A new unit distribution: properties, estimation, and regression analysis.

This research commences a unit statistical model named power new power function distribution, exhibiting a thorough analysis of its complementary properties. We investigate the advantages of the new model, and some fundamental distributional properties are derived. The study aims to improve insight and application by presenting quantitative and qualitative perceptions. To estimate the three unknown parameters of the model, we carefully examine various methods: the maximum likelihood, least squares, weighted least squares, Anderson-Darling, and Cramér-von Mises. Through a Monte Carlo simulation experiment, we quantitatively evaluate the effectiveness of these estimation methods, extending a robust evaluation framework. A unique part of this research lies in developing a novel regressive analysis based on the proposed distribution. The application of this analysis reveals new viewpoints and improves the benefit of the model in practical situations. As the emphasis of the study is primarily on practical applications, the viability of the proposed model is assessed through the analysis of real datasets sourced from diverse fields.

Weighted power Maxwell distribution: Statistical inference and COVID-19 applications.

During the course of this research, we came up with a brand new distribution that is superior; we then presented and analysed the mathematical properties of this distribution; finally, we assessed its fuzzy reliability function. Because the novel distribution provides a number of advantages, like the reality that its cumulative distribution function and probability density function both have a closed form, it is very useful in a wide range of disciplines that are related to data science. One of these fields is machine learning, which is a sub field of data science. We used both traditional methods and Bayesian methodologies in order to generate a large number of different estimates. A test setup might have been carried out to assess the effectiveness of both the classical and the Bayesian estimators. At last, three different sets of Covid-19 death analysis were done so that the effectiveness of the new model could be demonstrated.

The Impact of COVID-19 on Education: A Meta-Narrative Review.

The rapid and unexpected onset of the COVID-19 global pandemic has generated a great degree of uncertainty about the future of education and has required teachers and students alike to adapt to a new normal to survive in the new educational ecology. Through this experience of the new educational ecology, educators have learned many lessons, including how to navigate through uncertainty by recognizing their strengths and vulnerabilities. In this context, the aim of this study is to conduct a bibliometric analysis of the publications covering COVID-19 and education to analyze the impact of the pandemic by applying the data mining and analytics techniques of social network analysis and text-mining. From the abstract, title, and keyword analysis of a total of 1150 publications, seven themes were identified: (1) the great reset, (2) shifting educational landscape and emerging educational roles (3) digital pedagogy, (4) emergency remote education, (5) pedagogy of care, (6) social equity, equality, and injustice, and (7) future of education. Moreover, from the citation analysis, two thematic clusters emerged: (1) educational response, emergency remote education affordances, and continuity of education, and (2) psychological impact of COVID-19. The overlap between themes and thematic clusters revealed researchers' emphasis on guaranteeing continuity of education and supporting the socio-emotional needs of learners. From the results of the study, it is clear that there is a heightened need to develop effective strategies to ensure the continuity of education in the future, and that it is critical to proactively respond to such crises through resilience and flexibility.

Design considerations in emergency remote teaching during the COVID-19 pandemic: a human-centered approach.

This paper is in response to the article entitled "The process of designing for learning: understanding university teachers' design work" (Bennett et al., Educ Tech Res Dev 65:125-145, 2017). Bennett et al. (Educ Tech Res Dev 65:125-145) present a descriptive model of the design process that reports findings from a qualitative study investigating the design processes of 30 instructors from 16 Australian universities through semi-structured interviews. This exploratory study provides rich, contextualized descriptions about university teachers' design process and pinpoints key design characteristics as top-down, breadth-first, iterative, responsive, and reflective. These key design characteristics revealed by the rich contextual descriptions could provide applicable insights into the design process especially for new instructors. The findings of the study could inform how learning design could be adapted during an emergency remote teaching (ERT) as it is dynamic and open to revision. A noteworthy limitation of the study is that complementary data such as design artifacts could be utilized to ensure data triangulation in addition to self-reported data obtained via interviews. The study found that university instructors' design process did not appear to draw on instructional design models. Therefore, future studies could focus on to what extent and how such models could be used by university instructors. Lastly, future studies may explore how technology is used in ERT design to support their needs. In this article, I share how design can be informed by humanizing pedagogy and pedagogy of care during ERT.