Key factors influencing teachers' motivation to transfer technology-enabled educational innovation.

In response to the digital transformation in education, teachers are expected to develop new competencies. Although teachers gained valuable experience in digital technology use during the COVID-19 pandemic, research and practice show that primary school teachers need to be supported and trained for the new normal of innovative, advanced use and adoption of digital technologies in educational practice. This study aims to identify the key factors that influence teachers' motivation to transfer technology-enabled educational innovation in primary education. The Learning Transfer System Inventory (LTSI) factors and the adoption factors of technology-enabled educational innovation have been conceptually mapped. The LTSI model has been empirically validated with data collected from 12.7% of Lithuanian primary school teachers. The structural equation modeling technique was utilized to analyze causal relationships of factors influencing teachers' motivation to transfer technology-enabled educational innovation. The qualitative research method was used to provide a deeper understanding of key factors that influence motivation to transfer. The conducted analysis shows that motivation to transfer is significantly influenced by all five domains of factors: perceived value, personal characteristics, social practices, organizational and technology-enabled innovation factors. Motivation to transfer innovation varies according to teachers' perceived digital technology integration skills, which underpin the importance of applying different roles and strategies based on the teachers' skills. This study provides implications for designing effective professional development for in-service teachers and creating a suitable environment in schools for the adoption of innovation in post-COVID-19 education.

From expert to novice and back: a qualitative study of interprofessional collaboration and the experiences of frontline healthcare professionals during the first wave of COVID-19.

BACKGROUND: The global coronavirus disease 2019 pandemic put extreme pressure on healthcare systems worldwide, forcing a heavy workload on healthcare professionals. Frontline treatment and care for patients with coronavirus disease 2019 compelled healthcare professionals to rapidly adapt to new working conditions. This study explores the experiences of frontline healthcare professionals to learn more about how frontline work affects their learning and skills development but also interprofessional collaboration during a pandemic. METHODS: In-depth, one-to-one semi-structured interviews were conducted with 22 healthcare professionals. A broad interdisciplinary group, the participants were employed in public hospitals in four of Denmark's five regions. Using a reflexive methodology for the data analysis allowed reflexive interpretation when interpreting subjects and interpreting the interpretation. RESULTS: The study identified two empirical themes: into the unknown and in the same boat, which we critically interpreted using learning theory and theory on interprofessionalism. The study found that the healthcare professionals moved from being experts in their own fields to being novices in the frontline of the pandemic, and then back to being experts based on interprofessional collaboration that included shared reflection. Working in the frontline was imbued with a unique atmosphere in which workers were equals and functioned interdependently, the barriers normally obstructing interprofessional collaboration set aside to focus on combating the pandemic. CONCLUSIONS: This study reveals new insights regarding knowledge on frontline healthcare professionals in terms of learning and developing new skills, as well as the importance of interprofessional collaboration. The insights contributed to the understanding of the importance of shared reflection and how the development of expertise was a socially embedded process where discussions were possible without fear of being ridiculed and healthcare professionals were willing to share their knowledge.

Face Mask Detection Using SSD-Mobilenet-V2

Mask detection plays a major role in the prevention and control of epidemics after the COVID-19 outbreak as it is the most practical and effective method of prevention. For the appropriate employees, a great automatic real-time face mask identification system based on deep learning can significantly lessen work-related stress. The systems for mask identification that are currently in use, however, are largely resource-intensive and do not strike a reasonable balance between speed and accuracy. In our system, the mask detector is SSD, and to extract the image's features and decrease a number of parameters, MobileNet takes the role of VGG-16. Pre-trained models from other domains are transferred to our model using transfer learning techniques. © 2023 IEEE.

A Virtual Reality Lab for Automotive Service Specialists: A Knowledge Transfer System in the Digital Age

Under the influence of the COVID-19 pandemic, there is an accelerated transition from the traditional form of knowledge transfer to online learning. Our study of 344 automotive students showed that the success of this transition depends on the readiness to introduce special digital tools for organizing knowledge and conducting practical forms of classes. In this regard, a modern digital form of organizing and transferring knowledge to automotive service engineers in the form of virtual laboratories was developed and presented in the article. The work scenarios, functionality, and minimum technical requirements of virtual laboratories as software systems are described and reviewed in the paper. The rationale for the effectiveness of the application, based on the results of using 109 university students in training practice, is presented as a result of the research. An analysis of the distributions of the student survey results and their training progress revealed differences at the p = 0.05 significance level. This confirmed the hypothesis that the use of methods for teaching engineers special disciplines and language skills using VR technologies is much more effective than the traditional one. An increase in students' interest in learning was revealed, and their performance improved markedly. This proves that the immersive nature of VR technology makes it possible to better assimilate the studied material, increase the level of motivation of future car service specialists, and also allow the organization of the transfer of knowledge online. The very process of knowledge transfer becomes the point of acquiring new digital competencies necessary for high-tech industries. © 2023 by the authors.

Detection of Covid-19 using Transfer Learning Technique

The whole world is suffering from the wave of the novel coronavirus that causes the large-scale death of a population and is proclaimed a pandemic by WHO. As RT-PCR tests to detect Coronavirus are costly and time taking. So now these days, the purpose of the researcher is to detect these diseases with the help of Artificial Intelligence or Machine learning-based models using CT scan images and X-rays images. So the testing cost, time taken and the number of data required could be minimized. In this paper, transfer learning based on three fine-tuned models has been proposed for Covid detection. The performance of these proposed fine-tuned models has been also compared with other competing models to check the accuracy and other matrices. © 2022 IEEE.

A deep learning approach for COVID-19 detection from computed tomography scans

The classification of COVID-19 patients from chest computed tomography (CT) images is a very difficult task due to the similarities observed with other lung diseases. Based on various CT scans of COVID and non-COVID patients, the aim of this chapter is to propose a simple deep learning architecture and compare its diagnostic performance using transfer learning and several machine learning techniques that could extract COVID-19's graphical features and classify them in order to provide a clinical diagnosis ahead of the pathogenic test, thus saving critical time for disease control. We also compare our approach and show that it outperforms various previous state-of-the-art techniques. We propose a deep learning architecture for transfer learning that is just a simple modification of eight new layers on the ImageNet pretrained convolutional neural networks (CNNs) which yielded us the best test accuracy of 98.30%, F1 score of 0.982, area under the receiver operating characteristic (ROC) curve of 0.982, and kappa value of 0.964 after training. Moreover, we use the proposed architecture for feature extraction and study the performance of various classifiers on them and were able to obtain the highest test accuracy of 91.75% with K-nearest neighbors. Also, we compare multiple CNNs and machine learning models for their diagnostic potential in disease detection and suggest a much faster and automated disease detection methodology. We show that smaller and memory efficient architectures are equally good compared to deep and heavy architectures at classifying chest CTs. We also show that visual geometry group (VGG) architectures are overall the best for this task. © 2023 Elsevier Inc. All rights reserved.

COVID-DenseNet: A Deep Learning Architecture to Detect COVID-19 from Chest Radiology Images

COVID-19 has a severe risk of spreading rapidly, the quick identification of which is essential. In this regard, chest radiology images have proven to be a practical screening approach for COVID-19 affected patients. This study proposes a deep learning-based approach using DenseNet-121 to detect COVID-19 patients effectively. We have trained and tested our model on the COVIDx dataset and performed both two-class and three-class classifications, achieving 96.49% and 93.71% accuracy, respectively. By successfully utilizing transfer learning, we achieve comparable performance to the state-of-the-art method while using 15 × fewer model parameters. Moreover, we performed an interpretability analysis using Grad-CAM to highlight the most significant image regions at test time. Finally, we developed a website that takes chest radiology images as input and detects the presence of COVID-19 or pneumonia and a heatmap highlighting the infected regions. Source code for reproducing results and model weights is available. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Systematic literature review and bibliometric analysis on virtual reality and education.

The objective of this study is to identify and analyze the scientific literature with a bibliometric analysis to find the main topics, authors, sources, most cited articles, and countries in the literature on virtual reality in education. Another aim is to understand the conceptual, intellectual, and social structure of the literature on the subject and identify the knowledge base of the use of VR in education and whether it is commonly used and integrated into teaching-learning processes. To do this, articles indexed in the Main Collections of the Web of Science, Scopus and Lens were analyzed for the period 2010 to 2021. The research results are presented in two parts: the first is a quantitative analysis that provides an overview of virtual reality (VR) technology used in the educational field, with tables, graphs, and maps, highlighting the main performance indicators for the production of articles and their citation. The results obtained found a total of 718 articles of which the following were analyzed 273 published articles. The second stage consisted of an inductive type of analysis that found six major groups in the cited articles, which are instruction and learning using VR, VR learning environments, use of VR in different fields of knowledge, learning processes using VR applications or games, learning processes employing simulation, and topics published during the Covid-19 pandemic. Another important aspect to mention is that VR is used in many different areas of education, but until the beginning of the pandemic the use of this so-called "disruptive process" came mainly from students, Institutions were reluctant and slow to accept and include VR in the teaching-learning processes.

Bridging the gap between reflective learning and reflective practice through anticipatory reflection

Purpose>This paper aims to analyse student perspectives on the contribution that teaching anticipatory reflection can make to the development of their reflective practice. The project explores lived student experiences of anticipatory reflection and the value students attribute to these in helping them bridge the transfer gap between reflective learning and reflective practice.Design/methodology/approach>An interpretivist approach is taken whereby student reflections on the students' experiences of practicing anticipatory reflection in a workshop setting were analysed using template analysis to understand the value attributed to these. Students were guided through a series of exercises including visualisation of future events and the nature of future practice as well as reflective writing.Findings>Students identified multiple benefits of being taught and practising anticipatory reflection. Specifically, high levels of realism, personal relevance and engagement were reported, as well as increased confidence, self-efficacy and self-belief. In addition, the development of empathy and increases in self-awareness were common benefits of working through the process of anticipatory reflection.Originality/value>In contrast to existing retrospective approaches, here the authors focus on the future, using anticipatory reflection to inform pedagogical approaches enabling students to experience anticipatory reflection in a classroom setting. The positive value attributed to experiencing anticipatory reflection suggests that the temporal focus in teaching reflection should evolve to incorporate prospective approaches which have a valuable role to play in bridging existing transfer gaps between reflective learning and practice.

Psychometric Properties (Measurement Invariance and Latent Mean Difference across Gender) of the Learning Transfer Inventory System (LTIS) to Assess Thai Teachers' Learning Transfer of COVID-19 Prevention Guidance in the Classroom.

One of the most important ways to improve, update, and sustain teachers' skills in an institution is via training. Nonetheless, despite the resources invested in training, learners' mobilization of new learning after they return to work does not always reach expectations, in part because of a lack of learning transfer assessment tools. This study investigated the psychometric properties of the learning transfer inventory system (LTSI) in assessing the teachers' transfer of COVID-19 prevention measures in Thai public school institutions. Participants were a sample of 700 in-service teachers (females = 54.8%; mean age = 36 years, SD = 15.41) who completed training on health code guidance for COVID-19 prevention in school. Results following confirmatory factor analysis, a test of the measurement invariance and measurement of the latent mean difference across gender, of the instrument yielded support for the hypothesized 16-factor structure. Empirical support for discriminant and convergent validity was strong. Additionally, we found a significant latent mean difference between male and female teachers related to the constructs peer support, supervisor sanction, and training design. The LTSI appears to yield valid and reliable scores for measuring the learning transfer of Thai teachers following in-service training.

When knowing is not enough: A narrative exploration of how K-12 teachers make decisions about the transfer of critical competencies from professional learning to daily practice

School districts spend millions of dollars each year to provide training and learning to staff working in direct and indirect service to students (National Council on Teacher Quality, 2021). This financial commitment says nothing about what is even more important: the need for school employees and the systems in which we work to serve students more effectively. Despite vast allocations of time and money and presumably best intentions for better social and academic outcomes for students, very little data exist that reflect regular transfer and application of training/learning into professional practice (Nittler et al., 2015). By and large, schools and school systems look the same today as they did 50+ years ago despite the fact that the world looks very different and so much more is known about the cognitive process and contextual contributors involved in erudition development. Teacher application of critical competencies such as cultural responsiveness, trauma informed practices, social emotional learning and basic neuroscience in the ways they conceptualize and implement instructional practices may not be easily apparent during casual observation, yet they are inextricably linked to positive academic and social outcomes for students, thus imperative to effective professional practice. This study investigates the ways in which professional educators make decisions about the transfer and application of professional learning centered on critical competencies (soft skills) in their daily work. Narrative Inquiry (NI) provided the methodological frame for this exploratory study that through thematic analysis surfaced five key factors influencing learning transfer: Instructor/Presenter/Facilitator;Connection to Lived Experience;Relevance to Job Assignment;Alignment with Self-Identity;and COVID-19. This dissertation is available in open access at AURA (https://aura.antioch.edu ) and OhioLINK ETD Center (https://etd.ohiolink.edu). (PsycInfo Database Record (c) 2022 APA, all rights reserved)

COVID-19 Detection via Image Classification using Deep Learning on Chest X-Ray

The focus of this study is to evaluate and examine a set of deep learning transfer learning techniques applied to chest radiograph images for the classification of COVID-19, normal (healthy), and pneumonia. In this work, we have used four transfer learning models, VGG16, InceptionV3, ResNet50, and DenseNet121 for the classification tasks. Our results indicate that the VGG16 method outperforms comparative classification models in terms of accuracy, sensitivity, and specificity. The VGG16 model detects and classifies COVID-19, normal (healthy), and pneumonia with 94% test accuracy, 94% sensitivity, and 94.20% specificity. Code is publically available at: https://github.com/ayyaz-azeem/Covid19challenge.git © 2021 IEEE.