This scoping review provides an overview of COVID-19 approaches to managing unanticipated school closures and available literature related to young people learning outside-of-school. A range of material has been drawn upon to highlight educational issues of this learning context, including psychosocial and emotional repercussions. Globally, while some countries opted for a mass school shut-down, many schools remained open for students from disadvantaged backgrounds. This partial closure not only enabled learning in smaller targeted groups but also offered a safe sanctuary for those who needed a regulated and secure environment. In Australia, if full school closures were to be enforced over a long period, a significant proportion of students from more vulnerable backgrounds would likely experience persistent disadvantage through a range of barriers: long-term educational disengagement, digital exclusion, poor technology management, and increased psychosocial challenges. This scoping review combines research on technology availability and learning, with analysis of the long-term educational impacts of navigating the COVID-19 disruption.
The university student population in Australia contains increasing numbers of older students returning to learning after a significant gap in their educational journey. Many are choosing to enrol online to combine their studies with other time-consuming responsibilities. This article examines the nature of this online student experience with a focus on those aged 25 and over who are the first in their families to embark on university studies. Drawing on interviews conducted with both staff and students operating in this virtual space, as well as other related research and literature, this article offers recommendations to higher education institutions and educators on ways to improve retention and ongoing participation of this cohort.
Drawing upon Bourdieu's theories of social and cultural capital, a number of studies of the higher education environment have indicated that students who are first-infamily to come to university may lack the necessary capitals to enact success. To address this issue, university transition strategies often have the primary objective of 'filling students up' with legitimate forms of cultural capital required by the institution. However, this article argues that such an approach is fundamentally flawed, as students can be either framed as deficit or replete in capitals depending on how their particular background and capabilities are perceived. Drawing on interviews conducted with first-in-family students, this article explores how one cohort considered their movement into university and how they enacted success within this environment. Utilising Yosso's Community Cultural Wealth framework, this article discusses how these individuals drew upon existing and established capital reserves in this transition to higher education.
The principles of social inclusion have been embraced by institutions across the higher education sector but their translation into practice through pedagogy is not readily apparent. This paper examines perceptions of social inclusion and inclusive pedagogies held by academic staff at an Australian university. Of specific interest were the perceptions of teaching staff with regard to diverse student populations, particularly students from low socio-economic (LSES) backgrounds, given the institution's reasonably high proportion of LSES student enrolment (14%). A mixed-method approach was utilised: (i) in-depth interviews with a representative sample of academic staff and (ii) an online survey targeting all academic staff across the institution. The results point to the dual responsibilities of students and institutions in enacting inclusivity in order to move beyond reductive standpoints that simply apportion blame.
Worldwide, there are nearly 10 million new cases of dementia annually, of which Alzheimer’s disease (AD) is the most common. New measures are needed to improve the diagnosis of individuals with cognitive impairment due to various etiologies. Here, we report a deep learning framework that accomplishes multiple diagnostic steps in successive fashion to identify persons with normal cognition (NC), mild cognitive impairment (MCI), AD, and non-AD dementias (nADD). We demonstrate a range of models capable of accepting flexible combinations of routinely collected clinical information, including demographics, medical history, neuropsychological testing, neuroimaging, and functional assessments. We then show that these frameworks compare favorably with the diagnostic accuracy of practicing neurologists and neuroradiologists. Lastly, we apply interpretability methods in computer vision to show that disease-specific patterns detected by our models track distinct patterns of degenerative changes throughout the brain and correspond closely with the presence of neuropathological lesions on autopsy. Our work demonstrates methodologies for validating computational predictions with established standards of medical diagnosis.
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