ABSTRACT
Digital Literacy has become a pressing capability because of the infusion of digital technology in education systems due to the changes brought by COVID-19. Whether a student possesses digital literacy skills that are essential for online learning can be especially significant in an examination-oriented education system. In this qualitative study, we aimed to use the Digital Literacy Global Framework as our theoretical reference to 1) identify the digital literacy skills that students demonstrate in online exams, and 2) investigate their consistency in applying the identified skills. We utilized convenience sampling strategy to collect our data from a Hong Kong secondary school and we used content analysis to analyze our data. The validity and reliability of our data analysis were ensured by high intercoder agreement rates and Cohen's Kappa statistics. We found ten digital literacy skills of basic operation of devices and software that students demonstrate. In addition, some identified digital literacy skills are applied more consistently by students than other identified digital literacy skills. We discussed the findings in the context of implications to help researchers and professionals identify and assess essential digital literacy skills for online assessments. Suggestions for future studies were also made in the conclusions. © 2021 ACM.
ABSTRACT
Digital models that simulate the dynamics of a system are increasingly used to make predictions about the future. Although modeling has been central to decision-making under conditions of uncertainty across many industries for many years, the COVID-19 pandemic has made the role that models play in prediction and policymaking real for millions of people around the world. Despite the fact that modeling is a process through which experts use data and statistics to make sophisticated guesses, most consumers expect a model's predictions to be like crystal balls and provide perfect information about what the future will bring. Over the last decade, we have conducted a series of in-depth, longitudinal studies of digital modeling across several industries. From these studies, we share five lessons we have learned about modeling that demonstrate (1) why models are indeed not crystal balls and (2) why, despite their indeterminacy, people tend to treat them as crystal balls anyway. We discuss what each of these lessons can teach us about how to respond to the predictions made by COVID-19 models as well models of other stochastic processes and events about whose futures we wish to know today. © 2021 Elsevier Ltd
ABSTRACT
PURPOSE: Our aim was to study the association between abnormal findings on chest and brain imaging in patients with coronavirus disease 2019 (COVID-19) and neurologic symptoms. MATERIALS AND METHODS: In this retrospective, international multicenter study, we reviewed the electronic medical records and imaging of hospitalized patients with COVID-19 from March 3, 2020, to June 25, 2020. Our inclusion criteria were patients diagnosed with Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) infection with acute neurologic manifestations and available chest CT and brain imaging. The 5 lobes of the lungs were individually scored on a scale of 0-5 (0 corresponded to no involvement and 5 corresponded to >75% involvement). A CT lung severity score was determined as the sum of lung involvement, ranging from 0 (no involvement) to 25 (maximum involvement). RESULTS: A total of 135 patients met the inclusion criteria with 132 brain CT, 36 brain MR imaging, 7 MRA of the head and neck, and 135 chest CT studies. Compared with 86 (64%) patients without acute abnormal findings on neuroimaging, 49 (36%) patients with these findings had a significantly higher mean CT lung severity score (9.9 versus 5.8, P < .001). These patients were more likely to present with ischemic stroke (40 [82%] versus 11 [13%], P < .0001) and were more likely to have either ground-glass opacities or consolidation (46 [94%] versus 73 [84%], P = .01) in the lungs. A threshold of the CT lung severity score of >8 was found to be 74% sensitive and 65% specific for acute abnormal findings on neuroimaging. The neuroimaging hallmarks of these patients were acute ischemic infarct (28%), intracranial hemorrhage (10%) including microhemorrhages (19%), and leukoencephalopathy with and/or without restricted diffusion (11%). The predominant CT chest findings were peripheral ground-glass opacities with or without consolidation. CONCLUSIONS: The CT lung disease severity score may be predictive of acute abnormalities on neuroimaging in patients with COVID-19 with neurologic manifestations. This can be used as a predictive tool in patient management to improve clinical outcome.