Falling is one of the most critical outcomes of loss of consciousness during triage in emergency department (ED). It is an important sign requires an immediate medical intervention. This paper presents a computer vision-based fall detection model in ED. In this study, we hypothesis that the proposed vision-based triage fall detection model provides accuracy equal to traditional triage system (TTS) conducted by the nursing team. Thus, to build the proposed model, we use MoveNet, a pose estimation model that can identify joints related to falls, consisting of 17 key points. To test the hypothesis, we conducted two experiments: In the deep learning (DL) model we used the complete feature consisting of 17 keypoints which was passed to the triage fall detection model and was built using Artificial Neural Network (ANN). In the second model we use dimensionality reduction Feature-Reduction for Fall model (FRF), Random Forest (RF) feature selection analysis to filter the key points triage fall classifier. We tested the performance of the two models using a dataset consisting of many images for real-world scenarios classified into two classes: Fall and Not fall. We split the dataset into 80% for training and 20% for validation. The models in these experiments were trained to obtain the results and compare them with the reference model. To test the effectiveness of the model, a t-test was performed to evaluate the null hypothesis for both experiments. The results show FRF outperforms DL model, and FRF has same accuracy of TTS.

The SMARTER model, an innovative educational framework, is designed for blended learning environments, seamlessly integrating both online and face-to-face instructional components. Employing a flipped classroom methodology, this model ensures an equitable division between online and traditional classroom interactions, aiming to cultivate a dynamic and collaborative learning atmosphere. This research focused on developing and rigorously evaluating the SMARTER model’s validity, practicality, and effectiveness. Adopting a research and development (R&D) approach informed by the methodologies of Borg, Gall, and Gall, this study utilized a mixed-methods strategy. This encompassed a robust validation process by experts in design, content, and media, alongside an empirical analysis of the model’s application in actual educational settings. The aim was to comprehensively assess its effectiveness and practicality. The findings from this study affirm the SMARTER model’s validity, practicality, and effectiveness in improving students’ information literacy skills. Comparative analysis between a control group, taught using a traditional expository approach, and an experimental group, educated under the SMARTER model, highlighted significant improvements in the latter group. This effectiveness underscores the model’s capacity not only to efficiently deliver content but also to actively engage students in a collaborative learning process. The results advocate for the model’s potential broader adoption and adaptation across similar educational contexts. They also establish a foundation for future research aimed at exploring the SMARTER model’s scalability and adaptability across diverse instructional environments.

The rapid shift to online learning during COVID-19 posed challenges for students. This investigation explored these hurdles and suggested effective solutions using mixed methods. By combining a literature review, interviews, surveys, and the analytic hierarchy process (AHP), the study identified five key challenges: lack of practical experience, disruptions in learning environments, condensed assessments, technology and financial constraints, and health and mental well-being concerns. Notably, it found differences in priorities among students across academic years. Freshmen struggled with the absence of hands-on courses, sophomores with workload demands, and upperclassmen with mental health challenges. The research also discussed preferred strategies for resolution, emphasizing independent learning methods, managing distractions, and adjusting assessments. By providing tailored insights, this study aimed to enhance online learning. Governments and universities should support practical work, prioritize student well-being, improve digital infrastructure, adapt assessments, foster innovation, and ensure resilience.

The article analyzes the process of formation of research universities as one of the elements of a strong innovation economy. The formation of a new university model is a global trend, successfully implemented in English-speaking countries. In Russia, the educational system is not yet ready to ensure the country’s effective competition in the innovation market. The Strategic Academic Leadership Program “Priority-2030” is designed to carry out the functional transformation of the entire infrastructure of human capital reproduction in a short period of time in Russia. The article presents an analysis of the main conditions for the development of a university with a research strategy, as well as an assessment of the implementation of this strategy by Moscow Polytechnic University. The methodological basis of the study was formed by qualitative methods: included observation and benchmarking of universities’ activities, which allowed to generalize the current global trends and best practices in the field of education. For the analysis we used the data of monitoring the activities of higher education organizations, data of official statistics, as well as data from reports and presentation materials of universities and online publications participating in the “5-100” and “Priority-2030” programs. The results of the study may be useful for researchers and practitioners engaged in the transformation of the Russian higher education system.

This study investigates the impact of various educational and social factors on the digital skills of vocational education and training (VET) students, emphasizing the significance of continuous skill development in the digital age. Utilizing structural equation modeling (SEM), the paper analyzes data from 382 adult VET students in Greece, examining the effects of Erasmus program participation, daily computer use, educational platforms, and social network engagement on digital competencies. The findings reveal that participation in Erasmus programs and the use of educational platforms significantly enhance students’ digital skills, highlighting the value of international experiences and digital learning tools in VET. Conversely, daily computer use alone does not significantly impact digital skills, suggesting that structured and purposeful digital tool integration is essential for skill development. The study also underscores the positive role of social networks in improving content management skills, advocating for their strategic use in educational settings. These results demonstrate the need for targeted digital literacy initiatives within VET programs to prepare students for modern labor market demands. The research contributes to the theoretical understanding of digital skill acquisition and offers practical insights for educators and policymakers to enhance VET curricula, fostering economic and social progress through improved digital literacy.

The study’s goal was to investigate the impact of e-learning determinants on student satisfaction and intention to use e-learning tools. The dependent and independent variables in this study were based on the technological acceptance model. The study examines three determinants, including usefulness, ease of use, and facilitating conditions, as independent variables, while student satisfaction and intention to use were used as dependent variables. Additionally, this study is unique by adding student satisfaction as a dependent variable and a mediator to examine the relationship between e-learning determinants and intention to use. A questionnaire was prepared and distributed to 324 undergraduate students from Jordan’s private universities on the basis of a convenience sample. The proposed hypotheses were investigated using the quantitative techniques of regression in SPSS and SEM in AMOS. The findings of this study revealed that student satisfaction and intention to use e-learning were positively impacted by e-learning determinants. It found that intention to use was positively impacted by student satisfaction. Furthermore, e-learning intention to use was found to be positively impacted by e-learning determinants via student satisfaction. Universities and other educational institutions are advised to identify the appropriate e-learning determinants that satisfy students’ demands and motivate them to use e-learning tools in light of the study’s findings. Private universities can accomplish their goals, stay ahead of the competition, and obtain a competitive advantage by properly understanding e-learning determinants, student satisfaction, and the application of successful e-learning solutions.