A Meta-Synthesis of Qualitative Research on Reading Intervention Classes in Secondary Schools

Background/Context: In the past two decades there have been at least 10 quantitative reviews, syntheses, or meta-analyses focused on literacy interventions in secondary schools. To date, much of this research has focused on quantifiable outcomes such as reading test scores, and few efforts have been made to synthesize studies of adolescent literacy interventions that attend to how students themselves experience those interventions and what mediates their experiences, which previous adolescent literacy research suggests should be considered alongside other outcomes. Purpose/Objective/Research Question/Focus of Study: This meta-synthesis of qualitative research highlights additional factors that are overlooked when evidence of effectiveness is defined predominantly through assessment outcomes. It contributes insights from two decades of research on reading intervention classes (RICs), which are a long-standing approach to adolescent literacy intervention. We define RICs as compulsory, yearlong courses that supplement content-area classes with the goal of improving adolescents’ reading. Grounded in sociocultural theories of literacy and learning, our research question was: How do students experience and perceive RICs? Research Design: We conducted a qualitative meta-synthesis of 21 studies published between 2000 and 2020 that (1) focused on secondary (grades 6–12) RICs in the United States and (2) included data related to students’ experiences and perspectives. Data Collection and Analysis: We followed best practices in qualitative meta-synthesis, including assembling an author team composed of researchers with expertise in RICs, identifying a research meta-question, conducting a comprehensive search, selecting and appraising relevant studies, and coding and presenting findings using qualitative techniques. Findings/Results: We found that youth’s own diverse understandings of themselves as readers and writers, combined with the extent to which they viewed their RICs as relevant, agentive, and facilitative of relationships, mediated students’ experiences and perceptions of their RICs. In addition, students across studies described placement policies and practices as confusing, frustrating, and embarrassing. Conclusions/Recommendations: By providing a perspective that extends beyond test scores, the findings highlight some of the consequences of intervention placement policies and practices for adolescents. They also address the need for educational stakeholders to expand definitions of what counts as evidence of effectiveness to inform the future development of re-mediated literacy learning opportunities for adolescents that (1) rethink curriculum and instruction to affirm students’ literacy identities, histories, and capacities, and (2) reposition youth as literacy knowers and doers.

CoLocateMe: Aggregation-based, Energy, Performance and Cost Aware VM Placement and Consolidation in Heterogeneous IaaS Clouds

In many production clouds, with the notable exception of Google, aggregation-based VM placement policies are used to provision datacenter resources energy and performance efficiently. However, if VMs with similar workloads are placed onto the same machines, they might suffer from contention, particularly, if they are competing for similar resources. High levels of resource contention may degrade VMs performance, and, therefore, could potentially increase users' costs and infrastructure's energy consumption. Furthermore, segregation-based methods result in stranded resources and, therefore, less economics. The recent industrial interest in segregating workloads opens new directions for research. In this paper, we demonstrate how aggregation and segregation-based VM placement policies lead to variabilities in energy efficiency, workload performance, and users' costs. We, then, propose various approaches to aggregation-based placement and migration. We investigate through a number of experiments, using Microsoft Azure and Google's workload traces for more than twelve thousand hosts and a million VMs, the impact of placement decisions on energy, performance, and costs. Our extensive simulations and empirical evaluation demonstrate that, for certain workloads, aggregation-based allocation and consolidation is ~9.61% more energy and ~20.0% more performance efficient than segregation-based policies. Moreover, various aggregation metrics, such as runtimes and workload types, offer variations in energy consumption and performance, therefore, users' costs.<br>

CoLocateMe: Aggregation-based, Energy, Performance and Cost Aware VM Placement and Consolidation in Heterogeneous IaaS Clouds

In many production clouds, with the notable exception of Google, aggregation-based VM placement policies are used to provision datacenter resources energy and performance efficiently. However, if VMs with similar workloads are placed onto the same machines, they might suffer from contention, particularly, if they are competing for similar resources. High levels of resource contention may degrade VMs performance, and, therefore, could potentially increase users' costs and infrastructure's energy consumption. Furthermore, segregation-based methods result in stranded resources and, therefore, less economics. The recent industrial interest in segregating workloads opens new directions for research. In this paper, we demonstrate how aggregation and segregation-based VM placement policies lead to variabilities in energy efficiency, workload performance, and users' costs. We, then, propose various approaches to aggregation-based placement and migration. We investigate through a number of experiments, using Microsoft Azure and Google's workload traces for more than twelve thousand hosts and a million VMs, the impact of placement decisions on energy, performance, and costs. Our extensive simulations and empirical evaluation demonstrate that, for certain workloads, aggregation-based allocation and consolidation is ~9.61% more energy and ~20.0% more performance efficient than segregation-based policies. Moreover, various aggregation metrics, such as runtimes and workload types, offer variations in energy consumption and performance, therefore, users' costs.<br>

Dependability in fog computing: Challenges and solutions

The tremendous increase in IoT devices and the amount of data they produced is very expensive to be processed at cloud data centers. Therefore, fog computing was introduced in 2012 by Cisco as a decentralized computing environment that is considered to be more efficient in handling such a plethora in the number of requests. Fog computing is a distributed computing paradigm that focuses on bringing data processing at the network peripheral to reduce response time and increase the quality of service. Dependability challenges of such distributed and heterogeneous computing environments are considered in this paper. Because fog computing is a new computing paradigm, several studies have been presented to tackle its challenges and issues. However, dependability in specific did not receive much attention. In the paper, we explore several solutions to increase dependability in fog computing such as fault tolerance techniques, placement policies, middleware, and data management mechanisms aiming to help system designers choose the most appropriate solution.

Demystifying the Placement Policies of the NVIDIA GPU Thread Block Scheduler for Concurrent Kernels

In this work, we empirically derive the scheduler's behavior under concurrent workloads for NVIDIA's Pascal, Volta, and Turing microarchitectures. In contrast to past studies that suggest the scheduler uses a round-robin policy to assign thread blocks to streaming multiprocessors (SMs), we instead find that the scheduler chooses the next SM based on the SM's local resource availability. We show how this scheduling policy can lead to significant, and seemingly counter-intuitive, performance degradation; for example, a decrease of one thread per block resulted in a 3.58X increase in execution time for one kernel in our experiments. We hope that our work will be useful for improving the accuracy of GPU simulators and aid in the development of novel scheduling algorithms.

Immigrant Political Representation and Local Ethnic Concentration: Evidence from a Swedish Refugee Placement Program

This study leverages population registry data from Sweden to examine whether immigrants who live in areas with a high concentration of ethnic minorities are more or less likely to be nominated for political office. It exploits a refugee placement program in place in Sweden during the late 1980s and early 1990s that restricted refugees' opportunities to freely choose their place of residence. The article presents evidence that immigrants who live in areas with a high ethnic density are less likely to be nominated for political office. The findings have important implications for local integration policies as well as refugee placement policies, as many countries consider local context when resettling refugees.

Simply the Best?

This chapter examines officer promotion and selection policies in India. It begins with a conceptual discussion of the role of civilians in this process, examining its practice in other democracies. Thereafter it describes the historical evolution in the Indian military’s officer promotion and selection policies. In doing so it makes three broad arguments. First, promotion policies are an almost exclusive military affair, and civilians have little to do with the processes. As in other established democracies, senior officer promotions are subject to the approval of political authorities; but, exceptions apart, there is little evidence that civilians have actively shaped these policies. Second, a lack of civilian guidance exacerbates parochial divisions within the military. Third, military promotion policies would be better served with greater (but informed) civilian oversight. There is a need therefore to create conditions for a dialogue on promotion, selection, and placement policies. Such a measure, however, also needs to create safeguards to prevent politicization of the military.

Giving Community College Students Choice: The Impact of Self-Placement in Math Courses

This study examines the impact of a “natural experiment” that gave students the choice to place into or out of developmental math because of an unintended mistake made by a community college. During self-placement, more students chose to enroll in gateway college- and transfer-level math courses, however, greater proportions of female, Black, and Hispanic students enrolled in the lowest levels of math relative to test-placed counterparts. Difference-in-difference estimates show that self-placement led to positive outcomes, but mostly for White, Asian, and male students. This evidence suggests areas of concern and potential for improvement for self-placement policies. Self-determination theory, behavioral decision theory, and stereotype vulnerability provide possible explanations for the observed changes.