Explore the vast range of titles available.

This study analyzes various publications of scientists on the training of future IT specialists and the features of training programming using online simulators. The authors of the article made a comparative description of different online platforms for teaching programming according to certain criteria, selected interesting tasks from the online platform hackerrank.com, which have already been used to teach students. Online programming simulators have significant potential in organizing an effective distance learning system in Ukrainian universities. It is important to use online simulators in the learning process as an additional tool for the formation of professional competencies, which provides more intensive involvement of students in the process of writing code and practical (situational) application of existing knowledge. Gamification of the process of training future IT specialists helps to increase cognitive activity, and hence – the quality of the educational process and distance learning in particular. The authors recommend the use of online programming simulators as an additional tool for teaching computer science disciplines, taking into account their functionality, as well as the level of preparation of students and the expected learning outcomes.

The Coronavirus Disease 2019 (COVID-19) pandemic has caused an unprecedented disruption in medical education and healthcare systems worldwide. The disease can cause life-threatening conditions and it presents challenges for medical education, as instructors must deliver lectures safely, while ensuring the integrity and continuity of the medical education process. It is therefore important to assess the usability of online learning methods, and to determine their feasibility and adequacy for medical students. We aimed to provide an overview of the situation experienced by medical students during the COVID-19 pandemic, and to determine the knowledge, attitudes, and practices of medical students regarding electronic medical education. A cross-sectional survey was conducted with medical students from more than 13 medical schools in Libya. A paper-based and online survey was conducted using email and social media. The survey requested demographic and socioeconomic information, as well as information related to medical online learning and electronic devices; medical education status during the COVID-19 pandemic; mental health assessments; and e-learning knowledge, attitudes, and practices. A total of 3,348 valid questionnaires were retrieved. Most respondents (64.7%) disagreed that e-learning could be implemented easily in Libya. While 54.1% of the respondents agreed that interactive discussion is achievable by means of e-learning. However, only 21.1% agreed that e-learning could be used for clinical aspects, as compared with 54.8% who disagreed with this statement and 24% who were neutral. Only 27.7% of the respondents had participated in online medical educational programs during the COVID-19 pandemic, while 65% reported using the internet for participating in study groups and discussions. There is no vaccine for COVID-19 yet. As such, the pandemic will undeniably continue to disrupt medical education and training. As we face the prospect of a second wave of virus transmission, we must take certain measures and make changes to minimize the effects of the COVID-19 outbreak on medical education and on the progression of training. The time for change is now, and there should be support and enthusiasm for providing valid solutions to reduce this disruption, such as online training and virtual clinical experience. These measures could then be followed by hands-on experience that is provided in a safe environment.

Coronavirus Disease 2019 is a pandemic that is straining healthcare resources, mainly hospital beds. Multiple risk factors of disease progression requiring hospitalization have been identified, but medical decision-making remains complex. To characterize a large cohort of patients hospitalized with COVID-19, their outcomes, develop and validate a statistical model that allows individualized prediction of future hospitalization risk for a patient newly diagnosed with COVID-19. Retrospective cohort study of patients with COVID-19 applying a least absolute shrinkage and selection operator (LASSO) logistic regression algorithm to retain the most predictive features for hospitalization risk, followed by validation in a temporally distinct patient cohort. The final model was displayed as a nomogram and programmed into an online risk calculator. One healthcare system in Ohio and Florida. All patients infected with SARS-CoV-2 between March 8, 2020 and June 5, 2020. Those tested before May 1 were included in the development cohort, while those tested May 1 and later comprised the validation cohort. Demographic, clinical, social influencers of health, exposure risk, medical co-morbidities, vaccination history, presenting symptoms, medications, and laboratory values were collected on all patients, and considered in our model development. 4,536 patients tested positive for SARS-CoV-2 during the study period. Of those, 958 (21.1%) required hospitalization. By day 3 of hospitalization, 24% of patients were transferred to the intensive care unit, and around half of the remaining patients were discharged home. Ten patients died. Hospitalization risk was increased with older age, black race, male sex, former smoking history, diabetes, hypertension, chronic lung disease, poor socioeconomic status, shortness of breath, diarrhea, and certain medications (NSAIDs, immunosuppressive treatment). Hospitalization risk was reduced with prior flu vaccination. Model discrimination was excellent with an area under the curve of 0.900 (95% confidence interval of 0.886-0.914) in the development cohort, and 0.813 (0.786, 0.839) in the validation cohort. The scaled Brier score was 42.6% (95% CI 37.8%, 47.4%) in the development cohort and 25.6% (19.9%, 31.3%) in the validation cohort. Calibration was very good. The online risk calculator is freely available and found at https://riskcalc.org/COVID19Hospitalization/. Retrospective cohort design. Our study crystallizes published risk factors of COVID-19 progression, but also provides new data on the role of social influencers of health, race, and influenza vaccination. In a context of a pandemic and limited healthcare resources, individualized outcome prediction through this nomogram or online risk calculator can facilitate complex medical decision-making.

As continued COVID-19 disruption looks likely across the world, perhaps until 2021, contingency plans are evolving in case of further disruption in the 2020-2021 academic year. This includes delivering face-to-face programs fully online for at least part of the upcoming academic year for new and continuing cohorts. This temporary pivot will necessitate distance teaching and learning across almost every conceivable pedagogy, from fundamental degrees to professionally accredited ones. Each institution, program, and course will have its own myriad of individualized needs; however, there is a common question that unites us all: how do we provide teaching and assessment to students in a manner that is accessible, fair, equitable, and provides the best learning whilst acknowledging the temporary nature of the pivot? No \"one size fits all\" solution exists, and many of the choices that need to be made will be far from simple; however, this paper provides a starting point and basic principles to facilitate discussions taking place around the globe by balancing what we know from the pedagogy of online learning with the practicalities imposed by this crisis and any future crises.

The ability to build progressively on the achievements of earlier generations is central to human uniqueness, but experimental investigations of this cumulative cultural evolution lack real-world complexity. Here, we studied the dynamics of cumulative culture using a large-scale data set from online collaborative programming competitions run over 14 years. We show that, within each contest population, performance increases over time through frequent ‘tweaks’ of the current best entry and rare innovative ‘leaps’ (successful tweak:leap ratio = 16:1), the latter associated with substantially greater variance in performance. Cumulative cultural evolution reduces technological diversity over time, as populations focus on refining high-performance solutions. While individual entries borrow from few sources, iterative copying allows populations to integrate ideas from many sources, demonstrating a new form of collective intelligence. Our results imply that maximising technological progress requires accepting high levels of failure. The cumulative development of culture has proven difficult to study in the laboratory. Here, the authors examine entries to a series of large programming contests to show that successful entries are usually ‘tweaks’ of existing solutions, but occasional ‘leaps’ can bring larger benefits.

A variety of studies have demonstrated gains in cognitive ability following cognitive training interventions. However, other studies have not shown such gains, and questions remain regarding the efficacy of specific cognitive training interventions. Cognitive training research often involves programs made up of just one or a few exercises, targeting limited and specific cognitive endpoints. In addition, cognitive training studies typically involve small samples that may be insufficient for reliable measurement of change. Other studies have utilized training periods that were too short to generate reliable gains in cognitive performance. The present study evaluated an online cognitive training program comprised of 49 exercises targeting a variety of cognitive capacities. The cognitive training program was compared to an active control condition in which participants completed crossword puzzles. All participants were recruited, trained, and tested online (N = 4,715 fully evaluable participants). Participants in both groups were instructed to complete one approximately 15-minute session at least 5 days per week for 10 weeks. Participants randomly assigned to the treatment group improved significantly more on the primary outcome measure, an aggregate measure of neuropsychological performance, than did the active control group (Cohen's d effect size = 0.255; 95% confidence interval = [0.198, 0.312]). Treatment participants showed greater improvements than controls on speed of processing, short-term memory, working memory, problem solving, and fluid reasoning assessments. Participants in the treatment group also showed greater improvements on self-reported measures of cognitive functioning, particularly on those items related to concentration compared to the control group (Cohen's d = 0.249; 95% confidence interval = [0.191, 0.306]). Taken together, these results indicate that a varied training program composed of a number of tasks targeted to different cognitive functions can show transfer to a wide range of untrained measures of cognitive performance. ClinicalTrials.gov NCT-02367898.

PurposeIn the co-creation process from a network perspective, service is produced, designed, and evaluated entirely by the actors with dynamic roles and with less participation by the firm's employees in the service process. The purpose of this paper is to develop a theoretical model that represents environmental stimuli and value perceptions that contribute to service co-creation behaviour in an online network.Design/methodology/approachA total of 36 semi-structured interviews were conducted with members of two online programming communities – GitHub and Stack Overflow co-creators, with the data analysed using thematic analysis. The stimulus-organism-response model guided the development of the final model.FindingsSocial influence and trust are influential in actor value perceptions, including primary and network value, the interplay of which leads actors to co-production, supportive, and administrative behaviour. Environmental factors do not directly drive actors; rather it is the value that initiates and drives actors, which, by extension, initiates and drives the co-creation of services.Research limitations/implicationsThe service co-creation behaviour model provides a basis for future research in the co-creation and co-destruction context to model behaviours within the online network organisation setting and thereby enable improvement of such systems. This model can be operationalised in a network environment through design features.Originality/valueThis paper provides a rich understanding of environmental stimuli and value perception factors that contribute to the co-creation of services, and identifies different types of behaviours in dynamic online networks. This paper presents a new model of different types of behaviours emerging from actor participation in the co-creation process.

In South Korea, in 2019, approximately 45.5% of newly-graduated nurses quit their jobs within one year of employment. To better understand the adjustment to nursing practice upon graduation, we developed an online communication skills training program based on nonviolent communication and evaluated its effectiveness. A quasi-experimental design was adopted. The sample included 28 participants in the experimental group and 27 in the control group after one participant in the control group dropped out. The participants were fourth-year nursing students at the K and S University in Gyeonggi Province, South Korea, with some clinical training in a hospital setting. Data were analyzed using the χ 2 test, Fisher’s exact test, and independent t-test. Participants’ empathy, communication skills, anger, and self-efficacy were assessed before and after the training, as well as across the two groups. The experimental group showed significantly higher levels of empathy, communication skills, and self-efficacy compared to the control group after the program completion. However, there were no significant differences in anger. This study suggests the effectiveness of the online version of the nonviolent communication training. Therefore, providing this program to nursing students scheduled for graduation may help retain newly-graduated nurses.

Early prediction of student performance in online programming courses is essential for implementing timely interventions to enhance academic outcomes. This study aimed to predict academic success by comparing four machine learning models: Logistic Regression, Random Forest, Support Vector Machine (SVM), and Neural Network (Multilayer Perceptron, MLP). We analyzed data from the Moodle Learning Management System (LMS) and external factors of 591 students enrolled in online object-oriented programming courses at the Universidad Estatal de Milagro (UNEMI) between 2022 and 2023. The data were preprocessed to address class imbalance using the synthetic minority oversampling technique (SMOTE), and relevant features were selected based on Random Forest importance rankings. The models were trained and optimized using Grid Search with cross-validation. Logistic Regression achieved the highest Area Under the Receiver Operating Characteristic Curve (AUC-ROC) on the test set (0.9354), indicating strong generalization capability. SVM and Neural Network models performed adequately but were slightly outperformed by the simpler models. These findings suggest that integrating LMS data with external factors enhances early prediction of student success. Logistic Regression is a practical and interpretable tool for educational institutions to identify at-risk students, and to implement personalized interventions.

This study aimed to use a concept mapping methodology to develop a European framework of the needs of parents/guardians (P/G) for supporting athletes combining sport and education (dual career, DC). By means of a concept mapping methodology, 337 French, Irish, Italian, Portuguese, and Slovenian parents sorted and rated 80 potential statements associated to parenting DC athletes. Five distinct clusters emerged: 1. P/G' roles, needs and awareness to support athletes, including 22 statements (mean:3.7; range: 3.2-4.2 pt); 2. Requirements for effective planning of DC pathway, including 19 statements (mean:3.7; range: 3.2-4.5 pt); 3. Educational opportunity, including 13 statements (mean:3.5; range: 3.1-4.0 pt); 4. Policy and provision for DC, including 19 statements (mean:3.7; range: 3.1-4.2 pt); and 5. Athletes' lifestyle & self-management, including 7 statements (mean:4.0; range: 3.5-4.5 pt). Estimates of effect size (Partial eta-squared) were calculated for ANOVAs to assess the degree of variability on the statement importance ranking as the dependent variable accounted for by the demographic data. The concept mapping showed good validity (stress value: 0.11) and high reliability (rSHT: 0.99, rSHM: 0.98; rRR:0.98). One-third of the statements indicated differences (p<0.05) in relation to the P/Gs' gender and the athletes' education level, competition level and sport typology. In synthesizing the opinions, experience and needs of P/Gs of DC athletes the present framework provided sound theoretical underpinnings to inform the development of an online educational programme for empowering parenting DC athletes (https://edu.empatiasport.eu/eng/), as well as be a foundation for future Pan-European DC research on how these statements interact with each other, in different European contexts.