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Kombination qualitativer und quantitativer Methoden zur Untersuchung der Studieneinstiegsphase
(2019)
Mit Hilfe der Kombination von qualitativen und quantitativen Verfahren zielen Mixed-Methods Ansätze darauf ab, einen vertieften Einblick in komplexe Gegenstände zu gewinnen. In der Hochschulbildungsforschung finden sie zunehmend Anklang, da sie besonders geeignet erscheinen, das vielschichtige Wirkungsgefüge zu erfassen, das das Lehren und Lernen an Hochschulen auszeichnet. Der Beitrag geht den Potenzialen von Mixed-Methods Ansätzen am Beispiel einer Studie zur Studieneingangsphase nach, die den Wirkungszusammenhang zwischen der Nutzung von Angeboten für den Studieneinstieg und der Entwicklung von Studierfähigkeit untersucht. Der Beitrag veranschaulicht die Integration von Methoden und Ergebnissen, um Chancen und Grenzen von Mixed-Methods Studien für die Hochschulbildungsforschung zu diskutieren.
Next Generation Manufacturing promises significant improvements in performance, productivity, and value creation. In addition to the desired and projected improvements regarding the planning, production, and usage cycles of products, this digital transformation will have a huge impact on work, workers, and workplace design. Given the high uncertainty in the likelihood of occurrence and the technical, economic, and societal impacts of these changes, we conducted a technology foresight study, in the form of a real-time Delphi analysis, to derive reliable future scenarios featuring the next generation of manufacturing systems. This chapter presents the organization dimension and describes each projection in detail, offering current case study examples and discussing related research, as well as implications for policy makers and firms. Specifically, we highlight seven areas in which the digital transformation of production will change how we work, how we organize the work within a company, how we evaluate these changes, and how employment and labor rights will be affected across company boundaries. The experts are unsure whether the use of collaborative robots in factories will replace traditional robots by 2030. They believe that the use of hybrid intelligence will supplement human decision-making processes in production environments. Furthermore, they predict that artificial intelligence will lead to changes in management processes, leadership, and the elimination of hierarchies. However, to ensure that social and normative aspects are incorporated into the AI algorithms, restricting measurement of individual performance will be necessary. Additionally, AI-based decision support can significantly contribute toward new, socially accepted modes of leadership. Finally, the experts believe that there will be a reduction in the workforce by the year 2030.
Hydraulic modeling is the classical approach to investigate and describe complex fluid motion. Many empirical formulas in the literature used for the hydraulic design of river training measures and structures have been developed using experimental data from the laboratory. Although computer capacities have increased to a high level which allows to run complex numerical simulations on standard workstation nowadays, non-standard design of structures may still raise the need to perform physical model investigations. These investigations deliver insight into details of flow patterns and the effect of varying boundary conditions. Data from hydraulic model tests may be used for calibration of numerical models as well. As the field of hydraulic modeling is very complex, this chapter intends to give a short overview on capacities and limits of hydraulic modeling in regard to river flows and hydraulic structures only. The reader shall get a first idea of modeling principles and basic considerations. More detailed information can be found in the references.