Understanding teachers: The potential and possibility of discourse analysis

Understanding the ways in which teachers make sense of what they do and why is critical to a broader understanding of pedagogy. Historically, teachers have been understood through the thematic and content analysis of their beliefs or philosophies. In this paper, we argue that discourse analysis (DA) involves a much finer-grained analysis of the ‘lifeworlds’ of teachers and, in our view, provides a more detailed canvas from which inferences can be made. Our argument is structured in four parts. We begin by locating DA within the physical education (PE) literature and discuss what others have referred to as its relatively modest use. Following our location of DA, we outline a conceptual framework that we regard as useful, which contains six interrelated principles. We then introduce the idea of interpretive repertoires, which we consider to have particular explanatory power as well as being a sophisticated way to represent the subjectivities of PE teachers. Finally, we discuss the methodological strengths of interpretive repertoires. The paper concludes with a discussion on the theoretical and practical merits of adopting DA to analyse problems within PE.

Data-aware task scheduling for all-to-all comparison problems in heterogeneous distributed systems

Solving large-scale all-to-all comparison problems using distributed computing is increasingly significant for various applications. Previous efforts to implement distributed all-to-all comparison frameworks have treated the two phases of data distribution and comparison task scheduling separately. This leads to high storage demands as well as poor data locality for the comparison tasks, thus creating a need to redistribute the data at runtime. Furthermore, most previous methods have been developed for homogeneous computing environments, so their overall performance is degraded even further when they are used in heterogeneous distributed systems. To tackle these challenges, this paper presents a data-aware task scheduling approach for solving all-to-all comparison problems in heterogeneous distributed systems. The approach formulates the requirements for data distribution and comparison task scheduling simultaneously as a constrained optimization problem. Then, metaheuristic data pre-scheduling and dynamic task scheduling strategies are developed along with an algorithmic implementation to solve the problem. The approach provides perfect data locality for all comparison tasks, avoiding rearrangement of data at runtime. It achieves load balancing among heterogeneous computing nodes, thus enhancing the overall computation time. It also reduces data storage requirements across the network. The effectiveness of the approach is demonstrated through experimental studies.

ACE Research Vignette 050: What makes new migrant businesses grow? Connections or location?

This series of research vignettes is aimed at sharing current and interesting research findings from our team of international Entrepreneurship researchers. In this vignette, Dr. Roxanne Zolin and Dr. Artemis Chang consider both the role of ethnic connections and location in an ethnic neighbourhood on new migrant business growth.