2,857 research outputs found

    From Personalization to Adaptivity: Creating Immersive Visits through Interactive Digital Storytelling at the Acropolis Museum

    Get PDF
    Storytelling has recently become a popular way to guide museum visitors, replacing traditional exhibit-centric descriptions by story-centric cohesive narrations with references to the exhibits and multimedia content. This work presents the fundamental elements of the CHESS project approach, the goal of which is to provide adaptive, personalized, interactive storytelling for museum visits. We shortly present the CHESS project and its background, we detail the proposed storytelling and user models, we describe the provided functionality and we outline the main tools and mechanisms employed. Finally, we present the preliminary results of a recent evaluation study that are informing several directions for future work

    New Frontiers of Quantified Self 3: Exploring Understudied Categories of Users

    Get PDF
    Quantified Self (QS) field needs to start thinking of how situated needs may affect the use of self-tracking technologies. In this workshop we will focus on the idiosyncrasies of specific categories of users

    Devices, Information, and People: Abstracting the Internet of Things for End-User Personalization

    Get PDF
    Nowadays, end users can take advantage of end-user development platforms to personalize the Internet of Things. These platforms typically adopt a vendor-centric abstraction, by letting users to customize each of their smart device and/or online service through different trigger-action rules. Despite the popularity of such an approach, several research challenges in this domain are still underexplored. Which "things" would users personalize, and in which contexts? Are there any other effective abstractions besides the vendor-centric one? Would users adopt different abstractions in different contexts? To answer these questions, we report on the results of a 1-week-long diary study during which 24 participants noted down trigger-action rules arising during their daily activities. Results show that users would adopt multiple abstractions by personalizing devices, information, and people-related behaviors where the individual is at the center of the interaction. We found, in particular, that the adopted abstraction may depend on different factors, ranging from the user profile to the context in which the personalization is introduced. While users are inclined to personalize physical objects in the home, for example, they often go "beyond devices" in the city, where they are more interested in the underlying information. Our findings identify new design opportunities in HCI to improve the relationship between the Internet of Things, personalization paradigms, and users

    A literature synthesis of personalised technology-enhanced learning: what works and why

    Get PDF
    Personalised learning, having seen both surges and declines in popularity over the past few decades, is once again enjoying a resurgence. Examples include digital resources tailored to a particular learner’s needs, or individual feedback on a student’s assessed work. In addition, personalised technology-enhanced learning (TEL) now seems to be attracting interest from philanthropists and venture capitalists indicating a new level of enthusiasm for the area and a potential growth industry. However, these industries may be driven by profit rather than pedagogy, and hence it is vital these new developments are informed by relevant, evidence-based research. For many people, personalised learning is an ambiguous and even loaded term that promises much but does not always deliver. This paper provides an in-depth and critical review and synthesis of how personalisation has been represented in the literature since 2000, with a particular focus on TEL. We examine the reasons why personalised learning can be beneficial and examine how TEL can contribute to this. We also unpack how personalisation can contribute to more effective learning. Lastly, we examine the limitations of personalised learning and discuss the potential impacts on wider stakeholders

    The simplicity project: easing the burden of using complex and heterogeneous ICT devices and services

    Get PDF
    As of today, to exploit the variety of different "services", users need to configure each of their devices by using different procedures and need to explicitly select among heterogeneous access technologies and protocols. In addition to that, users are authenticated and charged by different means. The lack of implicit human computer interaction, context-awareness and standardisation places an enormous burden of complexity on the shoulders of the final users. The IST-Simplicity project aims at leveraging such problems by: i) automatically creating and customizing a user communication space; ii) adapting services to user terminal characteristics and to users preferences; iii) orchestrating network capabilities. The aim of this paper is to present the technical framework of the IST-Simplicity project. This paper is a thorough analysis and qualitative evaluation of the different technologies, standards and works presented in the literature related to the Simplicity system to be developed

    Factors Influencing User’s Attitude to Secondary Information Sharing and Usage

    Get PDF
    The increasing availability of enormous data about users online, along with availability of sophisticated tools and technology to store, aggregate, and analyze data for secondary use has raised concerns about how to balance the opportunity for secondary use of data with the need to protect the user privacy that may result from harmful use. To develop a privacy protection mechanism that is useful and meets the expectations and needs of the user, it is important to understand user’s attitude to privacy and secondary information sharing and usage of his/her data. While several studies have investigated factors influencing user’s attitude to privacy in primary data collection context, none of the existing studies have provided an understanding of user perception and attitude to privacy in secondary context. To fill this gap, this work has identified five factors that are important in a secondary usage context and carried out a study on their influence on user’s perception with respect to how their data is shared for secondary use. The main contribution of this paper is an understanding of factors influencing user decisions about privacy in secondary context, which can assist both technology designers and policy makers in the development of appropriate privacy protection that meets the needs and expectations of the user.</p

    A New Competence-based Approach for Personalizing MOOCs in a Mobile Collaborative and Networked Environment

    Get PDF
    Massive Open Online Courses (MOOCs) are a new disruptive development in higher education that combines openness and scalability in a most powerful way. They have the potential to widen participation in higher education. Thus, they contribute to social inclusion, the dissemination of knowledge and pedagogical innovation and also the internationalization of higher education institutions. However, one of the critical elements for a massive open language learning experience to be successful is to empower learners and to facilitate networked learning experiences. In fact, MOOCs are designed for an undefined number of participants, thus serving a high heterogeneity of profiles, with diverse learning styles and prior knowledge, and also contexts of participation and diversity of online platforms. Personalization can play a key role in this process. The iMOOC pedagogical model introduced the notion of diversity to MOOC design, allowing for a clear differentiation of learning paths and also virtual environments. In this article, the authors present a proposal based on the iMOOC approach for a new framework for personalizing and adapting MOOCs designed in a collaborative, networked pedagogical approach by identifying each participant's competence profile and prior knowledge, as well as the respective mobile communication device used to generate matching personalized learning. This article also shows the results obtained in a laboratory environment after an experiment has been performed with a prototype of the framework. It can be observed that creating personalized learning paths is possible and the next step is to test this framework with real experimental groups.Los cursos en lĂ­nea masivos y abiertos (MOOC) son una nueva tendencia rompedora en la educaciĂłn superior. Estos cursos combinan la propiedad de ser abiertos con la posibilidad de ser escalables de una forma muy potente. Tienen el potencial de permitir la participaciĂłn en la educaciĂłn superior para todas las personas, a todos los niveles. Por lo tanto, contribuyen a la inclusiĂłn social, la difusiĂłn del conocimiento y la innovaciĂłn pedagĂłgica, asĂ­ como la internalizaciĂłn de las instituciones de educaciĂłn superior. Sin embargo, uno de los elementos crĂ­ticos para que tenga Ă©xito una experiencia de aprendizaje de forma abierta y masiva es potenciar y facilitar una red de aprendizaje. De hecho, los MOOC no estĂĄn diseñados para un nĂșmero predefinido de participantes por lo que sirven para un alto nĂșmero de perfiles heterogĂ©neos, con diversidad de estilos de aprendizaje y conocimientos previos, pero tambiĂ©n contextos de participaciĂłn y diversidad de plataformas online. La personalizaciĂłn puede desempeñar un papel clave en este proceso. El modelo pedagĂłgico iMOOC introdujo el principio de diversidad en el diseño de MOOC, permitiendo una clara diferenciaciĂłn de caminos de aprendizaje y tambiĂ©n entornos virtuales. En este artĂ­culo los autores presentan una propuesta basada en el enfoque de iMOOC, sobre un nuevo sistema para la personalizaciĂłn y adaptaciĂłn de MOOC diseñados en un enfoque colaborativo y en una red pedagĂłgica. El mecanismo es identificar cada competencia del perfil de los participantes, el conocimiento previo que estos tienen asĂ­ como detectar sus respectivos dispositivos mĂłviles, y se genera un camino de aprendizaje personalizado en base a estos parĂĄmetros. Este artĂ­culo tambiĂ©n muestra los resultados obtenidos en un entorno de laboratorio despuĂ©s de un experimento llevado a cabo con un prototipo del sistema. Se puede observar que es posible crear caminos de aprendizaje personalizados y que el siguiente paso es probar este sistema con grupos experimentales reales
    • 

    corecore