An identity- and trust-based computational model for privacy

The seemingly contradictory need and want of online users for information sharing and privacy has inspired this thesis work. The crux of the problem lies in the fact that a user has inadequate control over the flow (with whom information to be shared), boundary (acceptable usage), and persistence (duration of use) of their personal information. This thesis has built a privacy-preserving information sharing model using context, identity, and trust to manage the flow, boundary, and persistence of disclosed information. In this vein, privacy is viewed as context-dependent selective disclosures of information. This thesis presents the design, implementation, and analysis of a five-layer Identity and Trust based Model for Privacy (ITMP). Context, trust, and identity are the main building blocks of this model. The application layer identifies the counterparts, the purpose of communication, and the information being sought. The context layer determines the context of a communication episode through identifying the role of a partner and assessing the relationship with the partner. The trust layer combines partner and purpose information with the respective context information to determine the trustworthiness of a purpose and a partner. Given that the purpose and the partner have a known level of trustworthiness, the identity layer constructs a contextual partial identity from the user's complete identity. The presentation layer facilitates in disclosing a set of information that is a subset of the respective partial identity. It also attaches expiration (time-to-live) and usage (purpose-to-live) tags into each piece of information before disclosure. In this model, roles and relationships are used to adequately capture the notion of context to address privacy. A role is a set of activities assigned to an actor or expected of an actor to perform. For example, an actor in a learner role is expected to be involved in various learning activities, such as attending lectures, participating in a course discussion, appearing in exams, etc. A relationship involves related entities performing activities involving one another. Interactions between actors can be heavily influenced by roles. For example, in a learning-teaching relationship, both the learner and the teacher are expected to perform their respective roles. The nuances of activities warranted by each role are dictated by individual relationships. For example, two learners seeking help from an instructor are going to present themselves differently. In this model, trust is realized in two forms: trust in partners and trust of purposes. The first form of trust assesses the trustworthiness of a partner in a given context. For example, a stranger may be considered untrustworthy to be given a home phone number. The second form of trust determines the relevance or justification of a purpose for seeking data in a given context. For example, seeking/providing a social insurance number for the purpose of a membership in a student organization is inappropriate. A known and tested trustee can understandably be re-trusted or re-evaluated based on the personal experience of a trustor. In online settings, however, a software manifestation of a trusted persistent public actor, namely a guarantor, is required to help find a trustee, because we interact with a myriad of actors in a large number of contexts, often with no prior relationships. The ITMP model is instantiated as a suite of Role- and Relationship-based Identity and Reputation Management (RRIRM) features in iHelp, an e-learning environment in use at the University of Saskatchewan. This thesis presents the results of a two-phase (pilot and larger-scale) user study that illustrates the effectiveness of the RRIRM features and thus the ITMP model in enhancing privacy through identity and trust management in the iHelp Discussion Forum. This research contributes to the understanding of privacy problems along with other competing interests in the online world, as well as to the development of privacy-enhanced communications through understanding context, negotiating identity, and using trust

CIVIC LEARNING IN ONLINE COURSES: THE EXPERIENCE OF EMERGING ADULTS AT THE COMMUNITY COLLEGE

Civic learning is an important part of the mission of higher learning. The community college is a unique and integral part of the system of higher education in the United States. Digital technology has increased the options for students to take classes at a distance in a fully online format. Many of the students in online classes at the community college are in a life stage known as emerging adulthood. While there has been considerable research on each of the four topics of civic learning, the community college, emerging adulthood, and online learning, there remains a substantial gap in the literature where these topics intersect. Instructors, administrators, and designers of online courses need more information with which to best plan and deliver civic learning opportunities to emerging adults in online classes at the public community college. Qualitative research is an appropriate methodology for areas of study with little extant literature. This study used the phenomenological method in order to better understand how emerging adults enrolled in asynchronous online classes at the public community college perceive civic responsibility, civic engagement, and the experience of civic learning in their online classes. The study produced findings that highlighted the importance of respect for diversity, civil discourse, nurturing of a social learning community and instructor presence. Implications for an online pedagogy to promote civic learning are included

Personalized Approaches to Supporting the Learning Needs of Lifelong Professional Learners

Advanced learning technology research has begun to take on a complex challenge: supporting lifelong learning. Professional learning is an essential subset of lifelong learning that is more tractable than the full lifelong learning challenge. Professionals do not always have access to professional teachers to provide input to the problems they encounter, so they rely on their peers in an online learning community (OLC) to help meet their learning needs. Supporting professional learners within an OLC is a difficult problem as the learning needs of each learner continuously evolve, often in different ways from other learners. Hence, there is a need to provide personalized support to learners adapted to their individual learning needs. This thesis explores personalized approaches for detecting the unperceived learning needs and meeting the expressed learning needs of learners in an OLC. The experimental test bed for this research is Stack Overflow (SO), an OLC used by software professionals. To date, seven experiments have been carried out mining SO peer-peer interaction data. Knowing that question-answerers play a huge role in meeting the learning needs of the question-askers, the first experiment aimed to detect the learning needs of the answerers. Results from experiment 1 show that reputable answerers themselves demonstrate unperceived learning needs as revealed by a decline in quality answers in SO. Of course, a decline in quality answers could impact the help-seeking experience of question-askers; hence experiment 2 sought to understand the effects of the help-seeking experience of question-askers on their enthusiasm to continuously participate within the OLC. As expected, negative help-seeking experiences of question-askers had a large impact on their propensity to seek further help within the OLC. To improve the help-seeking experience of question-askers, it is important to proactively detect the learning needs of the question-answerers before they provide poor quality answers. Thus, in experiment 3 the goal was to predict whether a question-answerer would give a poor answer to a question based on their past peer-peer interactions. Under various assumptions, accuracies ranging from 84.57% to 94.54% were achieved. Next, experiment 4 attempted to detect the unperceived learning needs of question-askers even before they are aware of such needs. Using information about a learner’s interactions over a 5-month period, a prediction was made as to what they would be asking about during the next month, achieving recall and precision values of 0.93 and 0.81. Knowing the learning needs of question-askers early creates an opportunity to predict prospective answerers who could provide timely and quality answers to their question. The goal of experiment 5 was thus to predict the actual answerers for questions based only on information known at the time the question was asked. The iv success rate was at best 63.15%, which would only be marginally useful to inform a real-life peer recommender system. Thus, experiment 6 explored new measures in predicting the answerers, boosting the success rate to 89.64%. Of course, a peer recommender system would be deemed to be especially useful if it can provide prompt interventions, especially to get answers to questions that would otherwise not be answered quickly. To this end, experiment 7 attempted to predict the question-askers whose questions would be answered late or even remain unanswered, and a success rate of 68.4% was achieved. Results from these experiments suggest that modelling the activities of learners in an OLC is key in providing support to them to meet their learning needs. Perhaps, the most important lesson learned in this research is that lightweight approaches can be developed to help meet the evolving learning needs of professionals, even as knowledge changes within a profession. Metrics based on the experiments above are exactly such lightweight methodologies and could be the basis for useful tools to support professional learners

Web2.0, knowledge sharing and privacy in E-learning

Quand le E-learning a émergé il ya 20 ans, cela consistait simplement en un texte affiché sur un écran d'ordinateur, comme un livre. Avec les changements et les progrès dans la technologie, le E-learning a parcouru un long chemin, maintenant offrant un matériel éducatif personnalisé, interactif et riche en contenu. Aujourd'hui, le E-learning se transforme de nouveau. En effet, avec la prolifération des systèmes d'apprentissage électronique et des outils d'édition de contenu éducatif, ainsi que les normes établies, c’est devenu plus facile de partager et de réutiliser le contenu d'apprentissage. En outre, avec le passage à des méthodes d'enseignement centrées sur l'apprenant, en plus de l'effet des techniques et technologies Web2.0, les apprenants ne sont plus seulement les récipiendaires du contenu d'apprentissage, mais peuvent jouer un rôle plus actif dans l'enrichissement de ce contenu. Par ailleurs, avec la quantité d'informations que les systèmes E-learning peuvent accumuler sur les apprenants, et l'impact que cela peut avoir sur leur vie privée, des préoccupations sont soulevées afin de protéger la vie privée des apprenants. Au meilleur de nos connaissances, il n'existe pas de solutions existantes qui prennent en charge les différents problèmes soulevés par ces changements. Dans ce travail, nous abordons ces questions en présentant Cadmus, SHAREK, et le E-learning préservant la vie privée. Plus précisément, Cadmus est une plateforme web, conforme au standard IMS QTI, offrant un cadre et des outils adéquats pour permettre à des tuteurs de créer et partager des questions de tests et des examens. Plus précisément, Cadmus fournit des modules telles que EQRS (Exam Question Recommender System) pour aider les tuteurs à localiser des questions appropriées pour leur examens, ICE (Identification of Conflits in Exams) pour aider à résoudre les conflits entre les questions contenu dans un même examen, et le Topic Tree, conçu pour aider les tuteurs à mieux organiser leurs questions d'examen et à assurer facilement la couverture des différent sujets contenus dans les examens. D'autre part, SHAREK (Sharing REsources and Knowledge) fournit un cadre pour pouvoir profiter du meilleur des deux mondes : la solidité des systèmes E-learning et la flexibilité de PLE (Personal Learning Environment) tout en permettant aux apprenants d'enrichir le contenu d'apprentissage, et les aider à localiser nouvelles ressources d'apprentissage. Plus précisément, SHAREK combine un système recommandation multicritères, ainsi que des techniques et des technologies Web2.0, tels que le RSS et le web social, pour promouvoir de nouvelles ressources d'apprentissage et aider les apprenants à localiser du contenu adapté. Finalement, afin de répondre aux divers besoins de la vie privée dans le E-learning, nous proposons un cadre avec quatre niveaux de vie privée, ainsi que quatre niveaux de traçabilité. De plus, nous présentons ACES (Anonymous Credentials for E-learning Systems), un ensemble de protocoles, basés sur des techniques cryptographiques bien établies, afin d'aider les apprenants à atteindre leur niveau de vie privée désiré.E-learning emerged over 20 years ago, and was merely book like text displayed on a computer screen. With the changes and advances in technology, E-learning has come a long way, providing personal and interactive rich content. Today, E-learning is again going through major changes. Indeed, with the proliferation of E-learning systems and content authoring tools, as well as established standards, it has become easier to share and reuse learning content. Moreover, with the shift to learner centered education and the effect of Web2.0 techniques and technologies, learners are no longer just recipients of the learning content, but can play an active role into enriching such content. Additionally, with the amount of information E-learning systems can gather about learners, and the impact this has on their privacy, concerns are being raised in order to protect learners’ privacy. Nonetheless, to the best of our knowledge, there is no existing work that supports the various challenges raised by these changes. In this work, we address these issues by presenting Cadmus, SHAREK, and privacy preserving E-learning. Specifically, Cadmus is an IMS QTI compliant web based assessment authoring tool, offering the proper framework and tools to enable tutors author and share questions and exams. In detail, Cadmus provides functionalities such as the EQRS (Exam Questions Recommender System) to help tutors locate suitable questions, ICE (Identification of Conflicts in Exams) to help resolve conflicts between questions within the same exam, and the topic tree, designed to help tutors better organize their exam questions and easily ensure the content coverage of their exams. On the other hand, SHAREK (Sharing REsources and Knowledge) provides the framework to take advantage of both the rigidity of E-learning systems and the flexibility of PLEs (Personal Learning Environment) while enabling learners to enrich the learning content, and helping them locate new learning resources. Specifically, SHAREK utilizes a multi-criteria content based recommender system, and combines Web2.0 technologies and techniques such as RSS and social web to promote new learning resources and help learners locate suitable content. Lastly, in order to address the various needs for privacy in E-learning, we propose a framework with four levels of privacy, and four levels of tracking, and we detail ACES (Anonymous Credentials for E-learning Systems), a set of protocols, based on well established cryptographic techniques, to help learners achieve their desired level of privacy

Gestionnaire de vie privée : un cadre pour la protection de la vie privée dans les interactions entre apprenants

L’évolution continue des besoins d’apprentissage vers plus d’efficacité et plus de personnalisation a favorisé l’émergence de nouveaux outils et dimensions dont l’objectif est de rendre l’apprentissage accessible à tout le monde et adapté aux contextes technologiques et sociaux. Cette évolution a donné naissance à ce que l’on appelle l'apprentissage social en ligne mettant l'accent sur l’interaction entre les apprenants. La considération de l’interaction a apporté de nombreux avantages pour l’apprenant, à savoir établir des connexions, échanger des expériences personnelles et bénéficier d’une assistance lui permettant d’améliorer son apprentissage. Cependant, la quantité d'informations personnelles que les apprenants divulguent parfois lors de ces interactions, mène, à des conséquences souvent désastreuses en matière de vie privée comme la cyberintimidation, le vol d’identité, etc. Malgré les préoccupations soulevées, la vie privée en tant que droit individuel représente une situation idéale, difficilement reconnaissable dans le contexte social d’aujourd’hui. En effet, on est passé d'une conceptualisation de la vie privée comme étant un noyau des données sensibles à protéger des pénétrations extérieures à une nouvelle vision centrée sur la négociation de la divulgation de ces données. L’enjeu pour les environnements sociaux d’apprentissage consiste donc à garantir un niveau maximal d’interaction pour les apprenants tout en préservant leurs vies privées. Au meilleur de nos connaissances, la plupart des innovations dans ces environnements ont porté sur l'élaboration des techniques d’interaction, sans aucune considération pour la vie privée, un élément portant nécessaire afin de créer un environnement favorable à l’apprentissage. Dans ce travail, nous proposons un cadre de vie privée que nous avons appelé « gestionnaire de vie privée». Plus précisément, ce gestionnaire se charge de gérer la protection des données personnelles et de la vie privée de l’apprenant durant ses interactions avec ses co-apprenants. En s’appuyant sur l’idée que l’interaction permet d’accéder à l’aide en ligne, nous analysons l’interaction comme une activité cognitive impliquant des facteurs contextuels, d’autres apprenants, et des aspects socio-émotionnels. L'objectif principal de cette thèse est donc de revoir les processus d’entraide entre les apprenants en mettant en oeuvre des outils nécessaires pour trouver un compromis entre l’interaction et la protection de la vie privée. ii Ceci a été effectué selon trois niveaux : le premier étant de considérer des aspects contextuels et sociaux de l’interaction telle que la confiance entre les apprenants et les émotions qui ont initié le besoin d’interagir. Le deuxième niveau de protection consiste à estimer les risques de cette divulgation et faciliter la décision de protection de la vie privée. Le troisième niveau de protection consiste à détecter toute divulgation de données personnelles en utilisant des techniques d’apprentissage machine et d’analyse sémantique.The emergence of social tools and their integration in learning contexts has fostered interactions and collaboration among learners. The consideration of social interaction has several advantages for learners, mainly establishing new connections, sharing personal experiences and receiving assistance which may improve learning. However, the amount of personal information that learners disclose in these interactions, raise several privacy risks such as identity theft and cyberbullying which may lead to serious consequences. Despite the raised concerns, privacy as a human fundamental right is hardly recognized in today’s social context. Indeed, the conceptualization of privacy as a set of sensitive data to protect from external intrusions is no longer effective in the new social context where the risks come essentially from the self-disclosing behaviors of the learners themselves. With that in mind, the main challenge for social learning environments is to promote social interactions between learners while preserving their privacy. To the best of our knowledge, innovations in social learning environments have only focused on the integration of new social tools, without any consideration of privacy as a necessary factor to establish a favorable learning environment. In fact, integrating social interactions to maintain learners’ engagement and motivation is as necessary as preserving privacy in order to promote learning. Therefore, we propose, in this research, a privacy framework, that we called privacy manager, aiming to preserve the learners’ privacy during their interactions. Considering social interaction as a strategy to seek and request peers’ help in informal learning contexts, we analyze learners’ interaction as a cognitive activity involving contextual, social and emotional factors. Hence, our main goal is to consider all these factors in order to find a tradeoff between the advantages of interaction, mainly seeking peer feedback, and its disadvantages, particularly data disclosure and privacy risks. This was done on three levels: the first level is to help learners interact with appropriate peers, considering their learning competency and their trustworthiness. The second level of protection is to quantify potential disclosure risks and decide about data disclosure. The third level of protection is to analyze learners’ interactions in order to detect and discard any personal data disclosure using machine learning techniques and semantic analysis