University of Luxembourg

About: University of Luxembourg is a education organization based out in Luxembourg, Luxembourg. It is known for research contribution in the topics: Context (language use) & Computer science. The organization has 4744 authors who have published 22175 publications receiving 381824 citations.

Predicting first-grade mathematics achievement: the contributions of domain-general cognitive abilities, nonverbal number sense, and early number competence

Abstract: Early number competence, grounded in number-specific and domain-general cognitive abilities, is theorized to lay the foundation for later math achievement. Few longitudinal studies have tested a comprehensive model for early math development. Using structural equation modeling and mediation analyses, the present work examined the influence of kindergarteners’ nonverbal number sense and domain-general abilities i.e., working memory, fluid intelligence, and receptive vocabulary) and their early number competence (i.e., symbolic number skills) on first grade math achievement (arithmetic, shape and space skills, and number line estimation) assessed one year later. Latent regression models revealed that nonverbal number sense and working memory are central building blocks for developing early number competence in kindergarten and that early number competence is key for first grade math achievement. After controlling for early number competence, fluid intelligence significantly predicted arithmetic and number line estimation while receptive vocabulary significantly predicted shape and space skills. In sum we suggest that early math achievement draws on different constellations of number-specific and domain-general mechanisms.

Perspectives on problem solving in educational assessment: analytical, interactive, and collaborative problem solving

Abstract: Problem solving has received broad public interest as an important competency in modern societies. In educational large-scale assessments paper-pencil based analytical problem solving was included first (e.g., Programme for International Student Assessment, PISA 2003). With growing interest in more complex situations, the focus has shifted to interactive problem solving (e.g., PISA 2012) requiring identification and control of complex systems. In the future, collaborative problem solving represents the next step in assessing problem solving ability (e.g., PISA 2015). This paper describes these different approaches to assessing problem solving ability in large-scale assessments considering theoretical questions as well as assessment issues. For each of the three types of problem solving, the definition and understanding of the construct is explained, items examples are shown together with some empirical results, and limitations of the respective approach are discussed. A final discussion centers on the connection of cognitive and differential psychology within educational research and assessment.

Comparing Immigration Policies: An Overview from the IMPALA Database

Abstract: This paper introduces a method and preliminary findings from a database that systematically measures the character and stringency of immigration policies. Based on the selection of that data for nine countries between 1999 and 2008, we challenge the idea that any one country is systematically the most or least restrictive toward admissions. The data also reveal trends toward more complex and, often, more restrictive regulation since the 1990s, as well as differential treatment of groups, such as lower requirements for highly skilled than low-skilled labor migrants. These patterns illustrate the IMPALA data and methods but are also of intrinsic importance to understanding immigration regulation.