Beliefs von Schweizer Schülerinnen und Schülern zum konstruktivistischen und instruktivistischen Lernen im Mathematikunterricht der Sekundarstufe I – Ergebnisse eines Large-Scale-Assessments zur Überprüfung mathematischer Grundkompetenzen (ÜGK) 2016

ZusammenfassungIm Rahmen der ersten repräsentativen schweizweiten Überprüfung der mathematischen Grundkompetenzen am Ende der Sekundarstufe I (ÜGK 2016) wurden auch die Einstellungen bzw. Beliefs von 10.539 Schülerinnen und Schülern zum Mathematikunterricht erhoben. Es wurde zwischen Beliefs zum instruktivistischen Lernen und Beliefs zum konstruktivistischen Lernen – differenziert in drei Subdimensionen entdeckendes Lernen, soziales Lernen und realitätsbezogenes Lernen – unterschieden. Anders als es die theoretischen Erwartungen nahelegten, bilden die konstruktivistischen und instruktivistischen Einstellungen keine Gegensätze, sondern bestehen nebeneinander. Einstellungen zum entdeckenden und instruktivistischen Lernen korrelieren hoch miteinander und sind positive Prädiktoren für ein gutes Ergebnis im mathematischen Leistungstest, während Einstellungen zum sozialen und realitätsbezogenen Lernen negative Prädiktoren sind. Diese Befunde sind für Schülerinnen stärker ausgeprägt als für Schüler und steigen mit zunehmenden Schulniveau an. Von Schülerinnen und Schülern wahrgenommene Angebote zu einem kognitiv aktivierenden Mathematikunterricht werden ähnlich wie bei impliziten Theorien zur Intelligenz vollständig über ihre Einstellungen zum Lernen auf ihre mathematischen Testergebnisse mediiert, und zwar positiv über Einstellungen zum entdeckenden und instruktivistischen Lernen und negativ über Einstellungen zum realitätsbezogenen Lernen.

The Potential of International Large-Scale Assessments for Meta-Analyses in Education

Meta-analyses and international large-scale assessments (ILSA) are key sources for informing educational policy, research, and practice. While many critical research questions could be addressed by drawing evidence from both of these sources, meta-analysts seldom integrate ILSAs, and the current integration practices lack methodological guidance. The aim of this methodological review is therefore to synthesize and illustrate the principles and practices of including ILSA data in meta-analyses. Specifically, we (a) review systematically whether and how existing meta-analyses included ILSA data; (b) present four inclusion approaches (i.e., analytic steps, potential, challenges); and (c) illustrate the application of these approaches. Seeing the need for meta-analyses on educational inequalities, we situated the review and illustration in the context of gender differences and socioeconomic gaps in student achievement. Ultimately, we propose an analytic framework outlining the steps meta-analysts could take to utilize the potential and address the challenges of ILSA data for meta-analyses in education.

RALSA: the R analyzer for large-scale assessments

This paper presents the R Analyzer for Large-Scale Assessments (), a newly developed package for analyzing data from studies using complex sampling and assessment designs. Such studies are, for example, the IEA’s Trends in International Mathematics and Science Study and the OECD’s Programme for International Student Assessment. The package covers all cycles from a broad range of studies. The paper presents the architecture of the package, the overall workflow and illustrates some basic analyses using it. The package is open-source and free of charge. Other software packages for analyzing large-scale assessment data exist, some of them are proprietary, others are open-source. However, is the first comprehensive package, designed for the user experience and has some distinctive features. One innovation is that the package can convert SPSS data from large scale assessments into native data sets. It can also do so for PISA data from cycles prior to 2015, where the data is provided in tab-delimited text files along with SPSS control syntax files. Another feature is the availability of a graphical user interface, which is also written in and operates in any operating system where a full copy of can be installed. The output from any analysis function is written into an MS Excel workbook with multiple sheets for the estimates, model statistics, analysis information and the calling syntax itself for reproducing the analysis in future. The flexible design of allows for the quick addition of new studies, analysis types and features to the existing ones.

Efficiency Analysis with Educational Data: How to Deal with Plausible Values from International Large-Scale Assessments

International large-scale assessments (ILSAs) provide several measures as a representation of educational outcomes, the so-called plausible values, which are frequently interpreted as a representation of the ability range of students. In this paper, we focus on how this information should be incorporated into the estimation of efficiency measures of student or school performance using data envelopment analysis (DEA). Thus far, previous studies that have adopted this approach using data from ILSAs have used only one of the available plausible values or an average of all of them. We propose an approach based on the fuzzy DEA, which allows us to consider the whole distribution of results as a proxy of student abilities. To assess the extent to which our proposal offers similar results to those obtained in previous studies, we provide an empirical example using PISA data from 2015. Our results suggest that the performance measures estimated using the fuzzy DEA approach are strongly correlated with measures calculated using just one plausible value or an average measure. Therefore, we conclude that the studies that decide upon using one of these options do not seem to be making a significant error in their estimates.