI: 10.1007/s40593-020-00224-0

Nowadays, privacy education plays an important role in teenagers’ lives. Since this domain is strongly linked to their social life, it is preferable to provide a collaborative learning environment that teaches privacy, and at the same time, allows students to share knowledge, to interact with each other, to solve quizzes collaboratively and to discuss privacy issues and situations. To this end, we propose “Teens-online”, a collaborative e-learning platform for privacy awareness. The curriculum provided in this platform is based on the International Competency Framework on Privacy Education. 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 the student’s need (behavior and/or knowledge). Thus, mutual benefits will be attained by largely minimizing the chances of cooperating with incompatible students. Experimental results show that the average utility obtained by applying the proposed algorithm is much higher than the average utility obtained using other matching mechanisms. The results suggest that by adopting the proposed approach, each student can be paired with their optimal partners, which in turn can help them to engage more in learning activities.