eLearning im Sportstudium

In der gesamten Hochschullandschaft begleiten eLearning-Szenarien organisatorische Erneuerungsprozesse und stellen damit ein vielversprechendes Instrument zur Unterstützung und Verbesserung der klassischen Präsenzlehre dar. Davon ausgehend wurde von 2010 bis 2011 das Kasseler Sportspiel-Modell um die integrative Vermittlung der Einkontakt-Rückschlagspiele erweitert (Heyer, Albert, Scheid & Blömeke-Rumpf, 2011) und in einen modularisierten eLearning-Content, bestehend aus insgesamt 4 Modulen (17 Lernkurse, 171 Kursseiten, 73 Grafiken, 73 Videos, 38 Lernkontrollfragen), eingebunden. Dieser Content wurde im Rahmen einer Evaluationsstudie in Blended Learning Seminaren, welche die didaktischen Vorteile von Online- und Präsenzphasen zu einer Seminarform vereinen (Treumann, Ganguin & Arens, 2012), vergleichend zur klassischen Präsenzlehre im Sportstudium betrachtet. Die Studie gliedert sich in insgesamt drei Phasen: 1.) Pilotstudie am IfSS in Kassel (WS 2011/12; N=17, Lehramt), 2.) Hauptuntersuchung I am IfSS in Kassel (SS 2012; N=67, Lehramt) und 3.) Hauptuntersuchung II am IfS in Frankfurt a. M. (WS 2012/13; N=112, BA). Mittels varianzanalytischer Untersuchungsverfahren erfasst die Studie auf drei unterschiedlichen Qualitätsebenen folgende Aspekte der Lehr-Lernforschung: 1.) Ebene der Inputqualität: Bewertung der Seminarform (BS), 2.) Ebene der Prozessqualität: Motivation (SELLMO-ST), Lernstrategien (LIST) und computerbezogene Einstellung (FIDEC), 3.) Ebene der Outcomequalität: Lernleistung (Abschlusstest und Transferaufgabe). In der vergleichenden Betrachtung der beiden Hauptuntersuchungen erfolgt eine Gegenüberstellung von je einem Präsenzseminar zu zwei unterschiedlichen Varianten von Blended Learning Seminaren (BL-1, BL-2). Während der Online-Phasen bearbeiten die Sportstudierenden in BL-1 die Module in Lerngruppen. Die Teilnehmer in BL-2 führen in diesen Phasen zusätzlich persönliche Lerntagebücher. Dies soll zu einer vergleichsweise intensiveren Auseinandersetzung mit den Inhalten der Lernkurse sowie dem eigenen Lernprozess auf kognitiver und metakognitiver Ebene anregen (Hübner, Nückles & Renkl, 2007) und folglich zu besseren Ergebnissen auf den drei Qualitätsebenen führen. Die Ergebnisse der beiden Hauptuntersuchungen zeigen in der direkten, standortbezogenen Gegenüberstellung aller drei Seminarformen überwiegend keine statistisch signifikanten Unterschiede. Der erwartete positive Effekt durch die Einführung des Lerntagebuchs bleibt ebenfalls aus. Im standortübergreifenden Vergleich der Blended-Learning-Seminare ist bemerkenswert, dass die Probanden aus Frankfurt gegenüber ihrer Seminarform eine tendenziell kritischere Haltung einnehmen, was möglicherweise mit den vorherrschenden, unterschiedlichen Studiengängen – Lehramt und BA – korrespondiert. Zusammenfassend lässt sich somit für den untersuchten Bereich der Rückschlagspielvermittlung festhalten, dass Blended-Learning-Seminare eine qualitativ gleichwertige Alternative zur klassischen Präsenzlehre im Sportstudium darstellen.

Statistical evaluation of texture analysis from the biocrystallization method

The consumers are becoming more concerned about food quality, especially regarding how, when and where the foods are produced (Haglund et al., 1999; Kahl et al., 2004; Alföldi, et al., 2006). Therefore, during recent years there has been a growing interest in the methods for food quality assessment, especially in the picture-development methods as a complement to traditional chemical analysis of single compounds (Kahl et al., 2006). The biocrystallization as one of the picture-developing method is based on the crystallographic phenomenon that when crystallizing aqueous solutions of dihydrate CuCl2 with adding of organic solutions, originating, e.g., from crop samples, biocrystallograms are generated with reproducible crystal patterns (Kleber & Steinike-Hartung, 1959). Its output is a crystal pattern on glass plates from which different variables (numbers) can be calculated by using image analysis. However, there is a lack of a standardized evaluation method to quantify the morphological features of the biocrystallogram image. Therefore, the main sakes of this research are (1) to optimize an existing statistical model in order to describe all the effects that contribute to the experiment, (2) to investigate the effect of image parameters on the texture analysis of the biocrystallogram images, i.e., region of interest (ROI), color transformation and histogram matching on samples from the project 020E170/F financed by the Federal Ministry of Food, Agriculture and Consumer Protection(BMELV).The samples are wheat and carrots from controlled field and farm trials, (3) to consider the strongest effect of texture parameter with the visual evaluation criteria that have been developed by a group of researcher (University of Kassel, Germany; Louis Bolk Institute (LBI), Netherlands and Biodynamic Research Association Denmark (BRAD), Denmark) in order to clarify how the relation of the texture parameter and visual characteristics on an image is. The refined statistical model was accomplished by using a lme model with repeated measurements via crossed effects, programmed in R (version 2.1.0). The validity of the F and P values is checked against the SAS program. While getting from the ANOVA the same F values, the P values are bigger in R because of the more conservative approach. The refined model is calculating more significant P values. The optimization of the image analysis is dealing with the following parameters: ROI(Region of Interest which is the area around the geometrical center), color transformation (calculation of the 1 dimensional gray level value out of the three dimensional color information of the scanned picture, which is necessary for the texture analysis), histogram matching (normalization of the histogram of the picture to enhance the contrast and to minimize the errors from lighting conditions). The samples were wheat from DOC trial with 4 field replicates for the years 2003 and 2005, “market samples”(organic and conventional neighbors with the same variety) for 2004 and 2005, carrot where the samples were obtained from the University of Kassel (2 varieties, 2 nitrogen treatments) for the years 2004, 2005, 2006 and “market samples” of carrot for the years 2004 and 2005. The criterion for the optimization was repeatability of the differentiation of the samples over the different harvest(years). For different samples different ROIs were found, which reflect the different pictures. The best color transformation that shows efficiently differentiation is relied on gray scale, i.e., equal color transformation. The second dimension of the color transformation only appeared in some years for the effect of color wavelength(hue) for carrot treated with different nitrate fertilizer levels. The best histogram matching is the Gaussian distribution. The approach was to find a connection between the variables from textural image analysis with the different visual criteria. The relation between the texture parameters and visual evaluation criteria was limited to the carrot samples, especially, as it could be well differentiated by the texture analysis. It was possible to connect groups of variables of the texture analysis with groups of criteria from the visual evaluation. These selected variables were able to differentiate the samples but not able to classify the samples according to the treatment. Contrarily, in case of visual criteria which describe the picture as a whole there is a classification in 80% of the sample cases possible. Herewith, it clearly can find the limits of the single variable approach of the image analysis (texture analysis).

Evaluation of water retention capacity and flood control function of the forest catchment

This research quantitatively evaluates the water retention capacity and flood control function of the forest catchments by using hydrological data of the large flood events which happened after the serious droughts. The objective sites are the Oodo Dam and the Sameura Dam catchments in Japan. The kinematic wave model, which considers saturated and unsaturated sub-surface soil zones, is used for the rainfall-runoff analysis. The result shows that possible storage volume of the Oodo Dam catchment is 162.26 MCM in 2005, while that of Samerua is 102.83 MCM in 2005 and 102.64 MCM in 2007. Flood control function of the Oodo Dam catchment is 173 mm in water depth in 2005, while the Sameura Dam catchment 114 mm in 2005 and 126 mm in 2007. This indicates that the Oodo Dam catchment has more than twice as big water capacity as its capacity (78.4 mm), while the Sameura Dam catchment has about one-fifth of the its storage capacity (693 mm).

Managing Quality Properties of Web Service Compositions

Web services from different partners can be combined to applications that realize a more complex business goal. Such applications built as Web service compositions define how interactions between Web services take place in order to implement the business logic. Web service compositions not only have to provide the desired functionality but also have to comply with certain Quality of Service (QoS) levels. Maximizing the users' satisfaction, also reflected as Quality of Experience (QoE), is a primary goal to be achieved in a Service-Oriented Architecture (SOA). Unfortunately, in a dynamic environment like SOA unforeseen situations might appear like services not being available or not responding in the desired time frame. In such situations, appropriate actions need to be triggered in order to avoid the violation of QoS and QoE constraints. In this thesis, proper solutions are developed to manage Web services and Web service compositions with regard to QoS and QoE requirements. The Business Process Rules Language (BPRules) was developed to manage Web service compositions when undesired QoS or QoE values are detected. BPRules provides a rich set of management actions that may be triggered for controlling the service composition and for improving its quality behavior. Regarding the quality properties, BPRules allows to distinguish between the QoS values as they are promised by the service providers, QoE values that were assigned by end-users, the monitored QoS as measured by our BPR framework, and the predicted QoS and QoE values. BPRules facilitates the specification of certain user groups characterized by different context properties and allows triggering a personalized, context-aware service selection tailored for the specified user groups. In a service market where a multitude of services with the same functionality and different quality values are available, the right services need to be selected for realizing the service composition. We developed new and efficient heuristic algorithms that are applied to choose high quality services for the composition. BPRules offers the possibility to integrate multiple service selection algorithms. The selection algorithms are applicable also for non-linear objective functions and constraints. The BPR framework includes new approaches for context-aware service selection and quality property predictions. We consider the location information of users and services as context dimension for the prediction of response time and throughput. The BPR framework combines all new features and contributions to a comprehensive management solution. Furthermore, it facilitates flexible monitoring of QoS properties without having to modify the description of the service composition. We show how the different modules of the BPR framework work together in order to execute the management rules. We evaluate how our selection algorithms outperform a genetic algorithm from related research. The evaluation reveals how context data can be used for a personalized prediction of response time and throughput.

Strategies towards evaluation beyond scientific impact

Various research fields, like organic agricultural research, are dedicated to solving real-world problems and contributing to sustainable development. Therefore, systems research and the application of interdisciplinary and transdisciplinary approaches are increasingly endorsed. However, research performance depends not only on self-conception, but also on framework conditions of the scientific system, which are not always of benefit to such research fields. Recently, science and its framework conditions have been under increasing scrutiny as regards their ability to serve societal benefit. This provides opportunities for (organic) agricultural research to engage in the development of a research system that will serve its needs. This article focuses on possible strategies for facilitating a balanced research evaluation that recognises scientific quality as well as societal relevance and applicability. These strategies are (a) to strengthen the general support for evaluation beyond scientific impact, and (b) to provide accessible data for such evaluations. Synergies of interest are found between open access movements and research communities focusing on global challenges and sustainability. As both are committed to increasing the societal benefit of science, they may support evaluation criteria such as knowledge production and dissemination tailored to societal needs, and the use of open access. Additional synergies exist between all those who scrutinise current research evaluation systems for their ability to serve scientific quality, which is also a precondition for societal benefit. Here, digital communication technologies provide opportunities to increase effectiveness, transparency, fairness and plurality in the dissemination of scientific results, quality assurance and reputation. Furthermore, funders may support transdisciplinary approaches and open access and improve data availability for evaluation beyond scientific impact. If they begin to use current research information systems that include societal impact data while reducing the requirements for narrative reports, documentation burdens on researchers may be relieved, with the funders themselves acting as data providers for researchers, institutions and tailored dissemination beyond academia.