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This chapter outlines the challenges that research and practice in higher education have faced in measuring students' competences and learning outcomes. Particular attention is given to the systematic and institutional contexts in Germany. Based on the outlined national and international contextual framework, the Germany-wide program “Modeling and Measuring Competences in Higher Education (KoKoHs)” is discussed in terms of its two central working stages, key outcomes and lessons learned. In particular, the central results of the second phase are presented for the first time and integrated into the current state of international research. Based on this analysis, perspectives for further research on student learning in higher education and implications for practice and policy are derived.

The intention of this article is to show possible contributions of the concept of autonomous cooperation to enable complex adaptive logistics systems (CALS) to cope with increasing complexity and dynamics and therefore to increase the systems' information-processing capacity by implementing autopoietic characteristics. In order to reach this target, the concepts of CALS and autopoietic systems will be introduced and connected. The underlying aim is to use the concept of self-organization as one of their essential similarities to lead over to the concept of autonomous cooperation as the most narrow view on self-organizing systems, which is discussed as a possible approach to enable systems to handle an increasing quantity of information. This will be analyzed from both a theoretical and an empirical point of view.

As a standard procedure of human factors engineering, the design of complex systems (e.g., operator interfaces) starts with analyses of system objectives, missions, functions, and tasks. Perceptual Control Theory (PCT) provides a theoretical framework for guiding this process. PCT is founded on notions from control theory, in which closed-loop, negative-gain, feedback systems can be used to build powerful models of goal-directed behavior and for implementing complex systems (Powers, 1973). One of the strengths of PCT over competing human behavior theories is that it explains how humans can control systems that are subject to a wide variety of external influences. UAV control is through the operators’ interaction with the interfaces in remote control stations. A closed-loop feedback system is crucial for both operators and control systems to understand the states and goals of each other. It is likely that advanced UAV control systems will require operators to interact with automated systems such as IAIs. IAIs are sophisticated and will require knowledge about mission goals, the operators’ goals, and states, as well as the UAV and environmental states. Thus, the methods of analysis used in this research were based on PCT given its engineering origins in control theory and advantages accommodating various external disturbances.

Climate change will affect tourism at several temporal and spatial levels. This chapter focuses on the quantification of effects and the development of strategies to reduce extremes and frequencies as well as thresholds in tourism areas. Knowledge about possibilities for mitigation and adaptation of current and expected climate conditions requires interdisciplinary approaches and solutions. Several examples are presented, including the effects of trees against climate change and extreme events (heat waves), behavior adaptations, urban and regional planning measures, bioclimatic conditions in the Mediterranean and human–biometeorological conditions under climate change conditions, and user-friendly computer tools for the quantification of urban bioclimate conditions.

This chapter argues that despite the proverbial claim that populism is ill-defined and has too broad a conceptual net, the literature on the subject tends to converge toward four core elements of populism that provides a conceptual and analytical unity. Furthermore, the conceptual core of populism explains why the concept has been able to encompass a wide range of populist manifestations without becoming an empty analytical shell. Also, the conceptual cores have helped provide the empirical basis that has given rise to a diverse and innovative literature that seeks to measure and compare cross-nationally populism.

Each of the four objectives can be applied within the military training environment. Military training often requires that soldiers achieve specific levels of performance or proficiency in each phase of training. For example, training courses impose entrance and graduation criteria, and awards are given for excellence in military performance. Frequently, training devices, training media, and training evaluators or observers also directly support the need to diagnose performance strengths and weaknesses. Training measures may be used as indices of performance, and to indicate the need for additional or remedial training.

Technology for training military teams has evolved through a convergence of advances in simulation technology for individual and collective training, methods for analyzing teamwork and designing training solutions, and intelligent tutoring technologies that adapt training to the student, to accelerate learning. A number of factors have slowed this evolution toward intelligent team tutoring systems (ITTS), including the challenges of processing communications data, which are the currency of teamwork, and the paucity of automated and generalizable measures of team work. Several systems fulfill a subset of the features required of an ITTS, namely the use of team training objectives, teamwork models, measures of teamwork, diagnostic capability, instructional strategies, and adaptation of training to team needs. We describe these systems: the Advanced Embedded Training System (AETS), Synthetic Cognition for Operational Team Training (SCOTT), the AWO Trainer, the Benchmarked Experiential System for Training (BEST), and the Cross-Platform Mission Visualization Tool. We close this chapter with recommendations for future research.

The authors analyze a model for N different measurements of a persistent latent time series when measurement errors are mean-reverting, which implies a common trend among measurements. The authors study the consequences of overdifferencing, finding potentially large biases in maximum likelihood estimators (MLE) of the dynamics parameters and reductions in the precision of smoothed estimates of the latent variable, especially for multiperiod objects such as quinquennial growth rates. The authors also develop an R² measure of common trend observability that determines the severity of misspecification. Finally, the authors apply their framework to US quarterly data on GDE and GDI, obtaining an improved aggregate output measure.