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

Active support for instructors and students in an online learning environment

By opening the learner model to both the learner and other peers within an e-learning system, the learner gains control over his or her learner model and is able to reflect on the contents presented in the model. Many current modeling systems translate an existing model to fit the context when information is needed. This thesis explores the observation that information in the model depends on the context in which it is generated and describes a method of generating the model for the specific user and purpose. The main advantage of this approach is that exactly the right information is generated to suit the context and needs of the learner. To explore the benefits and possible downsides of this approach, a learner model Query Tool was implemented to give instructors and learners the opportunity to ask specific questions (queries) of the content delivery system hosting several online courses. Information is computed in real time when the query is run by the instructor, so the data is always up-to-date. Instructors may then choose to allow students to run the query as well, enabling learner reflection on their progress in the course as the instructor has defined it. I have called this process active open learner modelling, referring to the open learner modelling community where learner models are accessible by learners for reflective purposes, and referring to the active learner modelling community which describes learner modelling as a context-driven process. Specific research questions explored in this thesis include "how does context affect the modelling process when learner models are opened to users", "how can privacy be maintained while useful information is provided", and "can an accurate and useful learner model be computed actively"

Degrees of Freedom: Expanding College Opportunities - for Currently and Formerly Incarcerated Californians

This report begins with a background on the higher education and criminal justice systems in California. This background section highlights the vocabulary and common pathways for each system, and provides a primer on California community colleges. Part II explains why California needs this initiative. Part III presents the landscape of existing college programs dedicated to criminal justice-involved populations in the community and in jails and prisons. This landscape identifies promising strategies and sites of innovation across the state, as well as current challenges to sustaining and expanding these programs. Part IV lays out concrete recommendations California should take to realize the vision of expanding high-quality college opportunities for currently and formerly incarcerated individuals. It includes guidelines for developing high-quality, sustainable programs, building and strengthening partnerships, and shaping the policy landscape, both by using existing opportunities and by advocating for specific legislative and policy changes. Profiles of current college students and graduates with criminal records divide the sections and offer first-hand accounts of the joys and challenges of a college experience

Collaborative tagging : folksonomy, metadata, visualization, e-learning, thesis

Collaborative tagging is a simple and effective method for organizing and sharing web resources using human created metadata. It has arisen out of the need for an efficient method of personal organization, as the number of digital resources in everyday lives increases. While tagging has become a proven organization scheme through its popularity and widespread use on the Web, little is known about its implications and how it may effectively be applied in different situations. This is due to the fact that tagging has evolved through several iterations of use on social software websites, rather than through a scientific or an engineering design process. The research presented in this thesis, through investigations in the domain of e-learning, seeks to understand more about the scientific nature of collaborative tagging through a number of human subject studies. While broad in scope, touching on issues in human computer interaction, knowledge representation, Web system architecture, e-learning, metadata, and information visualization, this thesis focuses on how collaborative tagging can supplement the growing metadata requirements of e-learning. I conclude by looking at how the findings may be used in future research, through using information based in the emergent social networks of social software, to automatically adapt to the needs of individual users

A data-assisted approach to supporting instructional interventions in technology enhanced learning environments

The design of intelligent learning environments requires significant up-front resources and expertise. These environments generally maintain complex and comprehensive knowledge bases describing pedagogical approaches, learner traits, and content models. This has limited the influence of these technologies in higher education, which instead largely uses learning content management systems in order to deliver non-classroom instruction to learners. This dissertation puts forth a data-assisted approach to embedding intelligence within learning environments. In this approach, instructional experts are provided with summaries of the activities of learners who interact with technology enhanced learning tools. These experts, which may include instructors, instructional designers, educational technologists, and others, use this data to gain insight into the activities of their learners. These insights lead experts to form instructional interventions which can be used to enhance the learning experience. The novel aspect of this approach is that the actions of the intelligent learning environment are now not just those of the learners and software constructs, but also those of the educational experts who may be supporting the learning process. The kinds of insights and interventions that come from application of the data-assisted approach vary with the domain being taught, the epistemology and pedagogical techniques being employed, and the particulars of the cohort being instructed. In this dissertation, three investigations using the data-assisted approach are described. The first of these demonstrates the effects of making available to instructors novel sociogram-based visualizations of online asynchronous discourse. By making instructors aware of the discussion habits of both themselves and learners, the instructors are better able to measure the effect of their teaching practice. This enables them to change their activities in response to the social networks that form between their learners, allowing them to react to deficiencies in the learning environment. Through these visualizations it is demonstrated that instructors can effectively change their pedagogy based on seeing data of their students’ interactions. The second investigation described in this dissertation is the application of unsupervised machine learning to the viewing habits of learners using lecture capture facilities. By clustering learners into groups based on behaviour and correlating groups with academic outcome, a model of positive learning activity can be described. This is particularly useful for instructional designers who are evaluating the role of learning technologies in programs as it contextualizes how technologies enable success in learners. Through this investigation it is demonstrated that the viewership data of learners can be used to assist designers in building higher level models of learning that can be used for evaluating the use of specific tools in blended learning situations. Finally, the results of applying supervised machine learning to the indexing of lecture video is described. Usage data collected from software is increasingly being used by software engineers to make technologies that are more customizable and adaptable. In this dissertation, it is demonstrated that supervised machine learning can provide human-like indexing of lecture videos that is more accurate than current techniques. Further, these indices can be customized for groups of learners, increasing the level of personalization in the learning environment. This investigation demonstrates that the data-assisted approach can also be used by application developers who are building software features for personalization into intelligent learning environments. Through this work, it is shown that a data-assisted approach to supporting instructional interventions in technology enhanced learning environments is both possible and can positively impact the teaching and learning process. By making available to instructional experts the online activities of learners, experts can better understand and react to patterns of use that develop, making for a more effective and personalized learning environment. This approach differs from traditional methods of building intelligent learning environments, which apply learning theories a priori to instructional design, and do not leverage the in situ data collected about learners

Good collaborations: A case study of the Health Information Technology partnership

The Health Information Technology grant was a collaborative partnership between the Cook Inlet Tribal Council (CITC), the University of Alaska Community & Technical College (UAA CTC) and the University of Alaska Southeast (UAS) to establish the infrastructure for a distance-delivered Occupational Endorsement in Health Information Technology. This document describes a case study research project that explored the activities of the collaboration, specifically as they pertain to student services and outcomes. Student eligibility criteria included: Alaska Native, low-income, GED or high school diploma, and a 10th grade TABE test score; many of the student participants exhibited demographic characteristics that placed them at high risk for noncompletion. Ultimately, 10 of 25 (40%) completed the credential, and of these graduates, five are continuing their postsecondary studies for an associate’s or bachelor’s degree. These success rates that exceed national averages for community college students prompted the team to explore the program elements that contributed to student success. A qualitative case study collected interview data from student completers, program staff, and faculty. It also reviewed program documents, and included visits to the physical spaces where the program was delivered. Tangible or material resources that contributed to the program’s success included stipends for student tuition and fees plus hourly compensation for time spent in class; the provision of laptops; adequate technology; staff and services that supported college transitions, social and personal needs, and academic success; a face-to-face kickoff event; and a cohort model. Qualitative aspects of the program that fostered success include staff commitment and positive attitude; clear roles for partners with a distributed workload; alignment of program objectives to each of the partners’ missions; communication; and student perseverance. Program elements that need to be revised, expanded, or improved prior to a second iteration include course sequencing, recruitment, technology, class times, and additional stipends. Opportunities for additional programming include industry involvement, career exploration, options for students who “change majors” or decide that the HIT field is not a good fit for their interests, job seeking and career planning support, additional attention to college readiness and soft skills, and incorporation of Alaska Native culture. A review of program elements that worked and need improvement identified opportunities to better align theory and philosophy, and to strengthen communication between staff and faculty who have complementary responsibilities to one another and to students. These discussions are recommended in order to develop more intentional and focused recruiting, to strengthen communication, and to develop a more culturally responsive curriculum. Though the program does not yet present itself as a best practice model, the program strengths and lessons learned were used to develop considerations for other programs and partnerships wishing to develop similar delivery methods.Office of Vocational and Adult Education Community &Technical College, University of Alaska AnchorageIntroduction / Health Information Technology / The credential / Employment landscape / Partners / Structure / Timeline & Schedule / Student Cohort / Outcomes / Method, participation, and analysis / Findings / What worked: The tangibles / What worked: the intangibles / What didn't work / Opportunities / Discussion / Philosophical -Student success / Theoretical: frameworks / Recommendations / Recruitment / Communication / Curriculum / Replication / Conclusion / References / Appendix: Considerations for replicatio

Social network analysis for technology-enhanced learning: review and future directions

Sie, R. L. L., Ullmann, T. D., Rajagopal, K., Cela, K., Bitter-Rijpkema, M., & Sloep, P. B. (2012). Social network analysis for technology-enhanced learning: review and future directions. International Journal of Technology Enhanced Learning, 4(3/4), 172-190.By nature, learning is social. The interactions by which we learn from others inherently form a network of relationships among people, but also between people and resources. This paper gives an overview of the potential social network analysis (SNA) may have for social learning. It starts with an overview of the history of social learning and how SNA may be of value. The core of the paper outlines the state-of-art of SNA for technology-enhanced learning (TEL), by means of four possible types of SNA applications: visualisation, analysis, simulation, and interventions. In an outlook, future directions of SNA research for TEL are provided

The State of Higher Education in California: Black Report

This report examines how the California's 2.16 million Black residents (6 percent of total population) are faring in higher education attainment compared with other racial/ethnic groups. While Black Californians have improved their education outcomes over the last couple of decades, they still experience significant opporunity gaps. The report calls for a concerted, strategic effort to produce better educational outcomes for Black students including a new statewide plan for California higher education, a redesign of pre-college level courses, re-enrollment of adults with some college but no degree, and allowing public universities to use race/ethnicity as one of many considerations in their admissions process

weHelp: A Reference Architecture for Social Recommender Systems

Recommender systems have become increasingly popular. Most of the research on recommender systems has focused on recommendation algorithms. There has been relatively little research, however, in the area of generalized system architectures for recommendation systems. In this paper, we introduce weHelp: a reference architecture for social recommender systems — systems where recommendations are derived automatically from the aggregate of logged activities conducted by the system's users. Our architecture is designed to be application and domain agnostic. We feel that a good reference architecture will make designing a recommendation system easier; in particular, weHelp aims to provide a practical design template to help developers design their own well-modularized systems