Personal Learning with Social Media : Reputation, Privacy and Identity Perspectives

Social media platforms are increasingly used in recent years to support learning activities, especially for the construction of activity- and learner-centric personal learning environments (PLEs). This thesis investigates the solutions to four essential design requirements for social media based PLEs: support for help seeking, privacy protection, identity management and activity monitoring, as well as context awareness. Three main components of the thesis, reputation, privacy, and identity, are built upon these four design requirements. We investigate the three components through the following research questions. How do we help learners to find suitable experts or peers who they can learn from or collaborate with in a particular learning context? How can we design a proper privacy mechanism to make sure the information shared by learners is only disclosed to the intended audience in a given context? What identity scheme should be used to preserve the privacy of learners while also providing personalized learning experience, especially for teenage learners? To tackle the design requirement of support for help seeking, we address the reputation dimension in the context of personal learning for doctoral studies, where doctoral students need to find influential experts or peers in a particular domain. We propose an approach to detect a domain-specific community in academic social media platforms. Based on that, we investigate the influence of scholars taking both their academic and social impact into account. We propose a measure called R-Index that aggregates the readership of a scholar's publications to assess her academic impact. Furthermore, we add the social dimension into the influence measure by adopting network centrality metrics in a domain-specific community. Our results show that academic influence and social influence measures do not strongly correlate with each other, which implies that, adding the social dimension could enhance the traditional impact metrics that only take academic influence into account. Moreover, we tackle the privacy dimension of designing a PLE in the context of higher education. To protect against unauthorized access to learners' data, we propose a privacy control approach that allows learners to specify the audience, action, and artifact for their sharing behavior. Then we introduce the notion of privacy protocol with which learners can define fine-grained sharing rules. To provide a usable application of the privacy protocol in social media based PLEs, we exploit the space concept that provides an easy way for users to define the privacy protocols within a particular context. The proposed approach is evaluated through two user studies. The results reveal that learners confirm the usefulness and usability of the privacy enhanced sharing scheme based on spaces. In the last part of the thesis, we study the identity dimension in the context of STEM education at secondary and high schools. To support personalization w