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  1. The COVID-19 pandemic disrupted many school accountability systems that rely on student-level achievement data. Many states encountered uncertainty about how to meet federal accountability requirements without typical school data. Prior research provides evidence that student achievement is correlated to students’ social background, which raises concerns about the predictive bias of accountability systems. This mixed-methods study (a) examines the predictive ability of non-achievement-based variables (i.e., students’ social background) on school districts’ report card letter grade in Ohio, and (b) explores educators’ perceptions of report card grades. Results suggest that social background and community demographic variables have a significant impact on measures of school accountability. 
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  2. The COVID-19 pandemic disrupted many school accountability systems that rely on student-level achievement data. Many states encountered uncertainty about how to meet federal accountability requirements without typical school data. Prior research provides evidence that student achievement is correlated to students’ social background, which raises concerns about the predictive bias of accountability systems. This mixed-methods study (a) examines the predictive ability of non-achievement-based variables (i.e., students’ social background) on school districts’ report card letter grade in Ohio, and (b) explores educators’ perceptions of report card grades. Results suggest that social background and community demographic variables have a significant impact on measures of school accountability. 
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  3. The COVID-19 pandemic disrupted many school accountability systems that rely on student- level achievement data. Many states have encountered uncertainty about how to meet federal accountability requirements without typical school data. Prior research provides an abundance of evidence that student achievement is correlated to students' social background, which raises concerns about the predictive bias of accountability systems. The focus of this quantitative study is to explore the predictive ability of non-achievement based variables (i.e., students' social background) on measures of school accountability in one Midwest state. Results suggest that social background and community demographic variables have a significant impact on measures of school accountability, and might be interpreted cautiously. Implications for policy and future research are discussed. 
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  4. Abstract

    The direct search for dark matter in the form of weakly interacting massive particles (WIMP) is performed by detecting nuclear recoils produced in a target material from the WIMP elastic scattering. The experimental identification of the direction of the WIMP-induced nuclear recoils is a crucial asset in this field, as it enables unmistakable modulation signatures for dark matter. The Recoil Directionality (ReD) experiment was designed to probe for such directional sensitivity in argon dual-phase time projection chambers (TPC), that are widely considered for current and future direct dark matter searches. The TPC of ReD was irradiated with neutrons at the INFN Laboratori Nazionali del Sud. Data were taken with nuclear recoils of known directions and kinetic energy of 72 keV, which is within the range of interest for WIMP-induced signals in argon. The direction-dependent liquid argon charge recombination model by Cataudella et al. was adopted and a likelihood statistical analysis was performed, which gave no indications of significant dependence of the detector response to the recoil direction. The aspect ratioRof the initial ionization cloud is$$R < 1.072$$R<1.072with 90 % confidence level.

     
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  5. Self-driving vehicles are the latest innovation in improving personal mobility and road safety by removing arguably error-prone humans from driving-related tasks. Such advances can prove especially beneficial for people who are blind or have low vision who cannot legally operate conventional motor vehicles. Missing from the related literature, we argue, are studies that describe strategies for vehicle design for these persons. We present a case study of the participatory design of a prototype for a self-driving vehicle human-machine interface (HMI) for a graduate-level course on inclusive design and accessible technology. We reflect on the process of working alongside a co-designer, a person with a visual disability, to identify user needs, define design ideas, and produce a low-fidelity prototype for the HMI. This paper may benefit researchers interested in using a similar approach for designing accessible autonomous vehicle technology. INTRODUCTION The rise of autonomous vehicles (AVs) may prove to be one of the most significant innovations in personal mobility of the past century. Advances in automated vehicle technology and advanced driver assistance systems (ADAS) specifically, may have a significant impact on road safety and a reduction in vehicle accidents (Brinkley et al., 2017; Dearen, 2018). According to the Department of Transportation (DoT), automated vehicles could help reduce road accidents caused by human error by as much as 94% (SAE International, n.d.). In addition to reducing traffic accidents and saving lives and property, autonomous vehicles may also prove to be of significant value to persons who cannot otherwise operate conventional motor vehicles. AVs may provide the necessary mobility, for instance, to help create new employment opportunities for nearly 40 million Americans with disabilities (Claypool et al., 2017; Guiding Eyes for the Blind, 2019), for instance. Advocates for the visually impaired specifically have expressed how “transformative” this technology can be for those who are blind or have significant low vision (Winter, 2015); persons who cannot otherwise legally operate a motor vehicle. While autonomous vehicles have the potential to break down transportation 
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  6. Free, publicly-accessible full text available June 1, 2024
  7. Free, publicly-accessible full text available October 1, 2024
  8. null (Ed.)