The future of higher education and the future of higher education research

Abstract

Higher education research is closely linked to the debates on higher education policy and practice. It provides the information basis for decisions about the future of higher education. As the themes of the public debate on problems and reform needs in higher education change quickly, higher education research has to anticipate future problems and themes of debates in order to develop concepts and to generate knowledge well in advance. Future‐conscious higher education research might aim to identify likely future changes in thematic areas which are already in the limelight of public attention, as trends in the areas of expansion of higher education, diversification of structures of the higher education system, system steering and institutional management as well as internationalisation and globalisation suggest. Moreover, future‐conscious higher education research should try to identify thematic areas not frequently discussed at present but likely to be major issues in the future. For example, professionalisation of higher education in terms of the emergence and expansion of new administrative and service professions in higher education institutions might have far‐reaching implications in the future and is worth to be paid attention by higher education researchers.     

The cross-cultural generalizability of a new structural model of academic self-concepts

Prior research on the structure of academic self-concepts has demonstrated academic self-concepts to be domain-specific and hierarchically organized, but has largely failed to support the hypothesis that general academic self-concept is at the apex of the hierarchy. The present study investigates a new multidimensional nested-factor model of academic self-concepts that incorporates both domain-specific and general academic self-concepts, and the position of general academic self-concept at the apex of the self-concept hierarchy. Data were obtained from representative samples of 15-year-old students in 26 countries (total N = 106,680). Results showed that the nested-factor model provided a good fit to the data in each of the 26 countries, and that general and domain-specific academic self-concepts were meaningfully related to gender as well as to student achievement. Moreover, it emerged that the relationships between academic self-concepts and these student characteristics may differ substantially depending on whether the model applied does or does not account for the influence of general academic self-concept on domain-specific measures of academic self-concepts.     

Vergleichbarkeit von Abiturleistungen

This article examines differences in the mathematics and English proficiency of academic-track students in Baden-Württemberg (N?=?3526) and Hamburg (N?=?3734), investigating whether and to what extent these differences are reflected in the Abitur grades the students are awarded. The article also examines the extent to which scores in centrally conducted examinations provide better comparability than do coursework grades. Multilevel analyses predicting coursework grades in mathematics revealed clear between-state differences, with students in Hamburg being awarded higher mathematics grades than comparably able students in Baden-Württemberg. These differences are partly attributable to frame of reference effects and their impact on teachers’ grading practices. No corresponding between-state differences were found for English. Mathematics examination scores provided a much better measure of student achievement than mathematics coursework grades. The findings are discussed in terms of meritocratic access to sought-after university and training places.     

Assessing the impact of learning environments: How to use student ratings of classroom or school characteristics in multilevel modeling

In educational research, characteristics of the learning environment (e.g., social climate, instructional quality, goal orientation) are often assessed via student reports, and their associations with outcome variables such as school achievement or student motivation then tested. However, studying the effects of the learning environment presents a series of methodological challenges. This article discusses three crucial elements in research that uses student reports to gauge the impact of the learning environment on student outcomes. First, from a conceptual point of view, it is argued that ratings aggregated at the relevant level (e.g., class or school level), and not individual student ratings, are of primary interest in these studies. Second, the reliability of aggregated student ratings must be routinely assessed before these perceptions are related to outcome variables. Third, researchers conducting multilevel analyses need to make very clear which centering option was chosen for the predictor variables. This article shows that conclusions about the impact of learning environments can be substantially affected by the choice of a specific centering option for the individual student ratings.     

Das AutoMoDe-Projekt Modellbasierte Entwicklung softwareintensiver Systeme im Automobil

Die Entwicklung eingebetteter Software für Automobile ist inh?rent komplex und vereint verschiedene Entwicklungsphasen, mehrere fachliche Disziplinen, sowie verschiedene Akteure in beteiligten Unternehmen. Der AutoMoDe-Ansatz zur Entwicklung automotiver Software beschreibt Systeme auf verschiedenen Abstraktionsebenen und definiert schrittweise überg?nge zwischen diesen Ebenen. Neben der Definition geeigneter Ebenen werden zur Modellierung von Echtzeitsystemen ein einheitliches Berechnungsmodell sowie dom?nenspezifische Beschreibungstechniken verwendet. Automatisierte Anbindungen für Analyse und Synthese komplexer Softwaresysteme mit dem Ziel eines konsistenzbetonten Entwicklungsprozesses wurden realisiert. Die beschriebenen Techniken wurden in den Werkzeugprototypen AutoFocus integriert und im Zusammenspiel mit einer Werkzeugkette demonstriert.