Do higher education students really seek ‘value for money’?: Debunking the myth

Although students are increasingly cast as consumers wanting ‘value for money’, this study empirically investigated whether students actively seek value for money. In Study 1, 1,772 undergraduates at a mid-ranked English university were asked open-ended questions about what they had wanted from their university learning experience and how that had turned out. Hopes were coded as fulfilled or unfulfilled. Responses were searched for key words related to ‘value for money’. Less than 2 per cent of students referenced ‘value for money’. Those students were significantly more likely to have unfulfilled hopes. In Study 2, 185 first-year science students were asked open-ended questions about why they chose their subject and their programme, and what they had wanted from their learning experience in that programme. None referenced value for money. Students’ reasons for choosing their subjects and programmes were analysed. ‘Value for money’ does not do justice to students’ hopes for university or their programme

Reconceptualising academic development as community development: lessons from working with Syrian academics in exile

This paper focuses on academic development for Syrian academics in exile. Academic development first emerged in resource-rich, global North environments including the UK, the USA, Australia, and Scandinavia nearly 50 years ago as reported by Gosling (International Journal for Academic Development, 14(1):5–18, 2009), and the majority of research studies in this field focus on activities in global North, resource-rich, institutional settings. Yet academics in resource-poor, [post-] conflict and post-colonial contexts face different challenges and circumstances, and have different academic development needs. This paper extends the conceptual and contextual scope of this field by investigating the experiences and academic development needs of Syrian academics in exile, and interrogating the concept of academic development within that context. It establishes the background context of Syrian academia in exile, before summarising the nature and aims of the Council for At Risk Academics (Cara) Syria Programme. It then outlines the study’s methodology, before presenting the findings of a thematic analysis of a multi-level data set. It then interrogates the concept and normative terrain of academic development in light of these findings, and proposes a model for academic community development to support academic communities in exile, and marginalised academic communities more widely

Learning a Static Analyzer from Data

To be practically useful, modern static analyzers must precisely model the effect of both, statements in the programming language as well as frameworks used by the program under analysis. While important, manually addressing these challenges is difficult for at least two reasons: (i) the effects on the overall analysis can be non-trivial, and (ii) as the size and complexity of modern libraries increase, so is the number of cases the analysis must handle. In this paper we present a new, automated approach for creating static analyzers: instead of manually providing the various inference rules of the analyzer, the key idea is to learn these rules from a dataset of programs. Our method consists of two ingredients: (i) a synthesis algorithm capable of learning a candidate analyzer from a given dataset, and (ii) a counter-example guided learning procedure which generates new programs beyond those in the initial dataset, critical for discovering corner cases and ensuring the learned analysis generalizes to unseen programs. We implemented and instantiated our approach to the task of learning JavaScript static analysis rules for a subset of points-to analysis and for allocation sites analysis. These are challenging yet important problems that have received significant research attention. We show that our approach is effective: our system automatically discovered practical and useful inference rules for many cases that are tricky to manually identify and are missed by state-of-the-art, manually tuned analyzers

Regular morphisms do not preserve FF-rationality

For each positive prime integer $p$ we construct a standard graded $F$-rational ring $R$, over a field $K$ of characteristic $p$, such that $R\otimes_K\overline{K}$ is not $F$-rational. By localizing we obtain a flat local homomorphism $(R, \mathfrak{m}) \to (S, \mathfrak{n})$ such that $R$ is $F$-rational, $S/\mathfrak{m} S$ is regular (in fact, a field), but $S$ is not $F$-rational. In the process we also obtain standard graded $F$-rational rings $R$ for which $R\otimes_K R$ is not $F$-rational.Comment: Comments welcome

SAP: Stall-aware pacing for improved DASH video experience in cellular networks

The dramatic growth of cellular video traffic represents a practical challenge for cellular network operators in providing a consistent streaming Quality of Experience (QoE) to their users. Satisfying this objective has so-far proved elusive, due to the inherent system complexities that degrade streaming performance, such as variability in both video bitrate and network conditions. In this paper, we present SAP as a DASH video traffic management solution that reduces playback stalls and seeks to maintain a consistent QoE for cellular users, even those with diverse channel conditions. SAP achieves this by leveraging both network and client state information to optimize the pacing of individual video flows. We extensively evaluate SAP performance using real video content and clients, operating over a simulated LTE network. We implement state-of-the-art client adaptation and traffic management strategies for direct comparison. Our results, using a heavily loaded base station, show that SAP reduces the number of stalls and the average stall duration per session by up to 95%. Additionally, SAP ensures that clients with good channel conditions do not dominate available wireless resources, evidenced by a reduction of up to 40% in the standard deviation of the QoE metric