Young children working together:Cooperative learning effects on group work of children in Grade 1 of primary education

It was examined whether cooperative learning within the Success for All (SfA) program led to improved group work behaviour of Grade 1 pupils. 168 pupils of six SfA schools and 144 pupils of four control schools participated. Positive and negative group work behaviour was observed during a group task, taking into account socioemotional ethos, group participation, and type of dialogue. Longitudinal multilevel analysis was used for the sequence of observed 20-s time intervals. SfA groups showed more positive and less negative group work behaviour compared to control groups, whilst controlling for several group characteristics. Results suggest that negative group work behaviour increased gradually during the whole task in control groups, while in SfA groups it increased only towards the end of the task. The findings indicate that cooperative learning may lead to improved group work behaviour of young pupils (6–7 years old)

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

Sharing Social Network Data: Differentially Private Estimation of Exponential-Family Random Graph Models

Motivated by a real-life problem of sharing social network data that contain sensitive personal information, we propose a novel approach to release and analyze synthetic graphs in order to protect privacy of individual relationships captured by the social network while maintaining the validity of statistical results. A case study using a version of the Enron e-mail corpus dataset demonstrates the application and usefulness of the proposed techniques in solving the challenging problem of maintaining privacy \emph{and} supporting open access to network data to ensure reproducibility of existing studies and discovering new scientific insights that can be obtained by analyzing such data. We use a simple yet effective randomized response mechanism to generate synthetic networks under $\epsilon$-edge differential privacy, and then use likelihood based inference for missing data and Markov chain Monte Carlo techniques to fit exponential-family random graph models to the generated synthetic networks.Comment: Updated, 39 page

Interviewer effects on non-response propensity in longitudinal surveys:a multilevel modelling approach

The paper investigates two different multilevel approaches, the multilevel cross-classified and the multiple-membership models, for the analysis of interviewer effects on wave non-response in longitudinal surveys. The models proposed incorporate both interviewer and area effects to account for the non-hierarchical structure, the influence of potentially more than one interviewer across waves and possible confounding of area and interviewer effects arising from the non-random allocation of interviewers across areas. The methods are compared by using a data set: the UK Family and Children Survey

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

Topological network alignment uncovers biological function and phylogeny

Sequence comparison and alignment has had an enormous impact on our understanding of evolution, biology, and disease. Comparison and alignment of biological networks will likely have a similar impact. Existing network alignments use information external to the networks, such as sequence, because no good algorithm for purely topological alignment has yet been devised. In this paper, we present a novel algorithm based solely on network topology, that can be used to align any two networks. We apply it to biological networks to produce by far the most complete topological alignments of biological networks to date. We demonstrate that both species phylogeny and detailed biological function of individual proteins can be extracted from our alignments. Topology-based alignments have the potential to provide a completely new, independent source of phylogenetic information. Our alignment of the protein-protein interaction networks of two very different species--yeast and human--indicate that even distant species share a surprising amount of network topology with each other, suggesting broad similarities in internal cellular wiring across all life on Earth.Comment: Algorithm explained in more details. Additional analysis adde