Navigating old and new terrains of academic practice in higher education: indelible and invisible marks left from the Covid-19 lockdown

Higher education has been (re)shaped by the Covid-19 pandemic in ways which have left both indelible and invisible marks of that period. Drawing on relevant literature, and informed by an exchange catalysed through a visual narrative method, authors from four European universities engage with two reflective questions in this article: As academics, what were our experiences of our practice during the lockdown periods of the Covid-19 pandemic? What might we carry forward, resist or reimagine in landscapes of academic practice emerging in the post-Covid future? The article explores how academics experienced and demonstrated resilience and ingenuity in their academic practice during that turbulent time. Particular insights include entanglements of the personal and professional, and the importance, affordances and limitations of technology. In addition, the authors reflect on some of the ongoing challenges exacerbated by the pandemic, such as education inequalities. The article concludes by reprising the key points about what marks are left behind in the post-Covid present, and how these relate to the future in which relational pedagogy and reflexivity are entangled in the ways in which we cohabit virtual and physical academic spaces. [ FROM AUTHOR] Copyright of London Review of Education is the property of UCL Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

After the Virus: Disaster Capitalism, Digital Inequity, and Transformative Education for the Future of Schooling

The 2020 COVID-19 disaster triggered an educational crisis in the United States, deeply exacerbating the inequities present in education as schools went online. This primary impact may not be the only one, however: literature describes a secondary impact of such disasters through "disaster capitalism," in which the private sector captures the public resources of disaster-struck communities for profit. In response to these warnings, we ask how schools, families, and communities can counteract disaster capitalism for educational equity. To address this question, we first synthesize a critical framework for analyzing digital inequity in education. We then dissect the strategies disaster capitalism uses to attack the school-family-community relationship and exacerbate digital inequity in "normal" times as well as during crises. Employing the notion of community funds of knowledge, we next examine the resources schools, families, and communities can mobilize against disaster capitalism and digital inequity. Finally, guided by the concepts of generative change and transformative learning, we consider actionable practices of countering disaster capitalism for a transformative education. [ FROM AUTHOR] Copyright of Education & Urban Society is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Collecting and communicating perishable data in a post-disaster context: rapid research and rapid dissemination.

Context: Puerto Rico experienced four natural disasters in 4 years (2017-2021): Hurricanes Irma and Maria, thousands of earthquakes reaching 6.4 magnitude, and the COVID-19 pandemic. In this context, our team sought to understand the impact of disaster aid distribution on poverty and economic inequality, and their relationship to the spread of COVID-19 across Puerto Rico. Rapid research was required to ensure we could collect perishable data within this ever-changing context. Challenges: Our mixed methods design relied on both secondary and primary data. Because analyses of the former were to inform where and how to collect the latter, timing was of the essence. The data sources identified were not readily available to the public, and thus required gaining access through direct requests to government agencies. The requests coincided with a transition between administrations after an election. This resulted in unexpected delays. Once in the field, the team had to balance the rapid nature of the research with the mindful work to avoid compounding traumas experienced by participants, heightened risk for re-traumatization and fatigue, the risk of COVID-19, the digital divide, and intermittent electrical and telecommunication services. Adaptations: In response to the delayed access to secondary data, we adjusted our research question. We continued to collect data as they became available, incorporating some immediately into analyses, and cleaning and storing others for future research opportunities. To overcome ongoing trauma challenges and prevent fatigue, we recruited and hired a large temporary team, including members of communities where we collected data. By recruiting participants and co-researchers at the same time and place, we both collapsed time between these activities and increased our team's contextual competency. To adapt to challenges presented by the pandemic, we created hybrid data collection procedures where some data were collected online, and some in person, while maintaining COVID-19 protections. We used similar adaptations for dissemination. Lessons: Rapid research needs to be agile. Working within a convergence framework to investigate wicked problems had the unexpected added benefit of providing our team with a variety of disciplinary approaches which proved helpful in adapting to the changing conditions in the field. In addition to the resourcefulness of a transdisciplinary team, it is important to be willing to pivot in response to changes and to collect data where and when you can. To increase participation, opportunities need to be designed with flexibility, mindful of competing demands faced by individuals willing to collaborate. Collecting and analyzing data iteratively and utilizing local resources can enable rapid research that is rigorous and yields rich data. Contributions: Our team applied the lessons learned to structure a rapid and iterative dissemination plan. We combined member-checking with community-level dissemination, enabling us to hone findings further before presenting to policy makers and media. Rapid research creates opportunities to make data-informed program and policy adjustments when they can be most impactful. Both the media and policy makers pay closer attention to research on current events. Hence, our recommendation is to do more rapid research! The more we do, the better we will get at it, and the more accustomed community leaders, policy makers, and program designers will become to using data to inform decisions.

A comparative study of medical image enhancement algorithms and quality assessment metrics on COVID-19 CT images.

Medical imaging can help doctors in better diagnosis of several conditions. During the present COVID-19 pandemic, timely detection of novel coronavirus is crucial, which can help in curing the disease at an early stage. Image enhancement techniques can improve the visual appearance of COVID-19 CT scans and speed-up the process of diagnosis. In this study, we analyze some state-of-the-art image enhancement techniques for their suitability in enhancing the CT scans of COVID-19 patients. Six quantitative metrics, Entropy, SSIM, AMBE, PSNR, EME, and EMEE, are used to evaluate the enhanced images. Two experienced radiologists were involved in the study to evaluate the performance of the enhancement techniques and the quantitative metrics used to assess them.

Identification of COVID-19 in CT Images using Deep Learning Model

Worldwide, COVID-19 has had a substantial impact on patients and hospital systems. Early identification and diagnosis are essential for regulating the growth of COVID-19. The input CT screening images are initially segmented into various regions using the Fuzzy C-means (FCM) clustering technique. Next, region-based image quality enhancement employs a histogram equalization method. Furthermore, certain necessary data is represented in a new image using the Local Directional Number technique. Lastly, the input images are portioned with the help of a traditional convolutional neural network model. The proposed convolutional neural network based system was able to give an accuracy of 98.60%, and the results revealed that methods for detecting COVID-19 impact from CT scan images must be developed significantly before considering it as a medical choice. Moreover, many diverse datasets are essential to assess the processes in a real-world setting. © 2022 IEEE.

Diagnosis of Pulmonary Diseases from Chest X-ray Using Deep Learning Approaches

In recent years, a severe pandemic has struck worldwide with the utmost shutter, enforcing a lot of stress in the medical industry. Moreover, the increasing population has brought to light that the work bestowed upon the healthcare specialists needs to be reduced. Medical images like chest X-rays are of utmost importance for the diagnosis of diseases such as pneumonia, COVID-19, thorax, and many more. Various manual image analysis techniques are time-consuming and not always efficient. Deep learning models for neural networks are capable of finding hidden patterns, assisting the experts in specified fields. Therefore, collaborating these medical images with deep learning techniques has paved the path for enormous applications leading to the reduction of pressure embarked upon the health industry. This paper demonstrates an approach for automatic lung diagnosing of COVID-19 (coronavirus) and thorax diseases from given CXR images, using deep learning techniques. The previously proposed model uses the concept of ResNet-18, ResNet-50, and Xception algorithms. This model gives the highest accuracy of 98% without segmentation and 95% with segmentation. Whereas, the proposed model uses CNN and CLAHE algorithms which achieves an accuracy of 99.22% without segmentation and 98.39% with segmentation. Therefore, this model will be able to provide assistance to health workforces and minimize manual errors precisely. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Social media in the election during the COVID-19 pandemic in South Korea: Candidates' use of social media when offline gatherings are prohibited

Objective: : This study investigated the utilization of social media during the 2020 South Korean general election, which took place during the COVID-19 pandemic, using the equalization versus normalization framework. Methods: : This study estimated the associations between the characteristics of candidates and their respective constituencies and the use of various social media platforms by the candidates. Results: : Dominant political actors were more active social media users, supporting the normalization hypothesis. However, when considering the candidates' chances of winning the election, social media's normalizing effect was weakened. Conclusion: : This study provides new insights into the equalization versus normalization debate by analyzing social media use in a context where offline campaigning was restricted. © 2023 by the Southwestern Social Science Association.

Face Mask Detection Using Viola-Jones and Cascade Classifier

Globally, the COVID-19 coronavirus outbreak is causing chaos in human health and therefore, the healthcare sector is in serious disarray. Many precautions have been taken to prevent the spread of this disease, including the usage of masks, which is strongly recommended by the World Health Organization (WHO). This research study has used the Viola-Jones algorithm for detecting face masks, where Histogram Equalization, Unsharp Filter and Gamma Correction are used as the preferred image pre-processing techniques to improve the overall accuracy. Haar Feature Selection is applied for creating integral images and AdaBoost training is performed on these images. Cascade classifier, a machine learning-based approach, is also integrated with the base algorithm where a cascade function assists Viola-Jones in accurately detecting objects in images. A total number of 1670 images is used in this work and our system is compared with four other machine learning algorithms, where Viola-Jones outperforms these ML-based classifiers and the overall accuracy obtained is 96%. © 2022 IEEE.

Lung disease detection using Self-Attention Generative Adversarial Capsule network optimized with sun flower Optimization Algorithm

Lung cancer is the uncontrolled growth of abnormal cells in one or both lungs. This is one of the dangerous diseases. A lot of feature extraction with classification methods were discussed previously regarding this disease, but none of the methods give sufficient results, not only that, those methods have high over fitting problem, as a result, the detection accuracy was minimizing. Therefore, to overcome these issues, a Lung Disease Detection using Self-Attention Generative Adversarial Capsule Network optimized with Sun flower Optimization Algorithm (SA-Caps GAN-SFOA-LDC) is proposed in this manuscript. Initially, NIH chest X-ray image dataset is gathered through Kaggle repository to diagnose the lung disease. Then, the chests X-ray images are pre-processed by using the contrast limited adaptive histogram equalization (CLAHE) filtering method to eliminate the noise and to enhance the image quality. These pre-processed outputs are fed to feature extraction process. In the feature extraction process, the empirical wavelet transform method is used. These extracted features are given into Self-Attention based Generative Adversarial Capsule classifier for detecting the lung disease. The hyper parameters of SA-Caps GAN classifier is optimized using Sun flower Optimization Algorithm. The simulation is implemented in MATLAB. The proposed SA-Caps GAN-SFOA-LDC method attains higher accuracy 21.05%, 33.28%, 30.27%, 29.68%, 32.57% and 44.28%, Higher Precision 30.24%, 35.68%, 32.08%, 41.27%, 28.57% and 34.20%, Higher F-Score 32.05%, 31.05%, 36.24%, 30.27%, 37.59% and 22.05% analyzed with the existing methods, SVM-SMO-LDC, CNN-MOSHO-LDC, XGboost-PSO-LDC respectively. © 2022 Elsevier Ltd

Efficient Grad-Cam-Based Model for COVID-19 Classification and Detection

Corona Virus (COVID-19) is a novel virus that crossed an animal-human barrier and emerged in Wuhan, China. Until now it has affected more than 119 million people. Detection of COVID-19 is a critical task and due to a large number of patients, a shortage of doctors has occurred for its detection. In this paper, a model has been suggested that not only detects the COVID-19 using X-ray and CT-Scan images but also shows the affected areas. Three classes have been defined;COVID-19, normal, and Pneumonia for X-ray images. For CT-Scan images, 2 classes have been defined COVID-19 and non-COVID-19. For classification purposes, pre-trained models like ResNet50, VGG-16, and VGG19 have been used with some tuning. For detecting the affected areas Gradient-weighted Class Activation Mapping (GradCam) has been used. As the X-rays and ct images are taken at different intensities, so the contrast limited adaptive histogram equalization (CLAHE) has been applied to see the effect on the training of the models. As a result of these experiments, we achieved a maximum validation accuracy of 88.10% with a training accuracy of 88.48% for CT-Scan images using the ResNet50 model. While for X-ray images we achieved a maximum validation accuracy of 97.31% with a training accuracy of 95.64% using the VGG16 model. © 2023 CRL Publishing. All rights reserved.

The relationship between formative assessment and reading achievement: A multilevel analysis of students in 19 countries/regions

Despite the general consensus on the positive impact of formative assessment on student learning, researchers have not shown the underlying mechanisms between specific formative assessment strategies and academic performance on an international sample. This study examines the link between student and teacher reports of teachers' formative assessment strategies (i.e. clarifying goals and monitoring progress, providing feedback, and instructional adjustments) and students' reading achievement, based on data from 151,969 fifteen‐year‐olds in 5,225 schools in 19 countries/regions in PISA 2018 via multilevel analysis of plausible values. The results show that clarifying goals and monitoring progress, and instruction adjustments are positively linked to reading achievement, but providing feedback alone has no significant impact. These findings highlight the complexity of formative assessment as a multifaceted concept and the different impacts of formative assessment strategies on student learning. Implications for researchers and practitioners are discussed. [ FROM AUTHOR]

The uses and abuses of boredom in the classroom

Although the educational and psychological hazards of boredom are well documented, an increasing number of researchers have argued that boredom may be a helpful, rather than harmful, emotion for the growing individual. In this paper, we engage with this re‐conception of boredom and explore its implications for contemporary education: Can boredom enhance student learning, or support certain forms of it? Can it be put to use in the classroom? What are the risks involved? In addressing these questions, we show that boredom can fulfil several important psychological functions under certain special conditions. At the same time, we argue that careful attention to the moral psychology of boredom reveals that it has significant disadvantages for helping students to develop a meaningful and fulfilling relationship to subject matter in the classroom. Against the backdrop of this analysis, we discuss the concept and experience of aspiration as a potential way of tempering and eventually obviating the psychological pitfalls of boredom. In the final section, we draw out several principles of an aspirational approach to grappling with boredom in education. [ FROM AUTHOR]

Assessing the learning of pupils with special educational needs and disabilities in mainstream school physical education

In the UK, one consequence of neoliberalism has been the development of test cultures in schools and standardised assessment strategies used to judge all pupils against within and across curriculum subjects. Few studies to date have explored the influence of this on assessing the learning of pupils with special educational needs and disabilities (SEND), and none have centred physical education (PE). This study used the concept of ableism and semi‐structured interviews to explore mainstream secondary school PE teachers' views and experiences of assessing the learning of pupils with SEND. Based on the findings, we discuss the importance of schools disrupting hegemonic, ableist modes of thinking that cast pupils with SEND as being of inferior ability when compared with their peers and thus being disadvantaged by standardised, normative assessment practices. Specifically, we identify a need for senior leaders and teachers in schools to recognise the needs and capabilities of pupils with SEND, through more holistic assessment approaches that focus on social, affective, cognitive and physical learning and development. We end by discussing the significance of initial teacher education and teacher networks to support this endeavour and advocating for the amplification of the voices of pupils with SEND, given that they have expert knowledge about the perceived inclusivity of assessment in PE because they can draw upon their lived and embodied experiences. [ FROM AUTHOR]

Social violence and the trivialising effects of youth in school bullying: Extended listening to South Australian young people on bullying and violence

School bullying attracts significant research and resources globally, yet critical questions are being raised about the long‐term impact of these efforts. There is a disconnect between young people's perspectives and the long‐established psychology‐based technical definitions of school bullying dominating practice and policy in Australia. This dominant paradigm has recently been described as the first paradigm of school bullying. In contrast, this paper explores the potential for reorienting school bullying research towards the concerns of young people and away from adult‐derived technical definitions. Borrowing from paradigm two, which emphasises the social, cultural and philosophical (among others) elements of school bullying, in this paper, I approach bullying under the broad banner of 'social violence'. This approach addresses some of the inherent limitations of the first paradigm to conceptualise social and cultural dynamics. I argue that a 'social violence' approach reveals that the exclusionary effects of the social phenomenon of youth continue to be overlooked. Furthermore, the term 'violence' in bullying research could benefit from integrating contemporary sociological insights on this phenomenon. This paper draws on qualitative insights from a small group of young people in secondary schooling in South Australia gained through prolonged listening to peer conversations in a series of focus groups. In addition, 1:1 interviews were conducted pre and post the focus group series. I argue that these participants' insights reveal the exclusionary effects of youth and the employment of bullying to trivialise young people's experiences and concern for harm. There is a need to reprioritise young people's knowledge in school bullying research and the exclusionary effects of youth alongside other social forces. [ FROM AUTHOR]

Getting in, getting on, going further: Exploring the role of employers in the degree apprentice to graduate transition

Transitions from education into work, or as part of career change and development, are increasingly central to policy debate and academic inquiry. However, the role that employers play in shaping transition is often overlooked. In this paper, we examine this issue through the experiences of a graduating cohort of 'degree apprentices'. We present original analysis of new empirical data from what we believe to be the first substantive qualitative longitudinal research conducted with those experiencing this new vocational pathway in the English Apprenticeships system. Through analysis of repeat semi‐structured interviews with 22 degree apprenticeship graduates (44 interviews in total), we provide early empirical insights into experiences of this new pathway and add to existing theoretical conceptualisations of transition within the educational literature and the employer's role within it. We show that the degree apprentice to graduate transition can be broken down into three key stages: 'getting in', 'getting on' and 'going further', and that employers—at both strategic and relational levels—shape experiences at each stage. [ FROM AUTHOR]

Cultural capital, curriculum policy and teaching Latin

Latin is currently being trialled as a subject in 40 state secondary schools in England. This paper focuses on one of the justifications of this trial: that teaching Latin in state secondary schools provides students with cultural capital which in turn counters social injustice. By taking the example of Latin as a starting point, I reach two conclusions about cultural capital. The first is that providing students with cultural capital can be good for some individuals, and so justified on a case‐by‐case basis depending on context. However, this justification does not hold for curriculum policy making. My second conclusion is that in the long term, pursuing cultural capital as part of curriculum policy exacerbates the social injustices it purports to address. Wherever an activity is introduced for the sake of cultural capital rather than its educational value, educationally valuable activities risk being pushed off the curriculum, potentially degrading the educational value of the curriculum. In the case of teaching Latin, it may provide benefits to particular students, but as part of curriculum policy it risks exacerbating social injustices and undermining the educational value of school curricula. Going beyond the place of Latin on the curriculum, I argue that all appeals to cultural capital provide a poor basis for curriculum policy making. [ FROM AUTHOR]

Capturing variations in how spelling is taught in primary school classrooms in England

Mastering spelling is important for children to progress in writing. The National Curriculum in England details spelling lists linked to each year group in primary education. Assessment practices also emphasise the importance of teaching spelling. However, to date, little is known about how teachers feel about teaching spelling nor the instructional methods that they use in primary schools in England. This study addresses this gap by investigating approaches to teaching spelling. An online survey was distributed to primary‐based teaching staff with roles in supporting teaching and learning. The survey asked for information about the respondents' teaching experience and school setting, and about their attitudes and approach to teaching spelling. The survey was completed in full by 158 respondents. Approaches to teaching spelling were varied and over two‐thirds of the sample highlighted that their school did not have a spelling policy. The importance of explicit teaching of spelling was supported by the majority of teachers. This judgement was more frequent and rated more highly by teachers supporting younger children. Teachers largely reported devising their own spelling resources, highlighted that the curriculum spelling lists lack guidance for teaching spelling strategies and questioned their suitability for pupils of varying abilities. A range of spelling programmes and strategies were recorded. The findings provide insight into universal instructional approaches. Practical implications for teacher training and professional development are discussed. [ FROM AUTHOR]

Educational inequality during isolation for COVID-19 in Buenos Aires

This article presents the results regarding the issue of education from the "Survey on living conditions and infant care during the preventative and obligatory social isolation of COVID-19." This survey was filled out by a sample of families (n=4,008), whose children were in three levels of public (62.6%) and private (37.4%) education in three districts of the province of Buenos Aires, Argentina. According to the results, the majority of the families sustained communication with the educational institutions;however, cases in which difficulties manifested were linked to limitations in connectivity or lack of technological apparatuses (above all, in public schools). Due to this, the interactions among teachers and students were limited, without the possibility of establishing synchronic communications, which made it difficult to carry out high-quality virtual education over a prolonged period, an impact of the COVID-19 pandemic. Finally, this article concludes by highlighting the limitations and reach of the study in order to analyze issues relating to educational equity and thus contribute to the possibility of designing policies that improve educational access. (English) [ FROM AUTHOR]

Diagnosis and Prediction of using Machine Learning Algorithm for CT Images

COVID-19 is associated with a high mortality rate all over the world. Early detection and prevention of COVID-19 delivers better protection. This procedure employs Histogram Equalization for image preprocessing as well as feature extraction, as well as a Gradient Boosting Algorithm (GBA) classifier to determine whether a patient's condition is normal or abnormal. The classifier's performance is determined by the number of correct and incorrect classifications. Early detection and treatment of covid-19 can significantly improve the survival rate of patients with computed Tomography (CT) images of the lungs that use mathematical morphological operations. The median filter removes speckle noise from images while improving contrast. Here, active contour processing is used to locate corona virus location. © 2022 IEEE.

Pre-Processing and Enhancement Techniques for Covid-19 X-Ray and CT-Scan Medical Images

In medical diagnosis, medical imaging plays an important role, also plays a role in diagnosis and detection of chest diseases. The Pre-Processing techniques are an important step required to increase quality for Chest X-ray and CT medical images. These Techniques are used of improving the quality of the Covid19 x-ray and CT scan images. Medical images contain many unnecessary noise components in the scanned images' actual format. Some of the image preprocessing techniques are needed to eliminate certain irritating sections of an image to properly visualize the images until specifically finding the diseases.The main aim of this paper is to apply pre-processing on the images of the lungs which includes images for Covid-19, Normal and pneumonia to improve the quality of the images. Image quality improvement is accomplished by the application of filtering techniques, noise reduction and contrast enhancement techniques. The proposed technique is evaluated by using Peak signal-to-noise ratio (PSNR), Mean Square Error (MSE), and Absolute Mean Brightness Error (AMBE) to evaluate the contrast enhancement of the image that has been processed. The results show that the used technique gives better images than original images. © 2022 American Institute of Physics Inc.. All rights reserved.