Knowledge acquisition comprises various processes. Each of those has its dedicated research domain. Two examples are the relations between knowledge types and the influences of person-related variables. Furthermore, the transfer of knowledge is another crucial domain in educational research. I investigated these three processes through secondary analyses in this dissertation. Secondary analyses comply with the broadness of each field and yield the possibility of more general interpretations. The dissertation includes three meta-analyses: The first meta-analysis reports findings on the predictive relations between conceptual and procedural knowledge in mathematics in a cross-lagged panel model. The second meta-analysis focuses on the mediating effects of motivational constructs on the relationship between prior knowledge and knowledge after learning. The third meta-analysis deals with the effect of instructional methods in transfer interventions on knowledge transfer in school students. These three studies provide insights into the determinants and processes of knowledge acquisition and transfer. Knowledge types are interrelated; motivation mediates the relation between prior and later knowledge, and interventions influence knowledge transfer. The results are discussed by examining six key insights that build upon the three studies. Additionally, practical implications, as well as methodological and content-related ideas for further research, are provided.

This thesis explores local and global normal and Edgeworth approximations for symmetric binomial distributions. Further, it examines the normal approximation of convolution powers of continuous and discrete uniform distributions. We obtain the optimal constant in the local central limit theorem for symmetric binomial distributions and its analogs in higher-order Edgeworth approximation. Further, we offer a novel proof for the known optimal constant in the global central limit theorem for symmetric binomial distributions using Fourier inversion. We also consider the effect of simple continuity correction in the global central limit theorem for symmetric binomial distributions. Here, and in higher-order Edgeworth approximation, we found optimal constants and asymptotically sharp bounds on the approximation error. Furthermore, we prove asymptotically sharp bounds on the error in the local case of a relative normal approximation to symmetric binomial distributions. Additionally, we provide asymptotically sharp bounds on the approximation error in the local central limit theorem for convolution powers of continuous and discrete uniform distributions. Our methods include Fourier inversion formulae, explicit inequalities, and Edgeworth expansions, some of which may be of independent interest.

Visualizing brain simulation data is in many aspects a challenging task. For one, data used in brain simulations and the resulting datasets is heterogeneous and insight is derived by relating all different kinds of it. Second, the analysis process is rapidly changing while creating hypotheses about the results. Third, the scale of data entities in these heterogeneous datasets is manifold, reaching from single neurons to brain areas interconnecting millions. Fourth, the heterogeneous data consists of a variety of modalities, e.g.: from time series data to connectivity data, from single parameters to a set of parameters spanning parameter spaces with multiple possible and biological meaningful solutions; from geometrical data to hierarchies and textual descriptions, all on mostly different scales. Fifth, visualizing includes finding suitable representations and providing real-time interaction while supporting varying analysis workflows. To this end, this thesis presents a scalable and flexible software architecture for visualizing, integrating and interacting with brain simulations data. The scalability and flexibility is achieved by interconnected services forming in a series of Coordinated Multiple View (CMV) systems. Multiple use cases are presented, introducing views leveraging this architecture, extending its ecosystem and resulting in a Problem Solving Environment (PSE) from which custom-tailored CMV systems can be build. The construction of such CMV system is assisted by semantic reasoning hence the term semantic-aware CMVs.

Sozialunternehmen haben mindestens zwei Ziele: die Erfüllung ihrer sozialen bzw. ökologischen Mission und finanzielle Ziele. Zwischen diesen Zielen können Spannungen entstehen. Wenn sie sich in diesem Spannungsfeld wiederholt zugunsten der finanziellen Ziele entscheiden, kommt es zum Mission Drift. Die Priorisierung der finanziellen Ziele überlagert dabei die soziale Mission. Auch wenn das Phänomen in der Praxis mehrfach beobachtet und in Einzelfallanalysen beschrieben wurde, gibt es bislang wenig Forschung zu Mission Drift. Der Fokus der vorliegenden Arbeit liegt darauf, diese Forschungslücke zu schließen und eigene Erkenntnisse für die Auslöser und Treiber des Mission Drifts von Sozialunternehmen zu ermitteln. Ein Augenmerk liegt auf den verhaltensökonomischen Theorien und der Mixed-Gamble-Logik. Dieser Logik zufolge liegt bei Entscheidungen immer eine Gleichzeitigkeit von Gewinnen und Verlusten vor, sodass Entscheidungsträger die Furcht vor Verlusten gegenüber der Aussicht auf Gewinne abwägen müssen. Das Modell wird genutzt, um eine neue theoretische Betrachtungsweise auf die Abwägung zwischen sozialen und finanziellen Zielen bzw. Mission Drift zu erhalten. Mit einem Conjoint Experiment werden Daten über das Entscheidungsverhalten von Sozialunternehmern generiert. Im Zentrum steht die Abwägung zwischen sozialen und finanziellen Zielen in verschiedenen Szenarien (Krisen- und Wachstumssituationen). Mithilfe einer eigens erstellten Stichprobe von 1.222 Sozialunternehmen aus Deutschland, Österreich und der Schweiz wurden 187 Teilnehmende für die Studie gewonnen. Die Ergebnisse dieser Arbeit zeigen, dass eine Krisensituation Auslöser für Mission Drift von Sozialunternehmen sein kann, weil in diesem Szenario den finanziellen Zielen die größte Bedeutung zugemessen wird. Für eine Wachstumssituation konnten hingegen keine solche Belege gefunden werden. Hinzu kommen weitere Einflussfaktoren, welche die finanzielle Orientierung verstärken können, nämlich die Gründeridentitäten der Sozialunternehmer, eine hohe Innovativität der Unternehmen und bestimmte Stakeholder. Die Arbeit schließt mit einer ausführlichen Diskussion der Ergebnisse. Es werden Empfehlungen gegeben, wie Sozialunternehmen ihren Zielen bestmöglich treu bleiben können. Außerdem werden die Limitationen der Studie und Wege für zukünftige Forschung im Bereich Mission Drift aufgezeigt.

Physically-based distributed rainfall-runoff models as the standard analysis tools for hydro-logical processes have been used to simulate the water system in detail, which includes spa-tial patterns and temporal dynamics of hydrological variables and processes (Davison et al., 2015; Ek and Holtslag, 2004). In general, catchment models are parameterized with spatial information on soil, vegetation and topography. However, traditional approaches for eval-uation of the hydrological model performance are usually motivated with respect to dis-charge data alone. This may thus cloud model realism and hamper understanding of the catchment behavior. It is necessary to evaluate the model performance with respect to in-ternal hydrological processes within the catchment area as well as other components of wa-ter balance rather than runoff discharge at the catchment outlet only. In particular, a consid-erable amount of dynamics in a catchment occurs in the processes related to interactions of the water, soil and vegetation. Evapotranspiration process, for instance, is one of those key interactive elements, and the parameterization of soil and vegetation in water balance mod-eling strongly influences the simulation of evapotranspiration. Specifically, to parameterize the water flow in unsaturated soil zone, the functional relationships that describe the soil water retention and hydraulic conductivity characteristics are important. To define these functional relationships, Pedo-Transfer Functions (PTFs) are common to use in hydrologi-cal modeling. Opting the appropriate PTFs for the region under investigation is a crucial task in estimating the soil hydraulic parameters, but this choice in a hydrological model is often made arbitrary and without evaluating the spatial and temporal patterns of evapotran-spiration, soil moisture, and distribution and intensity of runoff processes. This may ulti-mately lead to implausible modeling results and possibly to incorrect decisions in regional water management. Therefore, the use of reliable evaluation approaches is continually re-quired to analyze the dynamics of the current interactive hydrological processes and predict the future changes in the water cycle, which eventually contributes to sustainable environ-mental planning and decisions in water management. Remarkable endeavors have been made in development of modelling tools that provide insights into the current and future of hydrological patterns in different scales and their im-pacts on the water resources and climate changes (Doell et al., 2014; Wood et al., 2011). Although, there is a need to consider a proper balance between parameter identifiability and the model's ability to realistically represent the response of the natural system. Neverthe-less, tackling this issue entails investigation of additional information, which usually has to be elaborately assembled, for instance, by mapping the dominant runoff generation pro-cesses in the intended area, or retrieving the spatial patterns of soil moisture and evapotran-spiration by using remote sensing methods, and evaluation at a scale commensurate with hydrological model (Koch et al., 2022; Zink et al., 2018). The present work therefore aims to give insights into the modeling approaches to simulate water balance and to improve the soil and vegetation parameterization scheme in the hydrological model subject to producing more reliable spatial and temporal patterns of evapotranspiration and runoff processes in the catchment. An important contribution to the overall body of work is a book chapter included among publications. The book chapter provides a comprehensive overview of the topic and valua-ble insights into the understanding the water balance and its estimation methods. Moreover, the first paper aimed to evaluate the hydrological model behavior with re-spect to contribution of various sources of information. To do so, a multi-criteria evaluation metric including soft and hard data was used to define constraints on outputs of the 1-D hydrological model WaSiM-ETH. Applying this evaluation metric, we could identify the optimal soil and vegetation parameter sets that resulted in a “behavioral” forest stand water balance model. It was found out that even if simulations of transpiration and soil water con-tent are consistent with measured data, but still the dominant runoff generation processes or total water balance might be wrongly calculated. Therefore, only using an evaluation scheme which looks over different sources of data and embraces an understanding of the local controls of water loss through soil and plant, allowed us to exclude the unrealistic modeling outputs. The results suggested that we may need to question the generally accept-ed soil parameterization procedures that apply default parameter sets. The second paper attempts to tackle the pointed model evaluation hindrance by getting down to the small-scale catchment (in Bavaria). Here, a methodology was introduced to analyze the sensitivity of the catchment water balance model to the choice of the Pedo-Transfer Functions (PTF). By varying the underlying PTFs in a calibrated and validated model, we could determine the resulting effects on the spatial distribution of soil hydraulic properties, total water balance in catchment outlet, and the spatial and temporal variation of the runoff components. Results revealed that the water distribution in the hydrologic system significantly differs amongst various PTFs. Moreover, the simulations of water balance components showed high sensitivity to the spatial distribution of soil hydraulic properties. Therefore, it was suggested that opting the PTFs in hydrological modeling should be care-fully tested by looking over the spatio-temporal distribution of simulated evapotranspira-tion and runoff generation processes, whether they are reasonably represented. To fulfill the previous studies’ suggestions, the third paper then aims to focus on evalu-ating the hydrological model through improving the spatial representation of dominant run-off processes. It was implemented in a mesoscale catchment in southwestern Germany us-ing the hydrological model WaSiM-ETH. Dealing with the issues of inadequate spatial ob-servations for rigorous spatial model evaluation, we made use of a reference soil hydrologic map available for the study area to discern the expected dominant runoff processes across a wide range of hydrological conditions. The model was parameterized by applying 11 PTFs and run by multiple synthetic rainfall events. To compare the simulated spatial patterns to the patterns derived by digital soil map, a multiple-component spatial performance metric (SPAEF) was applied. The simulated DRPs showed a large variability with regard to land use, topography, applied rainfall rates, and the different PTFs, which highly influence the rapid runoff generation under wet conditions. The three published manuscripts proceeded towards the model evaluation viewpoints that ultimately attain the behavioral model outputs. It was performed through obtaining information about internal hydrological processes that lead to certain model behaviors, and also about the function and sensitivity of some of the soil and vegetation parameters that may primarily influence those internal processes in a catchment. Accordingly, using this understanding on model reactions, and by setting multiple evaluation criteria, it was possi-ble to identify which parameterization could lead to behavioral model realization. This work, in fact, will contribute to solving some of the issues (e.g., spatial variability and modeling methods) identified as the 23 unsolved problems in hydrology in the 21st century (Blöschl et al., 2019). The results obtained in the present work encourage the further inves-tigations toward a comprehensive model calibration procedure considering multiple data sources simultaneously. This will enable developing the new perspectives to the current parameter estimation methods, which in essence, focus on reproducing the plausible dy-namics (spatio-temporal) of the other hydrological processes within the watershed.