1,895 research outputs found
First steps towards an integration of a Personal Learning Environment at university level
Ebner, M., Schön, S., Taraghi, B., Drachsler, H., & Tsang, P. (2011). First steps towards an integration of a Personal Learning Environment at university level. In R. Kwan et al. (Eds.), ICT 2011, CCIS 177 (pp. 22–36), Springer-Verlag Berlin: Heidelberg 2011.Personalization is seen as the key approach to handle the plethora of
information in today’s knowledge-based society. It is expected that personalized
teaching and learning will address the needs of the learners more efficiently.
The education of the future will change by the influence of Web 2.0
contents and the steadily increasing amount of data. This means that the students
of tomorrow will regularly have to deal with sharing and merging contents
from different sources. Therefore, mashup technology will become a very
important means to focus on individual learning needs and to personalize the
access to particular information. The following article describes the challenges
of Personal Learning Environments at higher education institutions. In the first
section, the concept of Personal Learning Environments is presented, while the
second section discusses the new challenges that arise for learning with the help
of Personal Learning Environments. The third section of the article describes
the technical background of Personal Learning Environments and the widget
standard in general. In section four, a first prototype of a personal learning environment
will be presented, which is integrated into the Technical University of
Graz. A detailed description of the available widgets for the prototype, along
with a first expert evaluation, will be provided. Finally, the conclusion of the
article will sum up the main points of this paper and present the plans for future
research together with the prospective developments.NeLLL AlterEg
Privacy, security, and trust issues in smart environments
Recent advances in networking, handheld computing and sensor technologies have driven forward research towards the realisation of Mark Weiser's dream of calm and ubiquitous computing (variously called pervasive computing, ambient computing, active spaces, the disappearing computer or context-aware computing). In turn, this has led to the emergence of smart environments as one significant facet of research in this domain. A smart environment, or space, is a region of the real world that is extensively equipped with sensors, actuators and computing components [1]. In effect the smart space becomes a part of a larger information system: with all actions within the space potentially affecting the underlying computer applications, which may themselves affect the space through the actuators. Such smart environments have tremendous potential within many application areas to improve the utility of a space. Consider the potential offered by a smart environment that prolongs the time an elderly or infirm person can live an independent life or the potential offered by a smart environment that supports vicarious learning
Walking the Middle Path: How Medium Trade-Off Exposure Leads to Higher Consumer Satisfaction in Recommender Agents
Recommender Agents (RAs) facilitate consumers’ online purchase decisions for complex, multi-attribute products. As not all combinations of attribute levels can be obtained, users are forced into trade-offs. The exposure of trade-offs in a RA has been found to affect consumers’ perceptions. However, little is known about how different preference elicitation methods in RAs affect consumers by varying degrees of trade-off exposure. We propose a research model that investigates how different levels of trade-off exposure cognitively and affectively influence consumers’ satisfaction with RAs. We operationalize these levels in three different RA types and test our hypotheses in a laboratory experiment with 116 participants. Our results indicate that with increasing tradeoff exposure, perceived enjoyment and perceived control follow an inverted U-shaped relationship. Hence, RAs using preference elicitation methods with medium trade-off exposure yield highest consumer satisfaction. This contributes to the understanding of trade-offs in RAs and provides valuable implications toe-commerce practitioners
On the selection and analysis of software product line implementation components using intelligent techniques
En los últimos años y con el creciente avance tecnológico, las empresas
ya no se centran exclusivamente en diseñar un producto para un cliente (por
ejemplo, el diseño de un sitio web para el Hotel Decameron), sino en producir
para un dominio (por ejemplo, el diseño de sitios web para hoteles); es decir,
el diseño de un producto que pueda adaptarse fácilmente a las diferentes variaciones
que puedan existir para un mismo producto y que se adapte a los
gustos individuales de los clientes.
En la ingeniería de software, esto puede lograrse a través de la gestión de
líneas de productos de software (SPL). Una SPL se define como un conjunto de
sistemas que comparten un conjunto común de características que satisfacen
la demanda de un mercado específico. Una SPL intenta reducir el esfuerzo y
el costo de implementar y mantener en el tiempo un conjunto de productos de
software similares; sin embargo, manejar la variabilidad en estos sistemas es
una tarea dif´ıcil, a mayor n´umero de productos m´as complejo se hace manejarlos.
Los modelos de caracter´ısticas (FMs) se emplean para representar gr´aficamente
las partes comunes y variables de una SPL. Dada la gran cantidad de
caracter´ısticas que se pueden derivar de un modelo de caracter´ıstica (FM), resulta
dif´ıcil de gestionarlos. Para hacer frente a estos problemas se ha propuesto
el An´alisis Autom´atico de Modelos de Caracter´ısticas (AAFM) que mediante
el uso de herramientas asistidas por ordenador, se ocupa de la extracci ´on
de información de los modelos de características. No obstante, existen ciertos
escenarios en los que la configuración de un producto se convierte en una
actividad compleja dado el número de componentes que existen para implementar
una determinada característica.
En esta tesis, exploramos técnicas inteligentes para resolver dos problemas
que surgen al manejar una SPL:
i. Por un lado, hemos identificado los problemas que surgen cuando un
desarrollador desea mantener sus aplicaciones al d´ıa con los últimos
avances tecnol´ogicos. La estrecha relaci ´on entre las caracter´ısticas de
aplicaci ´on y los componentes de plataforma es dif´ıcil de rastrear. Los desarrolladores deben ser conscientes de las consecuencias que podr´ıan
traer a las aplicaciones existentes cuando cambia el hardware donde
se va a ejecutar; por ejemplo, cuando una aplicaci ´on se traslada de un
smartphone a una computadora/tablet, o cuando una plataforma se actualiza
a una nueva versi´on. Los diferentes tama˜nos y resoluciones de
pantalla, la posible ausencia de un radio celular o el aumento de la cantidad
de memoria pueden tener impactos positivos o negativos en una
aplicaci ón. En este contexto, dado que las caracter´ısticas de aplicaci ´on y
de plataforma están conceptualmente separadas, sus caracter´ısticas pueden
modelarse en dos modelos distintos. Por consiguiente, manejar la
trazabilidad entre estas dos capas y c´omo los posibles cambios en ciertas
caracter´ısticas puedan afectar a la otra capa, es un problema que est´a por
resolver.
ii. Por otro lado, hemos encontrado lo complicado que es para el desarrollador
de aplicaciones configurar un producto cuando hay una variedad
de componentes de implementación para cada característica. Por ejemplo,
un desarrollador web en WordPress busca manualmente aquellos
componentes (plugins) que son factibles y más óptimos para cada sitio
web. Esta tarea lleva tiempo y no siempre garantiza que los componentes
seleccionados sean los m´as adecuados (en términos de calidad) para
la aplicación requerida. Dos escenarios podrían surgir durante esta
configuraci´on: primero, la selecci ´on emp´ırica de un componente, en la
pr´actica, puede no proporcionar los resultados esperados; adem´as, no
tener criterios basados en la experiencia de otros usuarios con respecto a
estos componentes, podr´ıa inducir una mala selecci ´on y lograr una mala
experiencia para el usuario final. En este contexto, el manejo de la relaci
´on entre los componentes de implementaci´on y sus caracter´ısticas es
otro problema a resolver.
Concretamente, las contribuciones de esta tesis se detallan a continuaci´on;
Modelos de caracter´ısticas en m´ ultiples capas: En esta ´area introducimos un
framework para el an´alisis de modelos de caracter´ısticas de m´ ultiples
capas, llamado MAYA. Los objetivos que perseguimos con esta soluci´on
son: i) modelar la variabilidad de los sistemas software en dos capas, incluyendo
sus respectivas interdependencias; ii) definir un conjunto de
operaciones que puedan imponerse a dichos modelos; iii) una implementaci
´on de referencia para el an´alisis de m´ ultiples capas basado en un
caso de estudio en Android, y finalmente; iv) dos evaluaciones emp´ıricas
que demuestran la viabilidad de nuestra propuesta en la pr´actica.
Componentes de implementaci´on: La configuraci´on de un producto es una de las actividades m´as propensas a errores, m´as a ´un cuando para cada
caracter´ıstica hay m´as de un componente que la implemente. Para
gestionar estas configuraciones, introducimos un sistema de recomendaci
´on basado en componentes llamado RESDEC que facilita la selecci ´on
de componentes de implementaci´on al crear productos en una SPL. Concretamente
las contribuciones que se presentan con esta propuesta son:
i) modelado del problema de selecci ´on de componentes de implementaci
´on como una tarea de recomendaci´on utilizando algoritmos de filtrado
colaborativo y por contenido; ii) dise ˜no de un prototipo de herramienta
de sistema de recomendaci´on basada en componentes lista para ser
utilizada y extendida a otros entornos a partir de la selecci ´on de componentes
de implementaci´on y, finalmente; iii) una evaluaci´on emp´ırica
basado en sitios web de comercio electr ´onico enWordPress
Automating Software Customization via Crowdsourcing using Association Rule Mining and Markov Decision Processes
As systems grow in size and complexity so do their configuration possibilities. Users of modern systems are easy to be confused and overwhelmed by the amount of choices they need to make in order to fit their systems to their exact needs. In this thesis, we propose a technique to select what information to elicit from the user so that the system can recommend the maximum number of personalized configuration items. Our method is based on constructing configuration elicitation dialogs through utilizing crowd wisdom.
A set of configuration preferences in form of association rules is first mined from a crowd configuration data set. Possible configuration elicitation dialogs are then modeled through a Markov Decision Processes (MDPs). Within the model, association rules are used to automatically infer configuration decisions based on knowledge already elicited earlier in the dialog. This way, an MDP solver can search for elicitation strategies which maximize the expected amount of automated decisions, reducing thereby elicitation effort and increasing user confidence of the result. We conclude by reporting results of a case study in which this method is applied to the privacy configuration of Facebook
Algorithmic Collusion or Competition: the Role of Platforms' Recommender Systems
Recent academic research has extensively examined algorithmic collusion
resulting from the utilization of artificial intelligence (AI)-based dynamic
pricing algorithms. Nevertheless, e-commerce platforms employ recommendation
algorithms to allocate exposure to various products, and this important aspect
has been largely overlooked in previous studies on algorithmic collusion. Our
study bridges this important gap in the literature and examines how
recommendation algorithms can determine the competitive or collusive dynamics
of AI-based pricing algorithms. Specifically, two commonly deployed
recommendation algorithms are examined: (i) a recommender system that aims to
maximize the sellers' total profit (profit-based recommender system) and (ii) a
recommender system that aims to maximize the demand for products sold on the
platform (demand-based recommender system). We construct a repeated game
framework that incorporates both pricing algorithms adopted by sellers and the
platform's recommender system. Subsequently, we conduct experiments to observe
price dynamics and ascertain the final equilibrium. Experimental results reveal
that a profit-based recommender system intensifies algorithmic collusion among
sellers due to its congruence with sellers' profit-maximizing objectives.
Conversely, a demand-based recommender system fosters price competition among
sellers and results in a lower price, owing to its misalignment with sellers'
goals. Extended analyses suggest the robustness of our findings in various
market scenarios. Overall, we highlight the importance of platforms'
recommender systems in delineating the competitive structure of the digital
marketplace, providing important insights for market participants and
corresponding policymakers.Comment: 33 pages, 5 figures, 4 table
Towards Designing Robo-Advisory to Promote Consensus Efficient Group Decision-Making in New Types of Economic Scenarios
Robo-advisors are a new type of FinTech increasingly used by millennials in place of traditional financial advice. Building on artificial intelligence, robo-advisors provide personalized asset and wealth management services. Their application and study have hitherto focused exclusively on individual advisory regarding asset management. We observe a pressing need to investigate robo- advisors’ application for complex artificial intelligence based recommendation tasks both, in context of group decision-making and in contexts beyond asset management, due to robo-advisors’ potential as a lever for integrating artificial intelligence in the entire decision-making process. Thus, we present a action design research in progress aimed at designing such a robo-advisor. More specifically, this study investigates whether and how robo-advisory promotes consensus-efficient group decision-making in new types of economic scenarios (after-sales). Based on a comprehensive problem formulation, we aim towards deriving a set of meta-requirements and design principles that are embodied in a preliminary prototypical instantiation of a robo-advisor
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