Enhancing young students' high-level talk by using cooperative learning within Success for All lessons

This study examined whether students achieved high-level talk during group work because of involvement in cooperative learning within the Success for All (SfA) program. SfA is a comprehensive school program in which cooperative learning plays a key role, in addition to several other components such as parental involvement and tutoring. A quasi-experimental design with a treatment and a control group was used. At the end of the school year, grade-1 students (6- and 7-years-old children) executed a group task in small groups of four students. At that moment, SfA students had experienced cooperative learning within SfA lessons for a whole school year. In total, 160 students participated in this study. Using a coding scheme the quality of student's talk during group work was compared between treatment and control group. Compared to the control group, SfA students showed more high-level talk. SfA students expressed more extended elaborations of propositions and asked more open elaboration questions. Hence, the results of this study suggest that cooperative learning activities within SfA-lessons contributed to students' high-level talk.</p

Promoting students' social behavior in primary education through Success for All lessons

Success for All (SfA) is a comprehensive school reform program with a strong emphasis on cooperative learning that aims to improve students' social emotional learning alongside students' cognitive learning. In the present study it was examined whether SfA led to improved students' social behavior in Grade 1-3 of primary education. Peer sociometric data was collected for 974 students aged 6-9. Using multivariate multilevel analysis we found no significant effect of SfA on students' proand antisocial behavior over time. However, a significant interaction effect was found showing that antisocial behavior of students from disadvantaged backgrounds decreased in the intervention condition in Grade 2. This is a promising finding given that the SfA program was especially developed for schools serving large numbers of disadvantaged students. Implications of the study are described

The statistical mechanics of networks

We study the family of network models derived by requiring the expected properties of a graph ensemble to match a given set of measurements of a real-world network, while maximizing the entropy of the ensemble. Models of this type play the same role in the study of networks as is played by the Boltzmann distribution in classical statistical mechanics; they offer the best prediction of network properties subject to the constraints imposed by a given set of observations. We give exact solutions of models within this class that incorporate arbitrary degree distributions and arbitrary but independent edge probabilities. We also discuss some more complex examples with correlated edges that can be solved approximately or exactly by adapting various familiar methods, including mean-field theory, perturbation theory, and saddle-point expansions.Comment: 15 pages, 4 figure

On Natural Variation in Grades in Higher Education, and Its Implications for Assessing Effectiveness of Educational Innovations

To investigate the effect of innovations in the teaching?learning environment, researchers often compare study results from different cohorts across years. However, variance in scores can be attributed to both random fluctuation and systematic changes due to the innovation, complicating cohort comparisons. In the present study, we illustrate how using information about the variation in course grades over time can help researchers and practitioners better compare the grades and pass rates of different cohorts of students. To this end, all 375,093 grades from all 40,087 first-year students at a Dutch university during a period of six consecutive years were examined. Overall, about 17% of the variation in grades could be attributed to random variation between years and courses. With respect to passing courses, this percentage was almost 40%. Nonsignificant improvements in grades could be flagged as highly significant when this is ignored, thus leading to an overrepresentation of significant effects in educational literature. As a consequence, too many educational innovations are claimed to be effective

Solution of the 2-star model of a network

The p-star model or exponential random graph is among the oldest and best-known of network models. Here we give an analytic solution for the particular case of the 2-star model, which is one of the most fundamental of exponential random graphs. We derive expressions for a number of quantities of interest in the model and show that the degenerate region of the parameter space observed in computer simulations is a spontaneously symmetry broken phase separated from the normal phase of the model by a conventional continuous phase transition.Comment: 5 pages, 3 figure

fullfact: an R package for the analysis of genetic and maternal variance components from full factorial mating designs

Full factorial breeding designs are useful for quantifying the amount of additive genetic, nonadditive genetic, and maternal variance that explain phenotypic traits. Such variance estimates are important for examining evolutionary potential. Traditionally, full factorial mating designs have been analyzed using a two-way analysis of variance, which may produce negative variance values and is not suited for unbalanced designs. Mixed-effects models do not produce negative variance values and are suited for unbalanced designs. However, extracting the variance components, calculating significance values, and estimating confidence intervals and/or power values for the components are not straightforward using traditional analytic methods. We introduce fullfact an R package that addresses these issues and facilitates the analysis of full factorial mating designs with mixed-effects models. Here, we summarize the functions of the fullfact package. The observed data functions extract the variance explained by random and fixed effects and provide their significance. We then calculate the additive genetic, nonadditive genetic, and maternal variance components explaining the phenotype. In particular, we integrate nonnormal error structures for estimating these components for nonnormal data types. The resampled data functions are used to produce bootstrap-t confidence intervals, which can then be plotted using a simple function. We explore the fullfact package through a worked example. This package will facilitate the analyses of full factorial mating designs in R, especially for the analysis of binary, proportion, and/or count data types and for the ability to incorporate additional random and fixed effects and power analyses

Network dynamics with a nested node set: sociability in seven villages in Senegal

We propose two complementary ways to deal with a nesting structure in the node set of a network—such a structure may be called a multilevel network, with a node set consisting of several groups. First, within‐group ties are distinguished from between‐group ties by considering them as two distinct but interrelated networks. Second, effects of nodal variables are differentiated according to the levels of the nesting structure, to prevent ecological fallacies. This is elaborated in a study of two repeated observations of a sociability network in seven villages in Senegal, analyzed using the Stochastic Actor‐oriented Model