AGWAN: A Generative Model for Labelled, Weighted Graphs

Real-world graphs or networks tend to exhibit a well-known set of properties, such as heavy-tailed degree distributions, clustering and community formation. Much effort has been directed into creating realistic and tractable models for unlabelled graphs, which has yielded insights into graph structure and evolution. Recently, attention has moved to creating models for labelled graphs: many real-world graphs are labelled with both discrete and numeric attributes. In this paper, we presentAgwan (Attribute Graphs: Weighted and Numeric), a generative model for random graphs with discrete labels and weighted edges. The model is easily generalised to edges labelled with an arbitrary number of numeric attributes. We include algorithms for fitting the parameters of the Agwanmodel to real-world graphs and for generating random graphs from the model. Using real-world directed and undirected graphs as input, we compare our approach to state-of-the-art random labelled graph generators and draw conclusions about the contribution of discrete vertex labels and edge weights to graph structure.

A Pilot Study of Reciprocal Peer Tutoring in Irish Medium Schools

A pilot study to investigate the effects of mathematics peer tutoring in Irish medium primary schools was undertaken. Five schools and 90 students took part in the pilot. Materials and resources that had previously been shown to work in English medium Scottish schools were translated into Irish by CCEA. Irish medium teachers attended three professional development days. Teachers implemented the peer tutoring techniques during mathematics lessons during a period of 16 weeks. Changes in attainment were measures with an Irish translation of the Scottish Survey of Achievement Mathematics Test. Results were positive. Student attainment was significantly raised during the 16-week implementation period by over one standard deviation. This equated to one-year’s worth of mathematics development during this time period. Results must be treated with caution. No control group was used in the pilot study. However, results are very promising and indicate that reciprocal role peer tutoring may be a useful pedagogy in Irish medium education. Further work would be required to establish this definitively.

Generating Realistic Labelled, Weighted Random Graphs

Generative algorithms for random graphs have yielded insights into the structure and evolution of real-world networks. Most networks exhibit a well-known set of properties, such as heavy-tailed degree distributions, clustering and community formation. Usually, random graph models consider only structural information, but many real-world networks also have labelled vertices and weighted edges. In this paper, we present a generative model for random graphs with discrete vertex labels and numeric edge weights. The weights are represented as a set of Beta Mixture Models (BMMs) with an arbitrary number of mixtures, which are learned from real-world networks. We propose a Bayesian Variational Inference (VI) approach, which yields an accurate estimation while keeping computation times tractable. We compare our approach to state-of-the-art random labelled graph generators and an earlier approach based on Gaussian Mixture Models (GMMs). Our results allow us to draw conclusions about the contribution of vertex labels and edge weights to graph structure.

Education for global citizenship in Scotland: reciprocal partnership or politics of benevolence?

Links between schools in the United Kingdom and partner schools in developing countries are an increasingly popular approach to teaching global citizenship. This study addresses the limited empirical research to date on the influence of such links on pupils' learning and understanding. Following an overview of the curricular theme of global citizenship in the Scottish curriculum and in the context of a partnership between Scotland and Malawi, challenges and potential pitfalls of teaching global citizenship are illustrated by the voices of pupils at four schools. Data is analysed through the themes of knowledge and understanding, concerns about fairness, and giving and helping. We reflect on whether our study indicates the intended reciprocal partnership or a 'politics of benevolence'.