Tackling transition:the value of peer mentoring

This paper is aimed at those interested in the promotion of student retention in higher education; particularly those with an interest in peer mentoring as a means of student support. It critically discusses the results of an exploratory study analysing the perceptions of peer mentors and mentees within five universities in the United Kingdom. The aim of the study was to analyse how student peer mentoring can aid transition into university by focusing specifically on how senior students can support their junior counterparts in their first year at university. The paper discusses the results of a survey which was completed by 329 student peer mentors and mentees. Focusing on the benefits and outcomes of participation in Mentoring Programmes, the survey was distinctive in that it asked mentors and mentees similar questions. From a theoretical perspective, the paper contributes to debates about peer support in higher education showing that participation in such programmes can have positive outcomes from both social and pedagogic perspectives. Practically speaking, the results have important implications for Higher Education Institutions as the research highlights the importance of putting into place formally structured Peer Mentoring Programmes which facilitate student support at a time when new students are most at risk of ‘dropping out’

A review on distinct methods and approaches to perform triangulation for Bayesian networks

Summary. Triangulation of a Bayesian network (BN) is somehow a necessary step in order to perform inference in a more efficient way, either if we use a secondary structure as the join tree (JT) or implicitly when we try to use other direct techniques on the network. If we focus on the first procedure, the goodness of the triangulation will affect on the simplicity of the join tree and therefore on a quicker and easier inference process. The task of obtaining an optimal triangulation (in terms of producing the minimum number of triangulation links a.k.a. fill-ins) has been proved as an NP-hard problem. That is why many methods of distinct nature have been used with the purpose of getting as good as possible triangulations for any given network, especially important for big structures, that is, with a large number of variables and links. In this chapter, we attempt to introduce the problem of triangulation, locating it in the compilation process and showing first its relevance for inference, and consequently for working with Bayesian networks. After this introduction, the most popular and used strategies to cope with the triangulation problem are reviewed