Examining the Impact of Opportunity Bursaries on the Financial Circumstances and Attitudes of Undergraduate Students in England

Notwithstanding the expansion of higher education across the OECD, there continues to be concern about the levels of participation amongst those from disadvantaged backgrounds. In response to this, a new form of financial support for students from low-income families, the 'opportunity bursary', was introduced for a limited period in England from 2001/02. Surveys of two cohorts of opportunity-bursary applicants were carried out and these suggested possible psychological, behavioural and economic impacts. Fewer opportunity-bursary recipients than non-recipients reported that part-time work had interfered with their studies and more reported that the bursary had made them less worried about meeting the costs of going to university. There was some evidence that the scheme led to increased retention in the first year of university study; it also appeared to lead to lower levels of debt, in particular bank overdrafts or credit card debt.     

‘When words don't come easily’: personal narratives from adolescents experiencing shyness as an emotional and behavioural problem in the school context

The purpose of this article is to present narratives from 15 adolescents experiencing shy behaviour as an emotional and behavioural problem in the school context in light of narrative understanding. The investigation is intended to generate knowledge about this largely under‐researched phenomenon based on the personal accounts of those who are actually experiencing it. Their narratives are the foundation for a discussion regarding implications for future practice in the context of school in relation to this group.     

Assessing library topics using sentiment analysis in R: a discussion and code sample

Abstract

This article discusses the use of R programing language for executing a sentiment analysis of tweets pertaining to library topics. This discussion is situated within the literature of marketing and management sciences, which is employing methods of machine learning and business intelligence to make informed decision-making, and library administration, which has expressed great interest in social media engagement within its literature but has yet to adopt these types of analysis. Presented in this article is a sample code with instructions on how users may execute it within R to retrieve and analyze tweets relevant to library services. Two examples created using the code (analysis of top librarians’ tweets and analysis of posts about major book publishers) are used to demonstrate the functionality of the code. The code presented in this article may be used by libraries to analyze tweets about their library and library-related topics, which, in turn, may inform management and marketing design.     

Discriminating talent-identified junior Australian football players using a video decision-making task

This study examined if a video decision-making task could discriminate talent-identified junior Australian football players from their non-talent-identified counterparts. Participants were recruited from the 2013 under 18 (U18) West Australian Football League competition and classified into two groups: talent-identified (State U18 Academy representatives; n = 25; 17.8 ± 0.5 years) and non-talent-identified (non-State U18 Academy selection; n = 25; 17.3 ± 0.6 years). Participants completed a video decision-making task consisting of 26 clips sourced from the Australian Football League game-day footage, recording responses on a sheet provided. A score of “1” was given for correct and “0” for incorrect responses, with the participants total score used as the criterion value. One-way analysis of variance tested the main effect of “status” on the task criterion, whilst a bootstrapped receiver operating characteristic (ROC) curve assessed the discriminant ability of the task. An area under the curve (AUC) of 1 (100%) represented perfect discrimination. Between-group differences were evident (P < 0.05) and the ROC curve was maximised with a score of 15.5/26 (60%) (AUC = 89.0%), correctly classifying 92% and 76% of the talent-identified and non-talent-identified participants, respectively. Future research should investigate the mechanisms leading to the superior decision-making observed in the talent-identified group.