A Stable Personalised Partner Selection

for Collaborative Privacy Education

 Rita Yusri, Adel Abusitta, Esma Aïmeur

https://doi.org/10.1145/3386392.3397597

 

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).

The problem

One of the important problems in the collaborative environments is the lack of mechanisms for selecting the optimal partner(s). Usually, the process of selecting a partners is done randomly or by a student directly.

 Research contribution

  •  Propose and implement a stable personalized-based partner selection algorithm based on game theory. In particular, we integrate this algorithm into the proposed collaborative e-learning platform for privacy education.
  • Integrate the history of partners’ interactions (knowledge  and behavior) into the proposed matching algorithm, in order to allow students to improve their selections and learn from their experience, as well as from their colleagues’ experience.

The Stable Partner Selection Algorithm (SPSA)

The Stable Partner Selection Algorithm (SPSA) is based on ‘Game theory‘. More specifically, we adopt the “stable marriage algorithm” (Gale & Shapley) in order to obtain a perfect one-to-one match between the students of two groups (i.e., two classes) to enforce the collaboration among students from different backgrounds.
 
Notice: S. Shapley awarded the Nobel Prize in Economics for the theory of stable matching in 2012.
 
The procedures are divided to:
  • Knowledge-based Selection
  • Hybrid Selection (Knowledge and behavior based)
  • Behavioral evaluation
Each student can rank other students according to her/his preferences (knowledge needs and behavior preferences), The SPSA algorithm matches students by taking the preference lists as input and producing the stably matched pairs as output.

The Proposed Platform “Teens-Privacy”

This platform provides a collaborative environment, which allows each student to learn, to share knowledge and ideas with their partner(s).  In this platform, we propose a stable personalized partner selection algorithm (SPSA), based on game theory, which guarantees the choice of optimal partners depending on knowledge and behavior profile.

Experimental Evaluation

The SPSA algorithm was compared with three other methods: preference-based and characteristics-based homogeneous clustering method and the random-based matching method (RM). The experiment aims to study to what extent the proposed partner matching algorithm can enhance students’ satisfaction, according to multi-attributes satisfaction metric.
The 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.