Exposing multi-relational networks to single-relational network analysis algorithms

Many, if not most network analysis algorithms have been designed specifically for single-relational networks; that is, networks in which all edges are of the same type. For example, edges may either represent “friendship,” “kinship,” or “collaboration,” but not all of them together. In contrast, a multi-relational network is a network with a heterogeneous set of edge labels which can represent relationships of various types in a single data structure. While multi-relational networks are more expressive in terms of the variety of relationships they can capture, there is a need for a general framework for transferring the many single-relational network analysis algorithms to the multi-relational domain. It is not sufficient to execute a single-relational network analysis algorithm on a multi-relational network by simply ignoring edge labels. This article presents an algebra for mapping multi-relational networks to single-relational networks, thereby exposing them to single-relational network analysis algorithms.     

How do you make students learn more?

This article deals with the system of professional and vocational education and training in secondary schools in the Republic of Slovenia. It presents a research project, conducted on a sample of Slovene businesses. The purpose of the research was to establish whether companies that employ secondary school graduates are satisfied with their knowledge. It also shows one way to increase the knowledge of students in schools.     

Frühübergang in ein grundständiges Gymnasium – Übergang in ein privilegiertes Entwicklungsmilieu?

This article examines whether students’ learning gains in reading and mathematics depend on the time of their transition from elementary school to the academic track of secondary schooling. Drawing on data from the ELEMENT study, the learning gains of fifth and sixth graders in elementary schools (N?=?3167) and academic-track schools with various curricular profiles (N?=?1758) in Berlin were modelled by regression and propensity score matching (PSM) analyses. When baseline differences between the school types were controlled, analyses for reading did not reveal statistically significant differences at all. Findings for mathematics were inconsistent depending on the method of analysis. Regression analysis showed statistically significant results of small effect size in favour of the academic-track schools. However, this finding could not be replicated by means of PSM. The study does not provide evidence for a generally positive effect of early transition to the academic track of secondary schooling with respect to mathematics and reading performance. Theoretical and methodological implications and limitations of the study are discussed.     

Mathematically gifted students and high achievement: the role of motivation and classroom structure

One of the most intriguing questions for those who study intellectually gifted students is why some of them reach peak performances at school and others don’t. Moderator theories of giftedness assume that domain-specific gifts are transformed into achievement in a process influenced by non-cognitive and environmental variables. Thus, the current study investigates differences in the non-cognitive construct motivation (achievement goals, interest, self-concept, self-efficacy, implicit theories) and perception of classroom environment (classroom structure, class climate) between mathematically gifted high achievers (n = 66) and mathematically gifted students with non-high achievement (n = 144) using a latent variable approach. Gifted high achievers expressed higher levels of motivation than non-high achievers. Furthermore, they perceived a classroom structure that provides more tasks with a focus on learning and more autonomy.