E-Learning Readiness Assessment Using Machine Learning Methods

Assessing e-learning readiness is crucial for educational institutions to identify areas in their e-learning systems needing improvement and to develop strategies to enhance students' readiness. This paper presents an effective approach for assessing e-learning readiness by combining the ADKAR model and machine learning-based feature importance identification methods. The motivation behind using machine learning approaches lies in their ability to capture nonlinearity in data and flexibility as data-driven models. This study surveyed faculty members and students in the Economics faculty at Tlemcen University, Algeria, to gather data based on the ADKAR model's five dimensions: awareness, desire, knowledge, ability, and reinforcement. Correlation analysis revealed a significant relationship between all dimensions. Specifically, the pairwise correlation coefficients between readiness and awareness, desire, knowledge, ability, and reinforcement are 0.5233, 0.5983, 0.6374, 0.6645, and 0.3693, respectively. Two machine learning algorithms, random forest (RF) and decision tree (DT), were used to identify the most important ADKAR factors influencing e-learning readiness. In the results, ability and knowledge were consistently identified as the most significant factors, with scores of ability (0.565, 0.514) and knowledge (0.170, 0.251) using RF and DT algorithms, respectively. Additionally, SHapley Additive exPlanations (SHAP) values were used to explore further the impact of each variable on the final prediction, highlighting ability as the most influential factor. These findings suggest that universities should focus on enhancing students' abilities and providing them with the necessary knowledge to increase their readiness for e-learning. This study provides valuable insights into the factors influencing university students' e-learning readiness.

Build an Agent-Based Model for COVID-19 Effect of Mitigation Policies

Non-Drug Intervention (NDI) is one of the important means to prevent and control the outbreak of coronavirus disease 2019 (COVID-19), and the implementation of this series of measures plays a key role in the development of the epidemic. The purpose of this paper is to study the impact of different mitigation measures on the situation of the COVID 19, and effectively respond to the prevention and control situation in the "post-epidemic era". The present work is based on the Susceptible-Exposed-Infectious-Remove-Susceptible (SEIRS) Model, and adapted the agent-based model (ABM) to construct the epidemic prevention and control model framework to simulate the COVID-19 epidemic from three aspects: social distance, personal protection, and bed resources. The experiment results show that the above NDI are effective mitigation measures for epidemic prevention and control, and can play a positive role in the recurrence of COVID-19, but a single measure cannot prevent the recurrence of infection peaks and curb the spread of the epidemic;When social distance and personal protection rules are out of control, bed resources will become an important guarantee for epidemic prevention and control. Although the spread of the epidemic cannot be curbed, it can slow down the recurrence of the peak of the epidemic;When people abide by social distance and personal protection rules, the pressure on bed resources will be eased. At the same time, under the interaction of the three measures, not only the death toll can be reduced, but the spread of the epidemic can also be effectively curbed. © 2022 ACM.

Technological variables predictors of academic stress in nursing students in times of COVID-19

Objective: to analyze which technological variables, derived from the use of electronic devices, predict academic stress and its dimensions in Nursing students. Method: analytical cross-sectional study carried out with a total of 796 students from six universities in Peru. The SISCO scale was used and four logistic regression models were estimated for the analysis, with selection of variables in stages. Results: among the participants, 87.6% had a high level of academic stress;time using the electronic device, screen brightness, age and sex were associated with academic stress and its three dimensions;the position of using the electronic device was associated with the total scale and the stressors and reactions dimensions. Finally, the distance between the face and the electronic device was associated with the total scale and size of reactions. Conclusion: technological variables and sociodemographic characteristics predict academic stress in nursing students. It is suggested to optimize the time of use of computers, regulate the brightness of the screen, avoid sitting in inappropriate positions and pay attention to the distance, in order to reduce academic stress during distance learning.

MOOCs Usage in Russian Higher Educational Institutions: Why Is Not There Any Increasing Demand for MOOCs During Emergency Distance Learning

The abrupt transition to distance learning during the outbreak of the COVID-19 pandemic triggered an urgent need for online resources at higher education institutes (HIEs). Creating analogues of traditional full-time courses demanded for competencies and time resources. In this case ready-made massive open online courses (MOOCs) were supposed to be the most obvious and fastest solution for HIEs. However, analytics demonstrated that educational institutions did not consider MOOC a promising option. This contradiction served as an incentive to conduct this research, which includes the analysis of both non-reactive (MOOCs platform analytics) and reactive (online survey and interviews with instructors) data. Based on our research, we can conclude that the reasons for not integrating MOOCs at Russian HIEs during the COVID-19 pandemic are the following: the peculiarities of MOOCs format, low motivation of instructors, administrative risks, and the uncertainty of HIEs' and national policies on MOOCs integration. This article will be useful for those who determine educational policy in Russia, university administrators, methodologists responsible for the development of digital educational technologies in HIEs, as well as researchers of higher education. © 2023 Moscow Polytechnic University. All rights reserved.

Examination of Variables Affecting the Perceptions of Academic Performance of Higher Education Students during the Distance Education Process

COVID-19 has had serious consequences in all areas of social life, including education. In this period, distance education appeared as an inevitable solution. Even today, when the pandemic process is over and re-normalization has begun, online teaching environments have become such an indispensable part of education systems that it has been decided that a certain proportion of the courses will be conducted online in universities. For this reason, determining student experiences in online courses is important in planning the future of distance education. Since academic performance is the output of the teaching process, students' academic performance is one of the topics of interest in higher education research. There may be different factors affecting the academic performance of students in the distance education process, which imposes more responsibility on students and requires self-control. This study aimed to examine the relationship of academic performance in the distance education with home infrastructure, student interaction, computer skills, academic satisfaction. This research is based on a large-scale study, "The impact of the COVID-19 pandemic on the lives of higher education students", examining the pandemic's impact on higher education student perceptions in 2020. It has been observed that home infrastructure has a significant impact on the student's academic performance. The infrastructure increases the interaction of the student. When home infrastructure is taken as a control variable, students' computer skills are the highest predictor of their perception of academic performance, followed by their online interactions and, finally, perceived satisfaction. Today, pandemic conditions are still ongoing. In addition, even as the pandemic ends, online education has become an indispensable part of our education system. Therefore, the findings of the research would be beneficial for the ongoing planning process.

Effect of Distant Education on Children's Academic Achievement in Primary Schools During Corona Pandemic in Dubai Emirate- Field Study

This study aimed to examine the effect of distance education on the level of educational achievement of children during the Corona period in ten primary schools in the Emirate of Dubai. To achieve the objectives of the study the researchers adopted the descriptive analytical approach. The quantitative method of data collection had been applied using the electronic questionnaire tool consisted of four main axes for data collection and had been distributed to the study sample consisted of 190 students' parents and administrators selected by using simple random techniques. The results of the study indicated that the participation of students in the educational process, and in the establishment of appropriate educational programs and applications for the transmission to distance learning have contributed to reducing the negative effects of the process of shifting from traditional education / face-to-face education classroom teaching to virtual classroom (ZOOM).The study recommended the need for strengthening distance education mechanisms, which contribute in developing the student's interests, tendencies, attitudes, concentrating on the study material, and using of safe and secured electronic devices to increase the search for additional information to reach the correct knowledge. Also, the school administration should have good e-learning plan ahead with required financial credits that will help in overcoming the crisis and mange distance learning processes to reach future objectives successfully. © 2022 Owner/Author.

Distance learning in preparation for the licensing exam "Krok 3” on the "Emergencies” module: pros and cons

In 2020–2021, in the context of the COVID-19 pandemic, the postgraduate medical education system went to forced measures to preserve the life and health of students and teaching staff in the form of a temporary transition to distance learning. During the training on the "Emergencies” module by interns of various specialties, we conducted an anonymous testing of 342 young colleagues with subsequent certain conclusions. A remote andra gogical process is acceptable and useful only as a component of the dual form of higher medical education. The remote form of training in large classrooms has many disadvantages, so it is advisable to conduct classes in small groups. This type of training requires careful study, optimization and adaptation to modern conditions. © 2022. The Authors.

Online education action for defeating COVID-19 in China: An analysis of the system, mechanism and mode

This study explores the online education action for defeating COVID-19 in China from the perspectives of the system, mechanism and mode. In particular, the policy development of online education in China during the epidemic includes the education informatization policy, the online education system, and the online education mechanism in China. The online education and teaching mode during the epidemic involve the synchronous live class-based teaching mode, asynchronous recording and broadcasting teaching mode, online flipped classroom teaching mode, and online tutoring-based teaching mode. Both characteristics and trends of online education in China during the epidemic have been explored in this study. In addition, the problems, discussions and remarks are also offered.

Interdisciplinary Teaching Reform of Financial Engineering Majors Based on the Analytic Hierarchy Process in the Post-Pandemic Era

In the post-epidemic era, the labor market has become increasingly complex, making it even more crucial to incorporate sustainability into employment demand. As we enter the post-pandemic era, a globalization trend has become more apparent. It is crucial to modernize employability through educational reform in order to assist employees in enhancing their professional skills. This study began by analyzing the importance of financial engineering practice instruction and graduate employability in the post-epidemic era. Second, the study proposed the content and a plan for inter-disciplinary teaching reform to address talent cultivation needs based on labor market requirements. Third, a face-to-face survey and interview were conducted with students affected by changes in teaching, and the results were analyzed and summarized. On this basis, the impact of education reform was evaluated using both the expert scoring method and the analytic hierarchy approach. The results indicated that the suggested financial engineering teaching reform program improved the school's discipline strength, enrollment rate, employment rate, and competition awards, especially discipline strength. This research can be used to inform the teaching of financial engineering majors in various countries, assist job candidates in enhancing their professional skills, and build a formidable talent pool for the labor market.

Finding Strength in Times of Crisis? Post-Traumatic Growth During the Covid-19 Pandemic: A Saudi Arabian Perspective

This paper contributes to the Covid-19 literature by exploring the concept of post-traumatic growth (PTG) utilizing a mixed methods approach. The study examines to what extent the participants experienced positive growth and renewal arising from the prolonged period of lockdowns and emergency online learning. Exploring the experiences of 552 female undergraduate students in a private Saudi Arabian university, an online survey was utilized to gather the data. All the students had experienced online education as a result of the pandemic. The findings indicate the participants underwent a diversity of personal growth experiences. In addition, they also developed different coping mechanisms. The study provides insights into the responses of the students to the issues they were facing during the pandemic. It identifies ways in which participants experienced personal growth as well as a shift in perspective about their lives. There are implications for educators, counselors and policymakers emerging from this study. AD -, Kingdom of Saudi Arabia ;, Netherlands ;, Kingdom of Saudi Arabia

Social Distance and Face Mask Detection using vision-based sensors and IP cameras

Healthcare systems all over the world are strained as the COVID-19 pandemic's spread becomes more widespread. The only realistic strategy to avoid asymptomatic transmission is to monitor social distance, as there are no viable medical therapies or vaccinations for it. A unique computer vision-based framework that uses deep learning is to analyze the images that are needed to measure social distance. This technique uses the key point regressor to identify the important feature points utilizing the Visual Geometry Group (VGG19) which is a standard Convolutional Neural Network (CNN) architecture having multiple layers, MobileNetV2 which is a computer vision network that advances the-state-of-art for mobile visual identification, including semantic segmentation, classification and object identification. VGG19 and MobileNetV2 were trained on the Kaggle dataset. The border boxes for the item may be seen as well as the crowd is sizeable, and red identified faces are then analyzed by MobileNetV2 to detect whether the person is wearing a mask or not. The distance between the observed people has been calculated using the Euclidian distance. Pretrained models like (You only look once) YOLOV3 which is a real-time object detection system, RCNN, and Resnet50 are used in our embedded vision system environment to identify social distance on images. The framework YOLOV3 performs an overall accuracy of 95% using transfer learning technique runs in 22ms which is four times fast than other predefined models. In the proposed model we achieved an accuracy of 96.67% using VGG19 and 98.38% using MobileNetV2, this beats all other models in its ability to estimate social distance and face mask. © 2023 IEEE.

Development of Teaching Digital Skills: possibilities in Distance Education

The objective of this investigation is to understand how online distance education can contribute to promoting the development of digital skills, in particular, teaching digital skills. Contemporary society increasingly demands the use of digital technologies in the various areas of life, including in the teaching and learning process, even more evident in the pandemic context of COVID-19, which caused the suspension of face-to-face classes worldwide and imposed a new educational model, facilitated by digital technologies and based on online education methodologies, designated as emergency remote teaching. Among the many challenges, teachers had to adapt and reformulate their pedagogical practices and teaching methodologies in a virtual learning environment, often without having fundamental digital skills to promote quality learning. Through a narrative bibliographic review of an exploratory nature and qualitative methodology, studies, programs and models were identified that could be used as a reference to enable training propositions for the development of teaching digital skills, a key factor for pedagogical innovation.

Control System for Indoor Safety Measures Using a Faster R-CNN Architecture

This paper presents a control system for indoor safety measures using a Faster R-CNN (Region-based Convolutional Neural Network) architecture. The proposed system aims to ensure the safety of occupants in indoor environments by detecting and recognizing potential safety hazards in real time, such as capacity control, social distancing, or mask use. Using deep learning techniques, the system detects these situations to be controlled, notifying the person in charge of the company if any of these are violated. The proposed system was tested in a real teaching environment at Rey Juan Carlos University, using Raspberry Pi 4 as a hardware platform together with an Intel Neural Stick board and a pair of PiCamera RGB (Red Green Blue) cameras to capture images of the environment and a Faster R-CNN architecture to detect and classify objects within the images. To evaluate the performance of the system, a dataset of indoor images was collected and annotated for object detection and classification. The system was trained using this dataset, and its performance was evaluated based on precision, recall, and F1 score. The results show that the proposed system achieved a high level of accuracy in detecting and classifying potential safety hazards in indoor environments. The proposed system includes an efficiently implemented software infrastructure to be launched on a low-cost hardware platform, which is affordable for any company, regardless of size or revenue, and it has the potential to be integrated into existing safety systems in indoor environments such as hospitals, warehouses, and factories, to provide real-time monitoring and alerts for safety hazards. Future work will focus on enhancing the system's robustness and scalability to larger indoor environments with more complex safety hazards.

How a Sense of Collective Efficacy Influences Teacher Learning during Change: The Role of Academic Optimism and Shared Vision

Purpose: The purpose of this study is twofold. First, this study reflects on the development of professional capital through understanding collective cultural factors, namely, academic optimism and shared vision. Second, it aims at exploring teacher learning. Teacher learning resulting in changes to teacher knowledge, attitudes and practices is crucial for the necessary changes education is continually confronted with. This learning is too often studied as a result of individual traits or structural factors, such as motivation or time. The authors investigated how teacher learning is influenced by academic optimism and shared vision. Design/methodology/approach: The authors administered an online web-based survey to 278 teachers in higher education, using the educational change to online learning due to the COVID pandemic as a unique chance to study the role of collective cultural factors in teacher learning. Findings: Results showed how teachers characterized their learning, academic optimism and shared vision during the educational change to online learning resulting from the COVID pandemic. The authors found that teacher learning was greatly influenced by teachers' collective sense of efficacy, an aspect of their academic optimism. Teachers' strong belief in each other, that they as fellow professionals could handle the challenging changes that the COVID pandemic required, strongly enhanced teacher learning during the COVID pandemic. Teachers' feeling of a professional community helped teacher to make sense of, and push through, the undeniable chaos that was the COVID pandemic. Originality/value: Collective cultural factors are rarely studied in conjunction with educational change. Insights into how a collective culture of professionalism enhances or hinders teacher learning are important for theory, policy and practice as it helps understand how teacher teams can be supported to build their professional capital by learning from educational change.

Flexible curriculum design for quantitative skills development: Building on the insights gained during COVID-19

Before the COVID-19 pandemic, technology-enhanced learning and its relation to student engagement, and the necessity of good student-staff relationships for creating a successful education environment were evident. The COVID-19 pandemic forced higher education to adapt to a challenging technology-led learning environment that demanded, inter alia, high levels of flexibility and human-centredness. Valuable lessons were learned that highlighted new perspectives on curriculum design and delivery in a normalised, technology-driven environment. Against the background of COVID-19-related literature on teaching and learning, the authors reflect on their insights regarding curriculum design and delivery of two quantitative skills modules during the COVID-19 pandemic, and its impact on further curriculum planning. The focus of the article is on the intentional flexibility built into curriculum offerings during 2020-2022. The study reviewed flexibility on three levels, namely student, facilitator (staff) and delivery levels, through a multi-method research methodology. Quantitative data related to the academic performance of 2 949 students enrolled for the two quantitative skills modules from 2020 to 2022. Qualitative data related to themes through thematic analysis of student and facilitator surveys, focusgroup discussions and semi-structured interviews. The improved student academic performance reported by the study could be attributed to, amongst other factors, 1) flexibility of the selected delivery option, 2) positive staff and student experiences and engagement, and 3) intentional inclusion of activities promoting student-staff relationships. The good academic results obtained during the pandemic led to important curriculum decisions for a normalised future for these modules, which will be built on flexibility and human-centredness. Among these decisions is to continue presenting the modules in an online environment, even though traditional face-to-face teaching options are available.

Measurement and Evaluation Challenges in Distance Education: Systematic Review and Qualitative Meta-Synthesis

The challenges experienced in measurement and evaluation during the distance education process among student and instructor groups are discussed in the study. A qualitative meta-synthesis method is used in this research. Twenty studies were included in the meta-synthesis. The challenges experienced by the instructors are program utilization, technological inadequacies, a lack of in-service seminars, online exam diversity, inconsistency between what is measured and what is intended to be measured, interference of other features with the measurement, unsuitability of the measurement tools, academic dishonesty, lack of control over online exam diversity, preparation and grading challenges, infrastructural issues, and a lack or latency of feedback. Challenges experienced by learners include alienation to the electronic environment, language, technological inadequacies, lack of materials, validity-reliability issues, application difficulty, lack of guidance in exams, infrastructural issues, network problems, lack of interest and motivation, anxiety about online exams, and avoidance and resistance to self-disclosure in virtual environments.

Efficient Centroid Based Distance Monitoring System Using Deep Learning

COVID-19 virus spread very rapidly if we come in contact to the other person who is infected, this was treated as acute pandemic. As per the data available at WHO more than 663 million infected cases reported and 6.7 million deaths are confirmed worldwide till Dec, 2022. On the basis of this big reported number, we can say that ignorance can cause harm to the people worldwide. Most of the people are vaccinated now but as per standard guideline of WHO social distancing is best practiced to avoid spreading of COVID-19 variants. This is difficult to monitor manually by analyzing the persons live cameras feed. Therefore, there is a need to develop an automated Artificial Intelligence based System that detects and track humans for monitoring. To accomplish this task, many deep learning models have been proposed to calculate distance among each pair of human objects detected in each frame. This paper presents an efficient deep learning monitoring system by considering distance as well as velocity of the object detected to avoid each frame processing to improve the computation complexity in term of frames/second. The detected human object closer to some allowed limit (1m) marked by red color and all other object marked with green color. The comparison of with and without direction consideration is presented and average efficiency found 20.08 FPS (frame/Second) and 22.98 FPS respectively, which is 14.44% faster as well as preserve the accuracy of detection. © 2023 IEEE.

Adopting WhatsApp to Reduce Transactional Distance During the COVID-19 Pandemic

Mobile devices have penetrated all levels of society worldwide, including what are predominantly considered inaccessible, low-income communities in developing nations. Mobile devices are frequently used for economic, political, and social interactions and even business transactions. In a similar fashion, teachers are slowly adapting to the use of mobile devices as a pedagogical tool in education. The aim of this study was to ascertain how a WhatsApp group, a messaging facility, could be used to enhance group interaction among pre-service teachers during the COVID-19 pandemic. Moore's Transactional Distance theory and Salmon's five-stage model guided this qualitative study. Twelve pre-service teachers, out of a class of thirty-five, were purposely selected to work on a group task using the WhatsApp application during the COVID-19 pandemic. They also participated in a focus group interview. WhatsApp artifacts and focus group interview transcripts were analyzed deductively;the results showed that the WhatsApp intervention helped improve pre-service teachers' engagement and the sharing of content to successfully undertake the group activity virtually. WhatsApp's social nature has proven to be an enabler for keeping people connected despite physical distance: reducing cognitive loneliness resulting from social isolation. It is recommended that teacher educators, academics, and researchers, as well as students working remotely take advantage of the usability of WhatsApp for learning and research purposes.

Bridging the Digital Divide: Distance Music Learning among Older Adult Musicians

The purpose of this study was to examine the experiences of conductor-teachers and older adult musicians in a New Horizons ensemble engaged in distance online music-making and music learning. This study employed intrinsic and particularistic qualitative case study designs in which older adult musicians and conductor-teachers of a New Horizons orchestra were interviewed and observed for one year during the COVID-19 pandemic. Primary data sources included verbatim interview transcripts of 11 older adult musicians and the principal conductor, e-mail correspondences, video recordings, and the principal conductor's journal entries. Findings distilled from the data included (a) the information communication technology (ICT) and music learning technology (MLT) introduced and the technological pedagogical and content knowledge (TPACK) needed to teach orchestra members and (b) how orchestra members navigated both ICT and MLT to engage in meaningful music-making and music learning in a distance learning environment. Implications for research and practice include challenging implicit assumptions and messages regarding technology use among older adult musicians, continuing post-COVID distance music learning that may lead to promising models for informal music learning, and continued connectivity beyond the locality of the rehearsal hall.

The Study of the Correlation Betweenthe on the Diet, Sleep Quality and Perceived Stress among College Students in Taichung during the COVID-19 Pandemic Distance Learning.

Research indicates the COVID-19 epidemic changes people's health and diet, However, this has not yet been well discussed in Taiwan, especially in college students. Therefore, the purpose of this study is to investigate the impact of distance learning on college students' dietary patterns, sleep quality and perception of stress during the COVID-19 epidemic in Taiwan. 265 college students from a university in Taichung were recruited in this study. The self-administered online questionnaire was used to investigate the changes in eating behavior, sleep quality, and perception of stress before and one month after distance learning, and further analyzed the relationship among them. The questionnaire contains demographic information, dietary questionnaires (including six categories of food intake behaviors, convenience food intake frequency), the Pittsburgh Sleep Quality Index (PSQI) and Perceived Stress Scale (Chinese 14-item PSS). The results showed that the proportion of college students to meet the recommended Taiwan Dietary Guidelines amount in vegetables (21.9%), fruits (27.5%), meats and dairy products (15.8%), and nuts and seeds (11.3%) were lower during distance learning. The frequency of convenient food intake was lower during distance learning (13.31 +/- 6.10 points;never to occasionally). During the distance learning period, there was a significant negative correlation between dietary patterns and sleep quality (r = -0.160, p = 0.009), It shows that college students with higher dietary pattern scores have better sleep quality. During the distance learning period, there was a significant positive correlation between sleep quality and perceived stress (r = 0.320, p < 0.001), It shows that college students with higher levels of stress had poorer sleep quality. This study found that the lower the perceived stress of college students, the better their diet and sleep quality;conversely, the higher the perceived stress, the worse their diet and sleep quality. Studies have shown that a healthy, balanced diet can reduce the risk of getting various diseases. Therefore, in the post-epidemic era, it is recommended that schools increase the accessibility and availability of vegetables, fruits, dairy products, nuts and seeds on campus to make it easier for teachers and students to obtain such healthy food in order to achieve the goal of promoting balanced diet.Copyright © 2022 Nutrition Society in Taipei. All rights reserved.