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Institute
- Fakultät III - Medien, Information und Design (52) (remove)
„Grappa“ ist eine Middleware, die auf die Anbindung verschiedener Autobewerter an verschiedene E-Learning-Frontends respektive Lernmanagementsysteme (LMS) spezialisiert ist. Ein Prototyp befindet sich seit mehreren Semestern an der Hochschule Hannover mit dem LMS „moodle“ und dem Backend „aSQLg“ im Einsatz und wird regelmäßig evaluiert. Dieser Beitrag stellt den aktuellen Entwicklungsstand von Grappa nach diversen Neu- und Weiterentwicklungen vor. Nach einem Bericht über zuletzt gesammelte Erfahrungen mit der genannten Kombination von Systemen stellen wir wesentliche Neuerungen der moodle-Plugins, welche der Steuerung von Grappa aus moodle heraus dienen, vor. Anschließend stellen wir eine Erweiterung der bisherigen Architektur in Form eines neuentwickelten Grappa-php-Clients zur effizienteren Anbindung von LMS vor. Weiterhin berichten wir über die Anbindung eines weiteren Autobewerters „Graja“ für Programmieraufgaben in Java. Der Bericht zeigt, dass bereits wichtige Schritte für eine einheitliche Darstellung automatisierter Programmbewertung in LMS mit unterschiedlichen Autobewertern für die Studierenden absolviert sind. Die praktischen Erfahrungen zeigen aber auch, dass sowohl bei jeder der Systemkomponenten individuell, wie auch in deren Zusammenspiel via Grappa noch weitere Entwicklungsarbeiten erforderlich sind, um die Akzeptanz und Nutzung bei Studierenden sowie Lehrenden weiter zu steigern.
Vorgestellt wird ein Ansatz zur objektorientierten Modellierung, Simulation und Animation von Informationssystemen. Es wird ein Vorgehensmodell dargestellt, mit dem unter Verwendung des beschriebenen Ansatzes Anforderungs- oder Systemspezifikationen von Rechnergestützten Informationssystemen erstellt werden können. Der Ansatz basiert auf einem Metamodell zur Beschreibung Rechnergestützter Informationssysteme und verfügt über eine rechnergestützte Modellierungsumgebung. Anhand eines Projektes zur Entwicklung einer Anforderungsspezifikation für ein rechnergestütztes Pflegedokumentations- und -kommunikationssystems wird der Einsatz der Methode beispielhaft illustriert.
The dependency of word similarity in vector space models on the frequency of words has been noted in a few studies, but has received very little attention. We study the influence of word frequency in a set of 10 000 randomly selected word pairs for a number of different combinations of feature weighting schemes and similarity measures. We find that the similarity of word pairs for all methods, except for the one using singular value decomposition to reduce the dimensionality of the feature space, is determined to a large extent by the frequency of the words. In a binary classification task of pairs of synonyms and unrelated words we find that for all similarity measures the results can be improved when we correct for the frequency bias.
Discovery and efficient reuse of technology pictures using Wikimedia infrastructures. A proposal
(2016)
Multimedia objects, especially images and figures, are essential for the visualization and interpretation of research findings. The distribution and reuse of these scientific objects is significantly improved under open access conditions, for instance in Wikipedia articles, in research literature, as well as in education and knowledge dissemination, where licensing of images often represents a serious barrier.
Whereas scientific publications are retrievable through library portals or other online search services due to standardized indices there is no targeted retrieval and access to the accompanying images and figures yet. Consequently there is a great demand to develop standardized indexing methods for these multimedia open access objects in order to improve the accessibility to this material.
With our proposal, we hope to serve a broad audience which looks up a scientific or technical term in a web search portal first. Until now, this audience has little chance to find an openly accessible and reusable image narrowly matching their search term on first try - frustratingly so, even if there is in fact such an image included in some open access article.
Regional knowledge map is a tool recently demanded by some actors in an institutional level to help regional policy and innovation in a territory. Besides, knowledge maps facilitate the interaction between the actors of a territory and the collective learning. This paper reports the work in progress of a research project which objective is to define a methodology to efficiently design territorial knowledge maps, by extracting information of big volumes of data contained in diverse sources of information related to a region. Knowledge maps facilitate management of the intellectual capital in organisations. This paper investigates the value to apply this tool to a territorial region to manage the structures, infrastructures and the resources to enable regional innovation and regional development. Their design involves the identification of information sources that are required to find which knowledge is located in a territory, which actors are involved in innovation, and which is the context to develop this innovation (structures, infrastructures, resources and social capital). This paper summarizes the theoretical background and framework for the design of a methodology for the construction of knowledge maps, and gives an overview of the main challenges for the design of regional knowledge maps.
Regional Innovation Systems describe the relations between actors, structures and infrastructures in a region in order to stimulate innovation and regional development. For these systems the collection and organization of information is crucial. In the present paper we investigate the possibilities to extract information from websites of companies. First we describe regional innovation systems and the information types that are necessary to create them. Then we discuss the possibilities of text mining and keyword extraction techniques to extract this information from company websites. Finally, we describe a small scale experiment in which keywords related to economic sectors and commodities are extracted from the websites of over 200 companies. This experiment shows what the main challenges are for information extraction from websites for regional innovation systems.
Automatic classification of scientific records using the German Subject Heading Authority File (SWD)
(2012)
The following paper deals with an automatic text classification method which does not require training documents. For this method the German Subject Heading Authority File (SWD), provided by the linked data service of the German National Library is used. Recently the SWD was enriched with notations of the Dewey Decimal Classification (DDC). In consequence it became possible to utilize the subject headings as textual representations for the notations of the DDC. Basically, we we derive the classification of a text from the classification of the words in the text given by the thesaurus. The method was tested by classifying 3826 OAI-Records from 7 different repositories. Mean reciprocal rank and recall were chosen as evaluation measure. Direct comparison to a machine learning method has shown that this method is definitely competitive. Thus we can conclude that the enriched version of the SWD provides high quality information with a broad coverage for classification of German scientific articles.
Bei der Konzeption und Entwicklung der BID-Studiengänge ist neben den inhaltlichen und studienorganisatorischen Überlegungen die Ableitung und Entwicklung realistischer Planungsdaten eine der Hauptaufgaben des Modellversuchs BID und eine wesentliche Voraussetzung für ihre erfolgreiche Umsetzung in die Praxis gewesen. Auf diese Planungsergebnisse und die Umsetzung wird in diesem Beitrag vor allem einzugehen sein.