Review of Research on Privacy Decision Making from a Time Perspective

Managing privacy is a process in which people continuously negotiate the boundaries of their personal space. Time is embedded in and influences this continuous negotiation. Digital technologies increasingly incorporate temporal elements, such as allowing users to define the expiration date of social network postings. Yet, researchers have not systematically examined the effects of temporal elements in privacy decision making. In this paper, we review how existing information privacy research has related to time in terms of three dimensions: duration, timing, and past, present, and future modalities. Our findings suggest that 1) duration has a negative influence on information disclosure; 2) timing, in the form of personal and external events, influences how people make privacy decisions; and 3) sensemaking that involves prior experience and planning for the future affect privacy decisions. We discuss how privacy decision making frameworks need to be adjusted to account for a time perspective

Considering temporal aspects in recommender systems: a survey

Under embargo until: 2023-07-04The widespread use of temporal aspects in user modeling indicates their importance, and their consideration showed to be highly effective in various domains related to user modeling, especially in recommender systems. Still, past and ongoing research, spread over several decades, provided multiple ad-hoc solutions, but no common understanding of the issue. There is no standardization and there is often little commonality in considering temporal aspects in different applications. This may ultimately lead to the problem that application developers define ad-hoc solutions for their problems at hand, sometimes missing or neglecting aspects that proved to be effective in similar cases. Therefore, a comprehensive survey of the consideration of temporal aspects in recommender systems is required. In this work, we provide an overview of various time-related aspects, categorize existing research, present a temporal abstraction and point to gaps that require future research. We anticipate this survey will become a reference point for researchers and practitioners alike when considering the potential application of temporal aspects in their personalized applications.acceptedVersio

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

Rumo a um modelo de usuário standard para sistemas de saúde personalizados

La definición de un modelo de usuario es el soporte de la personalización en los sistemas computacionales. Para un sistema personalizado en salud, el cual soporta la promoción de hábitos y estilos de vida saludables, en particular la actividad física y la dieta saludable; se propone un modelo de usuario de acuerdo con el estándar ISO/TR 14292 para los PHR (Personal Health Record). La descripción del modelo de usuario estandarizado incluye: una caracterización del modelo de usuario; el modelo de usuario propuesto de acuerdo con la norma ISO; las relaciones e inferencias del modelo de usuario; y una arquitectura de referencia para el sistema a desarrollar. Finalmente, se implementa un prototipo con un pequeño set de datos y se diseñan algunos posibles mockups, como soporte a la arquitectura propuesta.The definition of a user model supports personalization in computer-based systems. For a personalized system in health, which supports the promotion of healthy habits and lifestyles, in particular physical activity and healthy diet, a user model according to the ISO/TR 14292 standard for Personal Health Records [PHR] is proposed. The description of the standardized user model includes: a characterization of the user model; the proposed user model according to the ISO standard; the relationships and inferences of the user model; and reference architecture for the system to be developed. Finally, a prototype with a small dataset is implemented and some possible mockups supporting the proposed architecture are designed.A definição de um modelo de usuário é o suporte para a personalização nos sistemas informáticos. Para um sistema personalizado de saúde, que apoia a promoção de hábitos e estilos de vida saudáveis, em particular a atividade física e dieta saudável; é proposto um modelo de usuário em conformidade com a norma ISO/TR 14292 para os PHR Personal Health Record). A descrição do modelo de usuário standard inclui: uma caracterização do modelo de usuário; o modelo de usuário proposto de acordo com a norma ISO; as relações e inferências do modelo de usuário; e uma arquitetura de referência para o sistema a ser desenvolvido. Finalmente, é implementado um protótipo com um pequeno conjunto de dados e são desenhados alguns possíveis mockups, como apoio à arquitetura proposta

Personality representation: predicting behaviour for personalised learning support

The need for personalised support systems comes from the growing number of students that are being supported within institutions with shrinking resources. Over the last decade the use of computers and the Internet within education has become more predominant. This opens up a range of possibilities in regard to spreading that resource further and more effectively. Previous attempts to create automated systems such as intelligent tutoring systems and learning companions have been criticised for being pedagogically ineffective and relying on large knowledge sources which restrict their domain of application. More recent work on adaptive hypermedia has resolved some of these issues but has been criticised for the lack of support scope, focusing on learning paths and alternative content presentation. The student model used within these systems is also of limited scope and often based on learning history or learning styles.This research examines the potential of using a personality theory as the basis for a personalisation mechanism within an educational support system. The automated support system is designed to utilise a personality based profile to predict student behaviour. This prediction is then used to select the most appropriate feedback from a selection of reflective hints for students performing lab based programming activities. The rationale for the use of personality is simply that this is the concept psychologists use for identifying individual differences and similarities which are expressed in everyday behaviour. Therefore the research has investigated how these characteristics can be modelled in order to provide a fundamental understanding of the student user and thus be able to provide tailored support. As personality is used to describe individuals across many situations and behaviours, the use of such at the core of a personalisation mechanism may overcome the issues of scope experienced by previous methods.This research poses the following question: can a representation of personality be used to predict behaviour within a software system, in such a way, as to be able to personalise support?Putting forward the central claim that it is feasible to capture and represent personality within a software system for the purpose of personalising services.The research uses a mixed methods approach including a number and combination of quantitative and qualitative methods for both investigation and determining the feasibility of this approach.The main contribution of the thesis has been the development of a set of profiling models from psychological theories, which account for both individual differences and group similarities, as a means of personalising services. These are then applied to the development of a prototype system which utilises a personality based profile. The evidence from the evaluation of the developed prototype system has demonstrated an ability to predict student behaviour with limited success and personalise support.The limitations of the evaluation study and implementation difficulties suggest that the approach taken in this research is not feasible. Further research and exploration is required –particularly in the application to a subject area outside that of programming

Learners as Learning Leaders: How Does Leadership for Learning Emerge Beyond the Traditional Teaching Models?

This E-book is a collection of outcome reports by thematic working groups (TWGs) of EDUsummIT2019 held from September 29 – October 2, 2019 in Quebec City, Canada. The theme of EDUsummIT2019, “Learners and learning contexts: new alignments for the digital age” was chosen to consider misalignments due to the consequences of changing knowledge representations, human computer interactions, blurring of formal and informal learning, changes in leadership patterns and many more emerging influences from IT. Thirteen TWGs discussed aspects of the overall them

Construction of an adaptive e-learning environment to address learning styles and an investigation of the effect of media choice

This study attempted to combine the benefits of multimedia learning, adaptive interfaces, and learning style theory by constructing a novel e-learning environment. The environment was designed to accommodate individual learning styles while students progressed through a computer programming course. Despite the benefits of individualised instruction and a growing worldwide e-learning market, there is a paucity of guidance on how to effectively accommodate learning styles in an online environment. Several existing learning-style adaptive environments base their behaviour on an initial assessment of the learner's profile, which is then assumed to remain stable. Consequently, these environments rarely offer the learner choices between different versions of content. However, these choices could cater for flexible learning styles, promote cognitive flexibility, and increase learner control. The first research question underlying the project asked how learning styles could be accommodated in an adaptive e-learning environment. The second question asked whether a dynamically adaptive environment that provides the learner with a choice of media experiences is more beneficial than a statically adapted environment. To answer these questions, an adaptive e-learning environment named iWeaver was created and experimentally evaluated. iWeaver was based on an introductory course in Java programming and offered learning content as style-specific media experiences, assisted by additional learning tools. These experiences and tools were based on the perceptual and information processing dimension of an adapted version of the Dunn and Dunn learning styles model. An experimental evaluation of iWeaver was conducted with 63 multimedia students. The analysis investigated the effect of having a choice of multiple media experiences (compared to having just one static media experience) on learning gain, enjoyment, perceived progress, and motivation. In addition to these quantitative measurements, learners provided qualitative feedback at the end of each lesson. Data from 27 participants were sufficiently complete to be analysed. For the data analysis, participants were divided into two groups of high and low interest in programming and Java, then into two groups of high and low experience with computers and the Internet. Both group comparisons revealed statistically significant differences for the effect of choice. Having a choice of media experiences proved beneficial for learners with low experience but detrimental for learners with high experience or interest. These findings suggest that the effect of choice appears to be strongly influenced by the learner's background. It is hypothesised that encouraging a more active learner role in educational systems would expand the positive influence of choice to a wider range of learners. The study has contributed some weight to the argument that for certain groups of learners, it is more beneficial to view learning style as a flexible, rather than a stable construct. As a practical implication, it seems advisable to collect data on prior experience, interest, and the initial learning style distribution of the target audience before developing environments comparable to iWeaver

Modern Socio-Technical Perspectives on Privacy

This open access book provides researchers and professionals with a foundational understanding of online privacy as well as insight into the socio-technical privacy issues that are most pertinent to modern information systems, covering several modern topics (e.g., privacy in social media, IoT) and underexplored areas (e.g., privacy accessibility, privacy for vulnerable populations, cross-cultural privacy). The book is structured in four parts, which follow after an introduction to privacy on both a technical and social level: Privacy Theory and Methods covers a range of theoretical lenses through which one can view the concept of privacy. The chapters in this part relate to modern privacy phenomena, thus emphasizing its relevance to our digital, networked lives. Next, Domains covers a number of areas in which privacy concerns and implications are particularly salient, including among others social media, healthcare, smart cities, wearable IT, and trackers. The Audiences section then highlights audiences that have traditionally been ignored when creating privacy-preserving experiences: people from other (non-Western) cultures, people with accessibility needs, adolescents, and people who are underrepresented in terms of their race, class, gender or sexual identity, religion or some combination. Finally, the chapters in Moving Forward outline approaches to privacy that move beyond one-size-fits-all solutions, explore ethical considerations, and describe the regulatory landscape that governs privacy through laws and policies. Perhaps even more so than the other chapters in this book, these chapters are forward-looking by using current personalized, ethical and legal approaches as a starting point for re-conceptualizations of privacy to serve the modern technological landscape. The book’s primary goal is to inform IT students, researchers, and professionals about both the fundamentals of online privacy and the issues that are most pertinent to modern information systems. Lecturers or teacherscan assign (parts of) the book for a “professional issues” course. IT professionals may select chapters covering domains and audiences relevant to their field of work, as well as the Moving Forward chapters that cover ethical and legal aspects. Academicswho are interested in studying privacy or privacy-related topics will find a broad introduction in both technical and social aspects