Student satisfaction with online teaching in times of COVID-19

Higher education is one of the driving forces behind the social and economic development of countries, with the ultimate aim of providing quality academic training. At present, teaching-learning models in virtual environments face a number of important challenges, particularly in the current situation caused by COVID-19. Some of these challenges will be addressed in this study. We worked with 225 third-year undergraduate students in health science degrees over two academic years during the pandemic. The objectives were: (1) to ascertain whether there were significant differences in student satisfaction with the teaching process in the first year of the pandemic (e-learning teaching) vs. the second year (b-learning teaching);(2) to determine whether there were significant differences in academic performance between the two groups. Quantitative research (using a 2x2 factorial design, ANOVA and ANCOVA) and qualitative research (using a comparative design with categorisation analysis) were carried out. The results indicate differences in some aspects of satisfaction and learning outcomes in favour of teaching in the second of the two years. Students rated the use of active methodologies and technological resources positively, although they concluded that their use required more work time. Future studies will seek to compare student satisfaction in other areas of knowledge. (PsycInfo Database Record (c) 2023 APA, all rights reserved) (Spanish) La Educacion Superior es uno de los motores del desarrollo social y economico de los paises, teniendo como objetivo ultimo el de facilitar una formacion academica de calidad. En la actualidad, los modelos de ensenanza-aprendizaje en entornos virtuales implican retos importantes, especificamente en la actual situacion por la COVID-19. Algunos de estos desafios se abordaran en este estudio. Se trabajo con 225 estudiantes de tercero de grado en titulaciones de Ciencias de la Salud, a lo largo de dos cursos academicos impartidos durante la situacion de pandemia. Los objetivos fueron: 1) comprobar si existian diferencias significativas en la satisfaccion de los estudiantes con el proceso docente respecto del primer ano de pandemia (se aplico docencia e-Learning) vs. el segundo ano (se aplico docencia b-Learning);2) comprobar si existian diferencias significativas en los resultados academicos entre ambos grupos. Se realizo una investigacion cuantitativa (se utilizo un diseno factorial 2x2, ANOVA y ANCOVA) y otra cualitativa (se utilizo un diseno comparativo con analisis de categorizacion). Los resultados indican diferencias en algunos aspectos de la satisfaccion y en los resultados de aprendizaje, a favor de la docencia en el segundo ano. Los estudiantes valoraron positivamente el uso de metodologias activas y de recursos tecnologicos, si bien concluyeron que su uso exigia mas tiempo de trabajo. Futuros estudios se dirigiran a contrastar la satisfaccion de estudiantes en otras ramas de conocimiento. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Multi-center Integrating Radiomics, Structured Reports, and Machine Learning Algorithms for Assisted Classification of COVID-19 in Lung Computed Tomography

Purpose To evaluate the classification performance of structured report features, radiomics, and machine learning (ML) models to differentiate between Coronavirus Disease 2019 (COVID-19) and other types of pneumonia using chest computed tomography (CT) scans. Methods Sixty-four COVID-19 subjects and 64 subjects with non-COVID-19 pneumonia were selected. The data was split into two independent cohorts: one for the structured report, radiomic feature selection and model building (n = 73), and another for model validation (n = 55). Physicians performed readings with and without machine learning support. The model's sensitivity and specificity were calculated, and inter-rater reliability was assessed using Cohen's Kappa agreement coefficient. Results Physicians performed with mean sensitivity and specificity of 83.4 and 64.3%, respectively. When assisted with machine learning, the mean sensitivity and specificity increased to 87.1 and 91.1%, respectively. In addition, machine learning improved the inter-rater reliability from moderate to substantial. Conclusion Integrating structured reports and radiomics promises assisted classification of COVID-19 in CT chest scans.

Measuring the impact of nonpharmaceutical interventions on the SARS-CoV-2 pandemic at a city level: An agent-based computational modelling study of the City of Natal

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic hit almost all cities in Brazil in early 2020 and lasted for several months. Despite the effort of local state and municipal governments, an inhomogeneous nationwide response resulted in a death toll amongst the highest recorded globally. To evaluate the impact of the nonpharmaceutical governmental interventions applied by different cities—such as the closure of schools and businesses in general—in the evolution and epidemic spread of SARS-CoV-2, we constructed a full-sized agent-based epidemiological model adjusted to the singularities of particular cities. The model incorporates detailed demographic information, mobility networks segregated by economic segments, and restricting bills enacted during the pandemic period. As a case study, we analyzed the early response of the City of Natal—a midsized state capital—to the pandemic. Although our results indicate that the government response could be improved, the restrictive mobility acts saved many lives. The simulations show that a detailed analysis of alternative scenarios can inform policymakers about the most relevant measures for similar pandemic surges and help develop future response protocols.

Multi-center Integrating Radiomics, Structured Reports, and Machine Learning Algorithms for Assisted Classification of COVID-19 in Lung Computed Tomography.

Purpose: To evaluate the classification performance of structured report features, radiomics, and machine learning (ML) models to differentiate between Coronavirus Disease 2019 (COVID-19) and other types of pneumonia using chest computed tomography (CT) scans. Methods: Sixty-four COVID-19 subjects and 64 subjects with non-COVID-19 pneumonia were selected. The data was split into two independent cohorts: one for the structured report, radiomic feature selection and model building (n = 73), and another for model validation (n = 55). Physicians performed readings with and without machine learning support. The model's sensitivity and specificity were calculated, and inter-rater reliability was assessed using Cohen's Kappa agreement coefficient. Results: Physicians performed with mean sensitivity and specificity of 83.4 and 64.3%, respectively. When assisted with machine learning, the mean sensitivity and specificity increased to 87.1 and 91.1%, respectively. In addition, machine learning improved the inter-rater reliability from moderate to substantial. Conclusion: Integrating structured reports and radiomics promises assisted classification of COVID-19 in CT chest scans.

Measuring the impact of nonpharmaceutical interventions on the SARS-CoV-2 pandemic at a city level: An agent-based computational modelling study of the City of Natal.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic hit almost all cities in Brazil in early 2020 and lasted for several months. Despite the effort of local state and municipal governments, an inhomogeneous nationwide response resulted in a death toll amongst the highest recorded globally. To evaluate the impact of the nonpharmaceutical governmental interventions applied by different cities-such as the closure of schools and businesses in general-in the evolution and epidemic spread of SARS-CoV-2, we constructed a full-sized agent-based epidemiological model adjusted to the singularities of particular cities. The model incorporates detailed demographic information, mobility networks segregated by economic segments, and restricting bills enacted during the pandemic period. As a case study, we analyzed the early response of the City of Natal-a midsized state capital-to the pandemic. Although our results indicate that the government response could be improved, the restrictive mobility acts saved many lives. The simulations show that a detailed analysis of alternative scenarios can inform policymakers about the most relevant measures for similar pandemic surges and help develop future response protocols.

Quantifying political influence on COVID-19 fatality in Brazil.

The COVID-19 pandemic was severely aggravated in Brazil due to its politicization by the country's federal government. However, the impact of diffuse political forces on the fatality of an epidemic is notoriously difficult to quantify. Here we introduce a method to measure this effect in the Brazilian case, based on the inhomogeneous distribution throughout the national territory of political support for the federal government. This political support is quantified by the voting rates in the last general election in Brazil. This data is correlated with the fatality rates by COVID-19 in each Brazilian state as the number of deaths grows over time. We show that the correlation between fatality rate and political support grows as the government's misinformation campaign is developed. This led to the dominance of such political factor for the pandemic impact in Brazil in 2021. Once this dominance is established, this correlation allows for an estimation of the total number of deaths due to political influence as 350±70 thousand up to the end of 2021, corresponding to (57±11)% of the total number of deaths.

Teacher Training Effectiveness in Self-Regulation in Virtual Environments

Higher education in the 21st century faces the challenge of changing the way in which knowledge is conveyed and how teachers and students interact in the teaching-learning process. The current pandemic caused by SARS-CoV-2 has hastened the need to face up to this challenge and has furthered the need to approach the issue from the perspective of digitalisation. To achieve this, it is necessary to design training programmes geared towards teaching staff and which address both the use of technology and instructional design aimed at promoting the development of self-regulated learning (SRL) and automatic feedback systems. In this study, work was carried out with 23 teachers (8 inexperienced and 15 experienced teachers) in a training programme conducted through Moodle. The aims were: (1) to test whether there were any significant differences between the behaviour patterns of new teachers compared to experienced teachers, (2) to determine whether clusters of behaviour patterns corresponded to the type of teacher and (3) to ascertain whether the level of teacher satisfaction with the training activity in digital teaching will depend on the type of teacher. A quantitative as well as a qualitative design was applied. Differences were found in the behaviour patterns in the training activities for the development of rubrics and use of learning analytics systems in virtual learning environments. It was also found that the type of teacher did not correspond exactly to the behaviour cluster in the learning platform. In addition, no significant differences were found in the level of satisfaction between the two kinds of teacher. The main contribution this study makes is to provide a detailed description of the training stage as well as the materials required for its repetition. Further analytical studies are required on teacher perception of training programmes in digital teaching in order to provide personalised training proposals that lead to an effective use of teaching in digital environments.

Student Satisfaction with Online Teaching in Times of COVID-19

Higher education is one of the driving forces behind the social and economic development of countries, with the ultimate aim of providing quality academic training. At present, teaching-learning models in virtual environments face a number of important challenges, particularly in the current situation caused by COVID-19. Some of these challenges will be addressed in this study. We worked with 225 third-year undergraduate students in health science degrees over two academic years during the pandemic. The objectives were: (1) to ascertain whether there were significant differences in student satisfaction with the teaching process in the first year of the pandemic (e-learning teaching) vs. the second year (b-learning teaching);and (2) to determine whether there were significant differences in academic performance between the two groups. Quantitative research (using a 2x2 factorial design, ANOVA and ANCOVA) and qualitative research (using a comparative design with categorisation analysis) were carried out. The results indicate differences in some aspects of satisfaction and learning outcomes in favour of teaching in the second of the two years. Students rated the use of active methodologies and technological resources positively, although they concluded that their use required more work time. Future studies will seek to compare student satisfaction in other areas of knowledge.

Promoting Success and Persistence in Pandemic Times: An Experience With First-Year Students.

The transition and adaptation of students to higher education (HE) involve a wide range of challenges that justify some institutional practices promoting skills that enable students to increase their autonomy and to face the difficulties experienced. The requirements for this adaptation were particularly aggravated by the containment and sanitary conditions associated with coronavirus disease 2019 (COVID-19). With the aim of promoting academic success and preventing dropout in the first year, a support program was implemented for students enrolled in two courses in the area of education at a public university in northern Portugal during the first semester of 2020/2021. Three sessions of 50/60 min were implemented, namely, the first session focused on the verbalization of the demands, challenges, and difficulties of the transition, and the second and third sessions focused on the difficulties of academic adaptation and academic performance. Data from a dropout risk screening instrument and from the activities performed during sessions were analyzed. The main results point to student satisfaction with the content and the activities of the sessions and their usefulness. Students report not only high satisfaction levels with HE attendance, but also some emotional exhaustion due to academic activities. The continuity of the program is recommended with some improvements in its planning to ensure a more definitive version of the program in the next two years.

Satisfacción de los estudiantes con la docencia online en tiempos de COVID-19

At present, teaching-learning models in virtual environments face a number of important challenges, particularly in the current situation caused by COVID-19. The objectives were: (1) to ascertain whether there were significant differences in student satisfaction with the teaching process in the first year of the pandemic (e-learning teaching) vs. the second year (b-learning teaching);(2) to determine whether there were significant differences in academic performance between the two groups. Online learning, technology innovation, satisfaction, project based learning, digital competence, COVID-19. 1.Introducción y estado de la cuestión La Educación Superior (ES) hoy merece una atención especial por parte de los gobiernos y la sociedad en general, ya que se la considera cada vez más como un importante motor del desarrollo social y económico de los países. En esta línea, el European Framework for the Digital Competence of Educators (DigCompEdu) diferencia seis niveles de competencia digital (Redecker & Punie, 2017): Newcomers (A1), son docentes que han tenido muy poco contacto con la utilización de herramientas digitales;Explorers (A2), son profesores que se han iniciado en el uso de las herramientas digitales, pero aún no tienen un enfoque global;Integrators (B1), son profesores que utilizan y experimentan con herramientas digitales para diversos fines, tratando de comprender qué estrategias digitales funcionan mejor en cada contexto;Experts (B2), son profesores que utilizan una serie de herramientas digitales con seguridad, creatividad y espíritu crítico con el fin de mejorar sus actividades profesionales, y amplían continuamente su repertorio de prácticas;Leaders (C1), son profesores que utilizan un amplio repertorio de estrategias digitales flexibles, completas y eficaces;y Pioneers (C2), son profesores que cuestionan la idoneidad de las prácticas digitales y pedagógicas contemporáneas, de las que ellos mismos son expertos, y al mismo tiempo lideran la innovación siendo un modelo para los profesores más jóvenes.

Usefulness of Digital Game-Based Learning in Nursing and Occupational Therapy Degrees: A Comparative Study at the University of Burgos.

Teaching in higher education in the 21st century is moving towards e-Learning or b-Learning teaching models. This situation has increased due to the SARS CoV-2 health crisis. Therefore, teaching-learning models must be based on the use of active methodologies that facilitate students' motivation to work in learning management systems (LMS). One of the most current resources is the digital game-based learning (DGBL) use, specifically in health sciences degrees (e.g., nursing). In this study, we worked with 225 third-year students of degrees in nursing (ND) and occupational therapy (OTD). The objectives were (1) to find out if there were significant differences between students who had worked with DGBL techniques vs. those who had not, and (2) to find out if there were significant differences depending on the type of degree (ND vs. OTD) regarding access to the LMS, learning outcomes and students' satisfaction with teachers' performance. A mixed-method research approach was applied. In the quantitative study, significant differences were found in the accesses to the LMS in favor of the groups that had worked with DGBL techniques. Significant differences were also found in ND students with respect to learning outcomes in the group that worked with DGBL. Regarding the results of the qualitative study, differences were found in the frequency of interaction and in the preference of DGBL activities depending on the type of degree. Further studies will investigate the possible causes of these differences.

Contribution of Higher Education Institutions to Social Innovation: Practices in Two Southern European Universities

The interactions between the higher education sector and society and industry have been attracting increased attention in terms of ways to develop social innovation solutions to societal problems. Despite calls from politicians and the existence of some guidelines, we know little about how higher education could incorporate social innovation activities into its structure and missions. This study examines some practice experiences in two southern European public universities in Portugal and Spain. We show that the third mission of universities, which includes social innovation, is both linked to the first two missions of teaching and research, depending on the university’s historical and social context. The high dependence of higher education institutions on economic returns increases the importance of political action to drive the development of social innovation activities. This conditioning factor seems to be intrinsic to some of the barriers that have been identified, such as lack of legitimization and recognition of social innovation practices at the formal governmental level.

COVID-19 in chronic myeloid leukemia patients in Latin America.

This observational, multicenter study aimed to report the clinical evolution of COVID-19 in patients with chronic myeloid leukemia in Latin America. A total of 92 patients presented with COVID-19 between March and December 2020, 26% of whom were severe or critical. The median age at COVID-19 diagnosis was 48 years (22-79 years), 32% were 60 years or older, and 61% were male. Thirty-nine patients presented with at least one comorbidity (42.3%). Eighty-one patients recovered (88%), and 11 (11.9%) died from COVID-19. There was one case of reinfection. Patients with a major molecular response presented superior overall survival compared to patients with no major molecular response (91 vs. 61%, respectively; p = 0.004). Patients in treatment-free remission and receiving tyrosine kinase inhibitors showed higher survival rates than patients who underwent hematopoietic stem cell transplantation and those who did not receive tyrosine kinase inhibitors (100, 89, 50, and 33%, respectively; p < 0.001).

COVID-19 pandemics modeling with modified determinist SEIR, social distancing, and age stratification. The effect of vertical confinement and release in Brazil.

The ongoing COVID-19 epidemics poses a particular challenge to low and middle income countries, making some of them consider the strategy of "vertical confinement". In this strategy, contact is reduced only to specific groups (e.g. age groups) that are at increased risk of severe disease following SARS-CoV-2 infection. We aim to assess the feasibility of this scenario as an exit strategy for the current lockdown in terms of its ability to keep the number of cases under the health care system capacity. We developed a modified SEIR model, including confinement, asymptomatic transmission, quarantine and hospitalization. The population is subdivided into 9 age groups, resulting in a system of 72 coupled nonlinear differential equations. The rate of transmission is dynamic and derived from the observed delayed fatality rate; the parameters of the epidemics are derived with a Markov chain Monte Carlo algorithm. We used Brazil as an example of middle income country, but the results are easily generalizable to other countries considering a similar strategy. We find that starting from 60% horizontal confinement, an exit strategy on May 1st of confinement of individuals older than 60 years old and full release of the younger population results in 400 000 hospitalizations, 50 000 ICU cases, and 120 000 deaths in the 50-60 years old age group alone. Sensitivity analysis shows the 95% confidence interval brackets a order of magnitude in cases or three weeks in time. The health care system avoids collapse if the 50-60 years old are also confined, but our model assumes an idealized lockdown where the confined are perfectly insulated from contamination, so our numbers are a conservative lower bound. Our results discourage confinement by age as an exit strategy.