Evidence on age-related differentiation in the structure of cognitive abilities in childhood and adolescence is still inconclusive. Previous studies often focused on the interrelations or the g-saturation of broad ability constructs, neglecting abilities on lower strata. In contrast, we investigated differentiation in the internal structure of fluid intelligence/gf (with verbal, numeric, and figural reasoning) and crystallized intelligence/gc (with knowledge in the natural sciences, humanities, and social studies). To better understand the development of reasoning and knowledge during secondary education, we analyzed data from 11,756 students attending Grades 5 to 12. Changes in both the mean structure and the covariance structure were estimated with locally-weighted structural equation models that allow handling age as a continuous context variable. To substantiate a potential influence of school tracking (i.e., different learning environments), analyses were additionally conducted separated by school track (academic vs. nonacademic). Mean changes in gf and gc were approximately linear in the total sample, with a steeper slope for the latter. There was little indication of age-related differentiation for the different reasoning facets and knowledge domains. The results suggest that the relatively homogeneous scholastic learning environment in secondary education prevents the development of more pronounced ability or knowledge profiles.
Past research has revealed that knowledge integration is an important prerequisite for
the success of new product development. For this reason, companies deploy a number
of formal mechanisms to foster integration across multiple functions and hierarchical
levels. In many SMEs, however, such formal mechanisms are complemented or
even replaced by informal social networks among managers and employees. Despite
the relevance of intraorganizational networks, past research on SMEs, however, was
focused mainly on the interorganizational level of analysis. To cover this gap, we map
ego networks of senior managers in SMEs and explore their knowledge exchange relationships
both inside and across functional and hierarchical boundaries. Our study
shows that these social networks are an essential driver of knowledge integration and
Coleman formulated the thesis that social capital, which is rooted in social relations, provides certain options for action, such as, for example, the accumulation of human capital. Based on this theoretical assumption many authors in international research – mainly in the USA – investigated the effects of different forms of social capital on academic success. Therefore, the paper at hand focuses on the question whether the findings from abroad can be replicated for students in the secondary school system in Germany. Applying data of BiKS-8-14 and multi-level regressions, the effect of relations within and outside the family on school competences and grades is investigated. The results indicate on the one hand that for German students in secondary education their own social relations in school are important and on the other hand that effects vary in their meaning between the different school tracks.
Knowing household properties, such as number of persons per apartment, age of housing, type of water heating, etc. enables energy consultants and utilities to develop targeted energy conservation services. Load profiles captured by smart power meters, can—besides several other applications—be used to reveal energy efficiency relevant household characteristics. The goal of this work is to develop methods of supervised machine learning that deduce properties of private dwellings using consumption time series recorded in 30-min intervals. The contribution of this paper to the state of the art is threefold: we quadruplicate the number of features that describe power consumption curves to preserve classification relevant structures, indicate dimensionality reduction techniques to reduce the large-scale input data to a set of few significant features and finally, we redefine classes for some properties. As a result, the classification accuracy is elevated up to 82 %, while the runtime complexity is significantly reduced. The classification quality that can be achieved by our eCLASS methodology renders personalized efficiency measures in large-scale practical settings possible.