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Durch die Einführung der Bildungsstandardsüberprüfungen erhält die Frage nach der Kompetenzentwicklung von Kindern einen zentralen Stellenwert in Bildungsangelegenheiten. Lehrkräfte stehen dadurch vor der Aufgabe, ihren Unterricht diesbezüglich zu verändern und Lösungen zu finden. Die Frage, ob der Einsatz der Montessoripädagogik die Entwicklung mathematischer Kompetenzen positiv beeinflusst, wurde im Rahmen einer Dissertation diskutiert und empirisch erforscht werden.
Der Arbeit liegt folgende Fragestellung zugrunde: Kann die Montessoripädagogik dazu beitragen, dass Schülerinnen und Schüler in österreichischen Regelvolksschulen ein hohes Kompetenzniveau in Mathematik erreichen? Wie unterscheidet sich die Kompetenzentwicklung in Klassen, die von Lehrpersonen mit beziehungsweise ohne Montessoriausbildung unterrichtet werden?
Bei der vorliegenden Studie handelt es sich um eine Längsschnittuntersuchung mit zwei Testzeitpunkten. Im Sinne einer summativen Evaluation wurden zu Beginn der Untersuchung die Basiskompetenzen mit dem „Entwicklungsorientierten Test zur Erfassung mathematischer Basiskompetenzen ab Schuleintritt (MBK 1+)“ analysiert. Am Ende der dritten Schulstufe wurde eine abschließende Bewertung im Rahmen der IKM-Testung des BIFIE vorgenommen, um die Wirksamkeit des Einsatzes der Montessorimethode zu ermitteln.
Untersucht wurden 14 Volksschulklassen, die entweder einer Untersuchungsgruppe oder einer Kontrollgruppe zugeordnet wurden. Die Klassen der Untersuchungsgruppe wurden von Lehrpersonen mit Montessori-Diplomausbildung unterrichtet, die Klassen der Kontrollgruppe von Lehrpersonen, die keine Montessoriausbildung absolviert haben. An der Studie nahmen 248 Schülerinnen und Schüler teil. Der Untersuchungszeitraum erstreckte sich über insgesamt drei Schuljahre.
Die Ergebnisse der Studie zeigen Tendenzen, dass sich der Einsatz der Montessoripädagogik positiv auf die Kompetenzentwicklung der Kinder im Bereich der Mathematik auswirkt.
In the last decade, crowdsourcing has proved its ability to address large scale data collection tasks, such as labeling large data sets, at a low cost and in a short time. However, the performance and behavior variability between workers as well as the variability in task designs and contents, induce an unevenness in the quality of the produced contributions and, thus, in the final output quality. In order to maintain the effectiveness of crowdsourcing, it is crucial to control the quality of the contributions. Furthermore, maintaining the efficiency of crowdsourcing requires the time and cost overhead related to the quality control to be at its lowest. While effective, current quality control techniques such as contribution aggregation, worker selection, context-specific reputation systems, and multi-step workflows, suffer from fairly high time and budget overheads and from their dependency on prior knowledge about individual workers.
In this thesis, we address this challenge by leveraging the similarity between completed and incoming tasks as well as the correlation between the worker declarative profiles and their performance in previous tasks in order to perform an efficient task-aware worker selection. To this end, we propose CAWS (Context AwareWorker Selection) method which operates in two phases; in an offline phase, completed tasks are clustered into homogeneous groups for each of which the correlation with the workers declarative profile is learned. Then, in the online phase, incoming tasks are matched to one of the existing clusters and the correspondent, previously inferred profile model is used to select the most reliable online workers for the given task. Using declarative profiles helps eliminate any probing process, which reduces the time and the budget while maintaining the crowdsourcing quality. Furthermore, the set of completed tasks, when compared to a probing task split, provides a larger corpus from which a more precise profile model can be learned. This translates to a better selection quality, especially for harder tasks.
In order to evaluate CAWS, we introduce CrowdED (Crowdsourcing Evaluation Dataset), a rich dataset to evaluate quality control methods and quality-driven task vectorization and clustering. The generation of CrowdED relies on a constrained sampling approach that allows to produce a task corpus which respects both, the budget and type constraints. Beside helping in evaluating CAWS, and through its generality and richness, CrowdED helps in plugging the benchmarking gap present in the crowdsourcing quality control community.
Using CrowdED, we evaluate the performance of CAWS in terms of the quality of the worker selection and in terms of the achieved time and budget reduction. Results shows the following: first, automatic grouping is able to achieve a learning quality similar to job-based grouping. And second, CAWS is able to outperform the state-of-the-art profile-based worker selection when it comes to quality. This is especially true when strong budget and time constraints are present on the requester side.
Finally, we complement our work by a software contribution consisting of an open source framework called CREX (CReate Enrich eXtend). CREX allows the creation, the extension and the enrichment of crowdsourcing datasets. It provides the tools to vectorize, cluster and sample a task corpus to produce constrained task sets and to automatically generate custom crowdsourcing campaign sites.
Blockchains and distributed ledger technology (DLT) that rely on Proof-of-Work (PoW) typically show limited performance. Several recent approaches incorporate Byzantine fault-tolerant (BFT) consensus protocols in their DLT design as Byzantine consensus allows for increased performance and energy efficiency, as well as it offers proven liveness and safety properties. While there has been a broad variety of research on BFT consensus protocols over the last decades, those protocols were originally not intended to scale for a large number of nodes. Thus, the quest for scalable BFT consensus was initiated with the emerging research interest in DLT. In this paper, we first provide a broad analysis of various optimization techniques and approaches used in recent protocols to scale Byzantine consensus for large environments such as BFT blockchain infrastructures. We then present an overview of both efforts and assumptions made by existing protocols and compare their solutions.
The Semantic Web exists for about 20 years by now, but its applicability as well as its presence does not live up to the standards of its original idea. Incorporated Semantic Web Technologies do have an initial barrier to learn and apply, which can discourage many potential users. This leads to less available data overall in addition to decreased data quality.
This work solves parts of the aforementioned problem by supporting idiomatic entry to those Semantic Web Technologies, allowing for "easier" accessibility and usability. Anno4j is a Java library that implements a form of Object-Relational Mapping for RDF data. With its application, RDF data can be created via a mapping by simply instantiating Java objects - an object-oriented programming concept the user is familiar with. On the other side, requesting persisted data is supported by a path-based querying possibility, while other features like transactional behaviour, code generation, and automated validation of input contribute to a more effective, comprehensive, and straightforward usage.
A use-case is provided by the MICO Platform, a centralized software instance that connects autonomous multimedia extractors in a workflow-driven fashion. This leads to a rich metadata background for the inserted multimedia files, enabling them to be used in diverse scenarios as well as unlocking yet hidden semantics. For this task it was necessary to design and implement a metadata model that is able to aggregate and merge the varying extractor results under a common denominator: the MICO Metadata Model.
The results of this work allow the use case to incorporate idiomatic Semantic Web Technologies which are then usable natively by non-Semantic Web experts. Additionally, an increase has been achieved in forms of data integration, synchronisation, integrity and validity, as well as an overall more comprehensive and rich implementation of the multimedia extractors.
Anke Donnerstag geht in ihrer komparativen Betrachtung der Filme DIE GUSTLOFF (D 2008) und EIN WEITES HERZ – SCHICKSALSJAHRE EINER DEUTSCHEN FAMILIE (D 2013) der Frage nach, wie die Deutschen und die Nazis in Fernsehspielfilmen der Gegenwart figuriert werden. Daran schließt sie die Frage an, in welchem Maße die dargestellten Deutschen und Nazis bereits vorhandenen Figurenstereotypen entsprechen. Unter Einbezug einer Studie, die sich mit der familiären Tradierung von Geschichtsbewusstsein befasst, wird im Beitrag erörtert, inwieweit die Figuren der untersuchten Filme durch ihre Darstellung ein Identifikationsangebot für die Gesellschaft bereitstellen. Konkret: Fördern die Figurendarstellungen das gegenwärtige kollektive Geschichtsbild, dass auch die deutsche Zivilbevölkerung als Opfer von Nationalsozialismus und Krieg versteht?
Der Binnentourismus in den ASEAN-Staaten wird an Hand von vorhandenen statistischen Daten untersucht. Durch Befragungen thailändischer Binnentouristen wird darüber hinaus die Datenerhebung ausgeweitet, um die vorhandenen statistischen Daten zu ergänzen und zu verifizieren. Hierbei wird insbesonders untersucht, in wieweit der Binnentourismus in den Ländern wahrgenommen wird, welche Bevölkerungsgruppen sich daran beteiligen, welche Interessen sie dabei verfolgen und welche Auswirkungen der Binnentourismus in physiognomischer und sozio-kultureller Hinsicht mit sich bringt.
Located at the interface of land and sea, Caribbean mangroves frequently experience severe disturbances by hurricanes, but in most cases storm-impacted mangrove forests are able to regenerate. How exactly regeneration proceeds, however, is still a matter of debate: does—due to the specific site conditions—regeneration follows a true auto-succession with exactly the same set of species driving regeneration that was present prior to the disturbance, or do different trajectories of regeneration exist? Considering the fundamental ecosystem services mangroves provide, a better understanding of their recovery is crucial. The Honduran island of Guanaja offers ideal settings for the study of regeneration dynamics of storm-impacted mangrove forests. The island was hit in October 1998 by Hurricane Mitch, one of the most intense Atlantic storms of the past century. Immediately after the storm, 97% of the mangroves were classified as dead. In 2005, long-term monitoring on the regeneration dynamics of the mangroves of the island was initiated, employing permanent line-transects at six different mangrove localities all around the island, which have been revisited in 2009 and 1016. Due to the pronounced topography of the island, different successional pathways emerge depending on the severity of the previous disturbance.
In the age of globalization, exponential growth and digitalization, organizations need to make faster decisions, as well as continually innovate and adapt to changing customer needs to ensure their long-term competitiveness (Kammerlander et al. 2018; Magnusson and Martini 2008). Notably, large and established companies find it difficult to keep up with start-ups and smaller companies as well as with digital transformation (Christensen et al. 2015).
Thus, organizations are increasingly providing their workforce with Enterprise Social Networks (ESNs) as intra-organizational social software platforms.
Although ESNs hold great promises for organizations and their employees (Mäntymäki and Riemer 2016), most ESN initiatives fail to leverage the intended benefits (Chin et al. 2015). This dissertation seeks to open up the black box behind this seemingly paradoxical relationship.
It comprises four studies that are guided by the following research questions:
R 1: How do different types of users (posters and lurkers) differ in their motivations for participating in ESNs? (= Paper 1)
R 2: Why do employees deliberately not use the ESN? (= Paper 2)
R 3: How can ESNs be successfully implemented and improved to overcome the challenges perceived by employees? (= Paper 3)
R 4: Does ESN usage impact on individual task performance? And how can ESNs be used effectively to increase performance outcomes? (= Paper 4)
Overall, the four studies contribute to a better understanding of the non-adoption phenomenon of ESNs, provide rich insights on employees’ underlying reasons and challenges regarding ESN usage, while underscoring the potential value of ESNs.
Previous laboratory studies on the centipede game have found that subjects exhibit surprisingly high levels of cooperation. Across disciplines, it has recently been highlighted that these high levels of cooperation might be explained by “team reasoning”, the willingness to think as a team rather than as an individual. We run an experiment with a standard centipede game as a baseline. In two treatments, we seek to induce team reasoning by making a joint goal salient. First, we implement a probabilistic variant of the centipede game that makes it easy to identify a joint goal. Second, we frame the game as a situation where a team of two soccer players attempts to score a goal. This frame increases the salience even more. Compared to the baseline, our treatments induce higher levels of cooperation. In a second experiment, we obtain similar evidence in a more natural environment–a beer garden during the 2014 FIFA Soccer World Cup. Our study contributes to understanding how a salient goal can support cooperation.
In dieser Arbeit wird eine neue Integraltransformation, die Roulettransformation, eingeführt. Diese arbeitet mit anisotropen Skalierungen und Rotationen. Es wird gezeigt, dass die Roulettransformation allgemeine gerichtete Singularitäten im Sinne von temperierten Distributionen auflöst. Die Abklingraten an Punkt- sowie Liniensingularitäten werden explizit angegeben.