De nos jours, avec l'utilisation croissante des technologies numériques, l'éducation à la préservation de la vie privée joue un rôle important en particulier pour les adolescents. Bien que plusieurs plateformes d'apprentissage en ligne à la sensibilisation à la vie privée aient été mises en œuvre, elles sont généralement basées sur des techniques traditionnelles d'apprentissage. Plus particulièrement, ces plateformes ne permettent pas aux étudiants de coopérer et de partager leurs connaissances afin d’améliorer leur apprentissage ensemble. En d'autres termes, elles manquent d'interactions élève-élève. Des recherches récentes sur les méthodes d'apprentissage montrent que la collaboration entre élèves peut entraîner de meilleurs résultats d'apprentissage par rapport à d'autres approches. De plus, le domaine de la vie privée étant fortement lié à la vie sociale des adolescents, il est préférable de fournir un environnement d'apprentissage collaboratif où l’on peut enseigner la préservation de la vie privée, et en même temps, permettre aux étudiants de partager leurs connaissances. Il serait souhaitable que ces derniers puissent interagir les uns avec les autres, résoudre des questionnaires en collaboration et discuter de problèmes et de situations de confidentialité. À cet effet, ce travail propose « Teens-online », une plateforme d'apprentissage en ligne collaborative pour la sensibilisation à la vie privée. Le programme d'études fourni dans cette plateforme est basé sur le Référentiel de formation des élèves à la protection des données personnelles. De plus, la plateforme proposée est équipée d'un mécanisme d'appariement de partenaires basé sur la théorie des jeux. Ce mécanisme garantit un appariement élève-élève stable en fonction des besoins de l'élève (comportement et / ou connaissances). Ainsi, des avantages mutuels seront obtenus en minimisant les chances de coopérer avec des pairs incompatibles. Les résultats expérimentaux montrent que l'utilité moyenne obtenue en appliquant l'algorithme proposé est beaucoup plus élevée que celle obtenue en utilisant d'autres mécanismes d'appariement. Les résultats suggèrent qu'en adoptant l'approche proposée, chaque élève peut être jumelé avec des partenaires optimaux, qui obtiennent également en retour des résultats d'apprentissage plus élevés. / 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.

Orientation: The safety of any natural being with respect to the processing of their personal information is an essential human right as specified in the Zimbabwe Data Protection Act (ZDPA) bill. Once enacted, the ZDPA bill will affect universities as public entities. It will directly impact how personal information is collected and processed. The bill will be fundamental in understanding the privacy perceptions of students in relation to privacy awareness, privacy expectations and confidence within university. These need to be understood to give guidelines to universities on the implementation of the ZPDA. Problem Statement: The current constitution and the ZDPA are not sufficient to give organisations guidelines on ensuring personal information privacy. There is need for guidelines to help organisations and institutions to implement and comply with the provisions of the ZDPA in the context of Zimbabwe. The privacy regulations, regarded as the three concepts (awareness, expectations and confidence), were used to determine the student perceptions. These three concepts have not been researched before in the privacy context and the relationship between the three concepts has not as yet been established. Research purpose: The main aim of the study was to develop and validate an Information Privacy Perception Survey (IPPS) diagnostic tool and a Student Personal Information Privacy Perception (SPIPP) model to give guidelines to universities on how they can implement the ZDPA and aid universities in comprehending student privacy perceptions to safeguard personal information and assist in giving effect to their privacy constitutional right. Research Methodology: A quantitative research method was used in a deductive research approach where a survey research strategy was applied using the IPPS instrument for data collection. The IPPS instrument was designed with 54 items that were developed from the literature. The preliminary instrument was taken through both the expert review and pilot study. Using the non-probability convenience sampling method, 287 students participated in the final survey. SPSS version 25 was used for data analysis. Both descriptive and inferential statistics were done. Exploratory factor analysis (EFA) was used to validate the instrument while confirmatory factor analysis (CFA) and the structural equation modelling (SEM) were used to validate the model. Main findings: diagnostic instrument was validated and resulted in seven new factors, namely university confidence (UC), privacy expectations (PE), individual awareness (IA), external awareness (EA), privacy awareness (PA), practice confidence (PC) and correctness expectations (CE). Students indicated that they had high expectations of the university on privacy. The new factors showed a high level of awareness of privacy and had low confidence in the university safeguarding their personal information privacy. A SPIPP empirical model was also validated using structural equation modelling (SEM) and it indicated an average overall good fit between the proposed SPIPP conceptual model and the empirically derived SPIPP model Contribution: A diagnostic instrument that measures the perceptions (privacy awareness, expectations and confidence of students) was developed and validated. This study further contributed a model for information privacy perceptions that illustrates the relationship between the three concepts (awareness, expectations and confidence). Other universities can use the model to ascertain the perceptions of students on privacy. This research also contributes to improvement in the personal information protection of students processed by universities. The results will aid university management and information regulators to implement measures to create a culture of privacy and to protect student data in line with regulatory requirements and best practice. / School of Computing / Ph. D. (Information Systems)