Hildesheim, Germany

University of Hildesheim

Hildesheim, Germany

The University of Hildesheim was founded in 1978. Wikipedia.

Time filter
Source Type

Piekny J.,University of Hildesheim | Maehler C.,University of Hildesheim
British Journal of Developmental Psychology | Year: 2013

According to Klahr's (2000, 2005; Klahr & Dunbar, 1988) Scientific Discovery as Dual Search model, inquiry processes require three cognitive components: hypothesis generation, experimentation, and evidence evaluation. The aim of the present study was to investigate (a) when the ability to evaluate perfect covariation, imperfect covariation, and non-covariation evidence emerges, (b) when experimentation emerges, (c) when hypothesis generation skills emerge, and (d), whether these abilities develop synchronously during childhood. We administered three scientific reasoning tasks referring to the three components to 223 children of five age groups (from age 4.0 to 13.5 years). Our results show that the three cognitive components of domain-general scientific reasoning emerge asynchronously. The development of domain-general scientific reasoning begins with the ability to handle unambiguous data, progresses to the interpretation of ambiguous data, and leads to a flexible adaptation of hypotheses according to the sufficiency of evidence. When children understand the relation between the level of ambiguity of evidence and the level of confidence in hypotheses, the ability to differentiate conclusive from inconclusive experiments accompanies this development. Implications of these results for designing science education concepts for young children are briefly discussed. © 2012 The British Psychological Society.

Agency: European Commission | Branch: FP7 | Program: CP | Phase: ICT-2013.4.2 | Award Amount: 8.48M | Year: 2014

Miniaturisation, progress with energy issues and cost reductions have resulted in rapid growth in deployment of networked devices and sensors, very strongly connecting the internet with the physical world. With wide adoption of smartphones and social media, also people have become key sources of information about the physical world, corresponding events and the intents and plans of many individuals. With more than a billion of people organizing their lives electronically and sharing information via social platforms on the Internet and with the number of devices connected to the Internet already exceeding the number of people on earth and still growing to an estimated 50 billion devices by 2020, handling these massive amounts of data becomes a huge challenge. Surmounting this challenge, however, may give us previously unattainable understanding of events and changes in our surrounding environments. EPPICS will develop large scale adaptive methods to enable pervasive modelling, monitoring and predicting of events in the real world by extracting and combining data and information from physical and social sensors. Such methods will be integrated into a platform that will support citizens, authorities and organizations in taking informed and timely decisions when tackling real world events. Application domains will cover the intelligent management in urban settings with a particular focus on city-wide events management as well as water management, specifically monitoring and reacting to widespread floods. EPPICS will provide the technological and methodological framework for the capturing, integrating, modeling and forecasting of the large-scale hybrid information deriving from hundreds of sensors, thousands of cars and large-scale social media. The technology will enable the authorities a huge leap in terms of the ability to manage large events where hundreds of thousands of people are involved at the same time.

Agency: European Commission | Branch: FP7 | Program: CP | Phase: ICT-2011.8.1 | Award Amount: 3.06M | Year: 2012

In the aftermath of the PISA studies, which identified weaknesses of students in many European countries, especially in mathematics, the education of children in the elementary school grades has received a lot of attention. Yet, most learning systems that have been developed for mathematics education have two significant limitations: first, they are usually constrained to text-based interactions and are thus hard to use by young learners (6 to 11-year-olds) who are still perfecting their basic literacy skills. Second, support is rarely tailored to the childrens needs in an adaptive fashion, even though depending on the current stage of the learning process, the support that children need varies between structured practice and more exploratory, conceptually-oriented learning.\n\nThe Intelligent Tutoring and Exploration for Robust Learning project aims to facilitate robust learning by creating a platform for intelligent support that combines structured learning with exploratory learning activities and applies cognitive models of the learning behaviour of students in elementary education. Relying on state-of-the-art machine learning methods, intelligent components will be able to provide adaptive feedback -- e.g., praise or hints --and suggest subsequent tasks. The platform will enable learners to communicate and interact more naturally via rich intuitive user interfaces leveraging direct manipulation and, in particular, natural language user interfaces. The pedagogical and technological outcomes of the project will be evaluated in two proven application scenarios in two European languages.\n\nThe project proposes to perform interdisciplinary, cutting-edge research in a multidisciplinary team with members from fields as diverse as artificial intelligence/machine learning, user modelling, intelligent tutoring systems, and natural language processing, as well as educational psychology and mathematics education.

Agency: European Commission | Branch: FP7 | Program: CP | Phase: ICT-2011.6.6 | Award Amount: 3.97M | Year: 2011

Reduction of CO2 emissions is the great challenge of the transport sector nowadays. Despite progress in vehicle manufacturing and fuel technology, additional innovative technologies are needed to address this challenge. According to the Int. Assoc. of Public Transport, a significant fraction of CO2 emissions in EU cities is resulting from public transport and other mass transport means, which are commonly organized into multi-modal transport fleets, because their vehicles have, on average, nearly substantial mileage and fuel consumption.The REDUCTION project focuses on advanced ICT solutions for managing multi-modal fleets and reducing their environmental footprint. REDUCTION collects historic and real-time data about driving behaviour, routing information, and emissions measurements, that are processed by advanced predictive analytics to enable fleets enhancing their current services as follows: 1) Optimizing driving behaviour: supporting effective decision making for the enhancement of drivers education and the formation of effective policies about optimal traffic operations (speeding, braking, etc.), based on the analytical results over the data that associate driving-behaviour patterns with CO2 emissions. 2) Eco-routing: suggesting environmental-friendly routes and allowing multi-modal fleets to reduce their overall mileage automatically. 3) Support for multi-modality: offering a transparent way to support multiple transportation modes and enabling co-modality.REDUCTION follows an interdisciplinary approach and brings together expertise from several communities. Its innovative, decentralized architecture allows scalability to large fleets by combining both V2V and V2I approaches. Its planned commercial exploitation, based on its proposed cutting-edge technology, aims at providing a major breakthrough in the fast growing market of services for green fleets in EU and worldwide, and present substantial impact to the challenging environmental goals of EU.

Agency: European Commission | Branch: FP7 | Program: CP | Phase: ICT-2013.4.2 | Award Amount: 3.75M | Year: 2014

The growing number of fine-granular data streams opens up new opportunities for improved risk analysis, situation and evolution monitoring as well as event detection. However, there are still some major roadblocks for leveraging the full potential of data stream processing, as it would, for example, be needed the highly relevant systemic risk analysis in the financial domain.The QualiMaster project will address those road blocks by developing novel approaches for autonomously dealing with load and need changes in large-scale data stream processing, while opportunistically exploiting the available resources for increasing analysis depth whenever possible. For this purpose, the QualiMaster infrastructure will enable autonomous proactive, reflective and cross-pipeline adaptation, in addition to the more traditional reactive adaptation. Starting from configurable stream processing pipelines, adaptation will be based on quality-aware component description, pipelines optimization and the systematic exploitation of families of approximate algorithms with different quality/performance tradeoffs. However, adaptation will not be restricted to the software level alone: We will go a level further by investigating the systematic translation of stream processing algorithms into code for reconfigurable hardware and the synergistic exploitation of such hardware-based processing in adaptive high performance large-scale data processing. The project focuses on financial analysis based on combining financial data streams and social web data, especially for systemic risk analysis. Our user-driven approach involves two SMEs from the financial sector. Rigorous evaluation with real world data loads from the financial domain enriched with relevant social Web content will further stress the applicability of QualiMaster results.

Agency: European Commission | Branch: FP7 | Program: CP | Phase: ICT-2009.1.2 | Award Amount: 6.07M | Year: 2010

Service-oriented computing has attracted significant attention over recent years. While this innovative approach provides enormous potential for software development in an open, networked environment, the very different constraints and quality concerns in different domains have so far led to a heterogeneous landscape of service platforms.\nWhile a single integrated platform would be highly desirable, the domain-specific constraints in areas like factory automation, business information systems or telecommunication are much too diverse, leading to the need for specialized, domain-specific service platforms. Already now a plethora of different service platforms are available. This fragmentation, however, effectively slows the emergence of a full service ecosystem. In order to fulfill the vision of an integrated platform ecosystem, the various platforms must be made interoperable.\n\nThe INDENICA project will address these challenges in an explicit and integrated way. It will provide a development method, infrastructure components and tools that support the efficient derivation of specialized, domain-specific service platforms. By deriving these platforms from common infrastructures, we will combine optimal adaptation of the service platform to domain-specific constraints with easy and fast development. At the same time, the resulting platforms will be interoperable by design. This allows integrating arbitrary INDENICA service platforms at any time into a virtual domain-specific service platform. The integrated platforms will act from an application point of view as a single platform, enabling transparent multi-platform deployment and comprehensive QoS management. On the technical side INDENICA will use Product Line Techniques (compositional and generative techniques) to provide an efficient approach for the derivation of the domain-specific platforms.\n\nINDENICA results will be systematically validated using an integrated multi-domain use case. This use case integrates components from the relevant areas of the industrial partners and provides a demonstration for the strong integration capabilities of INDENICA platforms.

Agency: European Commission | Branch: FP7 | Program: CP-FP | Phase: HEALTH-2009-3.3-2 | Award Amount: 2.03M | Year: 2010

The consumption of alcohol among young people in Europe is rising the last years. Several studies indicate that one quarter to one third of all the students has been drinking alcohol during the last month. Also the problematic drinking is a growing issue. Especially in the age group 12 until 14 year the use of alcohol has increased the last decade. The proposed multilevel research project aims to study the different possible effective strategies for the prevention of alcohol abuse among adolescents in different European countries. It will therefore analyse existing environmental strategies of public and private actors at different governance levels and confront these with outcomes of a study to identify and analyse risk factors that influence the initiation of alcohol use among young people in Europe. The study will build upon a unique dataset from a previous survey of self reported delinquency among 74,000 young people in 33 countries, realised with active involvement of the same research consortium. The (intermediate) outcomes of the study will be largely disseminated through experts and stakeholders conferences in different European regions and a web-based prevention policy guidance book

Agency: European Commission | Branch: H2020 | Program: RIA | Phase: YOUNG-2-2014 | Award Amount: 2.50M | Year: 2015

The overall ambition of MOVE is to provide a research-informed contribution towards an improvement of the conditions of the mobility of young people in Europe and a reduction of the negative impacts of mobility through the identification of ways of good practice thus fostering sustainable development and wellbeing. The consortium of MOVE is built up of nine partners within six countries: Luxembourg, Germany, Hungary, Norway, Romania and Spain. The main research question is: How can the mobility of young people be good both for socio-economic development and for individual development of young people, and what are the factors that foster/hinder such beneficial mobility? Based on an interdisciplinary and multilevel research approach the main objectives of MOVE are to: [1] carry out a comprehensive analysis of the phenomenon of mobility of young people in the EU; [2] generate systematic data about young peoples mobility patterns in Europe based on qualitative case studies, a mobility survey and on secondary data analysis; [3] provide a quantitative integrated database on European youth mobility; [4] offer a data based theoretical framework in which mobility can be reflected, thus contributing to the scientific and political debates. [5] explore factors that foster and factors that hinder good practice based on an integrative approach with qualitative and quantitative evidence. [6] provide evidence-based knowledge and recommendations for policy makers through the development of good-practice models. MOVE is based on a multilevel research design, including case studies on six types of mobility (higher education, voluntary work, employment, vocational training, pupils exchange and entrepreneurship), a survey (N=6400) and secondary data analysis, taking into consideration social inequality (e.g. migration background, gender, educational inequalities, impairments). The focus will be on the regional contexts of mobility and the agency of young people.

Agency: European Commission | Branch: FP7 | Program: CP | Phase: ICT-2007.1.5 | Award Amount: 4.36M | Year: 2008

MyMedia addresses the key social problem of what has been called the Crisis of Choice, the problem of information overload. We want to increase the level of relevant content over the noise. The problem worsens year-by-year as more and more content, including self-created content, becomes available online as well as through traditional broadcast means (delivered through satellite, over-the-air, IPTV, and cable). This is not a new problem yet it still has not been solved in a way that satisfies end-users. We will address this problem by creating an open source software framework to dynamically personalize the delivery and consumption of multimedia. MyMedia will tame growing volume of content streams by combining them and allow users to sip from a single manageable stream of the most personally relevant content.\nThe project will pioneer the integration of multiple, content catalogues and recommender algorithms in a single system. The project delivers an open source software framework which allows researchers and potential commercial exploiters outside of the consortium to easily plug-in and experiment with new recommender algorithms and content sources. This simplifies take-up outside the consortium and creates an even wider impact. The project will evaluate resulting theoretical user models through a set of lab analysis and field trials. The framework will also be evaluated on multiple trial platforms and will be language agnostic. To understand cultural differences field trials will be conducted in multiple countries and languages. MyMedia will innovate by enabling the creation of media-centric social networks that leverage user generated metadata such as tags and explore other possibilities focused on improving the end user experience such as automatically generated content metadata enrichment. It will pave the way to sharing preferences and recommendation results, in a privacy respecting manner, within communities.

Nanopoulos A.,University of Hildesheim
IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans | Year: 2011

Along with the new opportunities introduced by Web 2.0 and collaborative tagging systems, several challenges have to be addressed too, notably, the problem of information overload. Recommender systems are among the most successful approaches for increasing the level of relevant content over the noise. Traditional recommender systems fail to address the requirements presented in collaborative tagging systems. This paper considers the problem of item recommendation in collaborative tagging systems. It is proposed to model data from collaborative tagging systems with three-mode tensors, in order to capture the three-way correlations between users, tags, and items. By applying multiway analysis, latent correlations are revealed, which help to improve the quality of recommendations. Moreover, a hybrid scheme is proposed that additionally considers content-based information that is extracted from items. Experimental comparison, using data from a real collaborative tagging system (Last.fm), against both recent tag-aware and traditional (non tag aware) item recommendation algorithms indicates significant improvements in recommendation quality. Moreover, the experimental results illustrate the advantage of the proposed hybrid scheme. © 2011 IEEE.

Loading University of Hildesheim collaborators
Loading University of Hildesheim collaborators