Predicting students' satisfaction with virtual education based on health-oriented lifestyle behaviors

The aim of this study was to investigate student satisfaction with virtual education based on their health-oriented lifestyle behaviours. The present study was a descriptive correlational study. The statistical population included all undergraduate students in engineering and psychological fields at Islamic Azad University of Shahre Rey during the second semester of 2020-2021. Of these students, 188 (93 engineering students and 95 psychology students) were randomly selected. To collect the data, an instrument for measuring satisfaction with virtual education as well as the measurement scale for health-oriented academic lifestyle behaviours (Salehzadeh et al., 2017) were used. Findings revealed that the components of a health-oriented lifestyle as a whole explain 37.4% of the variance in student satisfaction with virtual education. The relationship between health-oriented lifestyle facilitators (academic optimism, mastery goal orientation, and academic resilience) and student satisfaction with virtual education was positive and significant. The relationship between health-oriented lifestyle inhibitor components (learned helplessness and procrastination) and student satisfaction with virtual education was negative and significant. The relationship between effort withdrawal and student satisfaction with virtual education was not significant (p>0.05). There was no difference between the components of a health-oriented lifestyle and student satisfaction with virtual education according to educational groups. Accordingly, creating a resilient educational environment, trying to participate, and teaching towards meaningful and problem-based learning will prevent students from avoiding virtual education.

Hybrid Teaching: Reflections on the Learning Process in Students of the Technical University of Babahoyo. 2022

The teaching process has been affected for multiple reasons, one of them was the Covid-19 pandemic, but since years ago the educational processes have been affected by the incessant advance of technologies and their incorporation into the educa-tional process, for which many teachers were not prepared, for this reason the objective of this work is to determine the way in which hybrid education affects the learning process, for this we have established the methodology of the mixed approach, and making use of the explanatory investigative type , the student population is 3216 students from the FCJSE-UTB and through probabilistic sampling a sample of 1845 students from the diffe-rent careers of the faculty was determined, likewise, the survey technique was used for data collection with an instrument vali-dated, the questionnaire. The results analyzed showed that the variables under study were not independent, but on the contrary, according to the statistical tests, they reflected a significant re-lationship between them and, in addition, the dimensions analy-zed had a similar behavior, evidencing a relationship between them and the dependent variable. It was concluded that hybrid teaching has a significant impact at the medium-high level with a Spearman's Rho correlation = 0.755 and with a regressive model that yields a determination coefficient of R2 = 054, in other words that the behavior of the learning process is explained up to 54% for hybrid teaching.

Debriefing in Online Primary ESL Classrooms During the COVID-19 Pandemic: A Case Study

—The meaningfulness of the current educational landscape, where online learning is heavily practised, is often questioned. Experiential learning focuses on the learning process that learners undergo. It is beli eved to help them to make sense of the learning process through active participation and meaningful reflective practice. Debriefing is an experiential learning strategy that requires learners to reflect on their learning experiences and connect them to real-life situations. However, only a limited number of studies have investigated the use of debriefing in the English language teaching and learning context. To this end, this case study aimed to explore the effects of debriefing in online ESL classrooms and the challenges of online debriefing. The case study was conducted in Bintulu, a town in the Malaysian state of Sarawak, and involved two teachers who were actively conducting online ESL lessons during the COVID-19 pandemic. Data collection was conducted through in-depth interviews and observations of recorded online ESL lessons with a focus on the debriefing sessions. The findings indicated that debriefing has positive effects on active English language learners as it helps to improve their critical thinking ability as well as their oral and written language proficiency. The challenges of debriefing in online ESL classrooms include learners being hesitant to talk during lessons, teachers facing difficulties in using appropriate debriefing questions, as well as various technical problems. © 2023 ACADEMY PUBLICATION.

Smart Visual Sensing for Overcrowding in COVID-19 Infected Cities Using Modified Deep Transfer Learning

Currently, COVID-19 is circulating in crowded places as an infectious disease. COVID-19 can be prevented from spreading rapidly in crowded areas by implementing multiple strategies. The use of unmanned aerial vehicles (UAVs) as sensing devices can be useful in detecting overcrowding events. Accordingly, in this article, we introduce a real-time system for identifying overcrowding due to events such as congestion and abnormal behavior. For the first time, a monitoring approach is proposed to detect overcrowding through the UAV and social monitoring system (SMS). We have significantly improved identification by selecting the best features from the water cycle algorithm (WCA) and making decisions based on deep transfer learning. According to the analysis of the UAV videos, the average accuracy is estimated at 96.55%. Experimental results demonstrate that the proposed approach is capable of detecting overcrowding based on UAV videos' frames and SMS's communication even in challenging conditions. © 2005-2012 IEEE.

Vaccine allocation policy optimization and budget sharing mechanism using reinforcement learning

The optimal allocation of vaccines to population subgroups over time is a challenging health care management problem. In the context of a pandemic, the interaction between vaccination policies adopted by multiple agents and their cooperation (or lack thereof) creates a complex environment that affects the global transmission dynamics of the disease. In this study, we take the perspective of decision-making agents that aim to minimize the size of their susceptible populations and must allocate vaccines under limited supply. We assume that vaccine efficiency rates are unknown to agents and we propose a reinforcement learning approach based on Thompson sampling to learn the mean vaccine efficiency rates over time. Furthermore, we develop a budget-balanced resource sharing mechanism to promote cooperation among agents. We apply the proposed framework to the COVID-19 pandemic. We use a raster model of the world where agents represent the main countries worldwide and interact in a global mobility network to generate multiple problem instances. Our numerical results show that the proposed vaccine allocation policy achieves a larger reduction in the number of susceptible individuals, infections and deaths globally compared to a population-based policy. In addition, we show that, under a fixed global vaccine allocation budget, most countries can reduce their national number of infections and deaths by sharing their budget with countries with which they have a relatively high mobility exchange. The proposed framework can be used to improve policy-making in health care management by national and global health authorities. © 2022 Elsevier Ltd

The Student Learning Experience: A Case Study in Object-Based Learning

Artifacts are a primary source of information for fashion history students participating in object-based learning through careful observation, analysis and interpretation. Object-based learning is an advantage that allows students to connect the course material with the physical artifact in-person. Due to the Covid-19 pandemic, classes at a Midwestern university moved midterm to an online format. Artifacts previously viewed in person were posted digitally, thus this was the first semester that artifact analysis included both in-person and online. Students evaluated their learning experience in artifact analysis in-person and online with the goal to understand their perspective on advantages and disadvantages. In-person, students could examine details such as textile weave and hand or machine stitching. Online, multiple views of a garment on a mannequin were available to better understand the shape and silhouette of the garment. In the future, we will incorporate both venues, taking advantage of the best student learning experiences from each. © 2022 ITAA.

Application of machine learning approaches to predict the impact of ambient air pollution on outpatient visits for acute respiratory infections

With a remarkable increase in industrialization among fast-developing countries, air pollution is rising at an alarming rate and has become a public health concern. The study aims to examine the effect of air pollution on patient's hospital visits for respiratory diseases, particularly Acute Respiratory Infections (ARI). Outpatient hospital visits, air pollution and meteorological parameters were collected from March 2018 to October 2021. Eight machine learning algorithms (Random Forest model, K-Nearest Neighbors regression model, Linear regression model, LASSO regression model, Decision Tree Regressor, Support Vector Regression, X.G. Boost and Deep Neural Network with 5-layers) were applied for the analysis of daily air pollutants and outpatient visits for ARI. The evaluation was done by using 5-cross-fold confirmations. The data was randomly divided into test and training data sets at a scale of 1:2, respectively. Results show that among the studied eight machine learning models, the Random Forest model has given the best performance with R2 = 0.606, 0.608 without lag and 1-day lag respectively on ARI patients and R2 = 0.872, 0.871 without lag and 1-day lag respectively on total patients. All eight models did not perform well with the lag effect on the ARI patient dataset but performed better on the total patient dataset. Thus, the study did not find any significant association between ARI patients and ambient air pollution due to the intermittent availability of data during the COVID-19 period. This study gives insight into developing machine learning programs for risk prediction that can be used to predict analytics for several other diseases apart from ARI, such as heart disease and other respiratory diseases. © 2022 Elsevier B.V.

A Hybrid Deep Fused Learning Approach to Segregate Infectious Diseases

Humankind is facing another deadliest pandemic of all times in history, caused by COVID-19. Apart from this challenging pandemic, World Health Organization (WHO) considers tuberculosis (TB) as a preeminent infectious disease due to its high infection rate. Generally, both TB and COVID-19 severely affect the lungs, thus hardening the job of medical practitioners who can often misidentify these diseases in the current situation. Therefore, the time of need calls for an immediate and meticulous automatic diagnostic tool that can accurately discriminate both diseases. As one of the preliminary smart health systems that examine three clinical states (COVID-19, TB, and normal cases), this study proposes an amalgam of image filtering, data-augmentation technique, transfer learning-based approach, and advanced deep-learning classifiers to effectively segregate these diseases. It first employed a generative adversarial network (GAN) and Crimmins speckle removal filter on X-ray images to overcome the issue of limited data and noise. Each pre-processed image is then converted into red, green, and blue (RGB) and Commission Internationale de l'Elcairage (CIE) color spaces from which deep fused features are formed by extracting relevant features using DenseNet121 and ResNet50. Each feature extractor extracts 1000 most useful features which are then fused and finally fed to two variants of recurrent neural network (RNN) classifiers for precise discrimination of three-clinical states. Comparative analysis showed that the proposed Bi-directional long-short-term-memory (Bi-LSTM) model dominated the long-short-term-memory (LSTM) network by attaining an overall accuracy of 98.22% for the three-class classification task, whereas LSTM hardly achieved 94.22% accuracy on the test dataset. © 2023 Tech Science Press. All rights reserved.

A Robust Automated Framework for Classification of CT Covid-19 Images Using MSI-ResNet

Nowadays, the COVID-19 virus disease is spreading rampantly. There are some testing tools and kits available for diagnosing the virus, but it is in a limited count. To diagnose the presence of disease from radiological images, automated COVID-19 diagnosis techniques are needed. The enhancement of AI (Artificial Intelligence) has been focused in previous research, which uses X-ray images for detecting COVID-19. The most common symptoms of COVID-19 are fever, dry cough and sore throat. These symptoms may lead to an increase in the rigorous type of pneumonia with a severe barrier. Since medical imaging is not suggested recently in Canada for critical COVID-19 diagnosis, computer-aided systems are implemented for the early identification of COVID-19, which aids in noticing the disease progression and thus decreases the death rate. Here, a deep learning-based automated method for the extraction of features and classification is enhanced for the detection of COVID-19 from the images of computer tomography (CT). The suggested method functions on the basis of three main processes: data preprocessing, the extraction of features and classification. This approach integrates the union of deep features with the help of Inception 14 and VGG-16 models. At last, a classifier of Multi-scale Improved ResNet (MSI-ResNet) is developed to detect and classify the CT images into unique labels of class. With the support of available open-source COVID-CT datasets that consists of 760 CT pictures, the investigational validation of the suggested method is estimated. The experimental results reveal that the proposed approach offers greater performance with high specificity, accuracy and sensitivity. © 2023 CRL Publishing. All rights reserved.

COVID-19 clinical medical relationship extraction based on MPNet

With the rapid development of biomedical research and information technology, the number of clinical medical literature has increased exponentially. At present, COVID-19 clinical text research has some problems, such as lack of corpus and poor annotation quality. In clinical medical literature, there are many medical related semantic relationships between entities. After the task of entity recognition, how to further extract the relationships between entities efficiently and accurately becomes very critical. In this study, a COVID-19 clinical trial data relationship extraction model based on deep learning method is proposed. The model adopts MPNet model, bidirectional-GRU (BiGRU) network, MAtt mechanism and Conditional Random Field inference layer integration architecture and improves the problem that static word vector cannot represent ambiguity through pre-trained language model. BiGRU network is used to replace the current Bi directional long short term memory structure and simplify the network structure of Long Short Term Memory to improve the training efficiency of the model. Through comparative experiments, the proposed method performs well in the COVID-19 clinical text entity relation extraction task. © 2023 The Authors. IET Cyber-Physical Systems: Theory & Applications published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

Speech emotion recognition with artificial intelligence for contact tracing in the COVID-19 pandemic

If understanding sentiments is already a difficult task in human-human communication, this becomes extremely challenging when a human-computer interaction happens, as for instance in chatbot conversations. In this work, a machine learning neural network-based Speech Emotion Recognition system is presented to perform emotion detection in a chatbot virtual assistant whose task was to perform contact tracing during the COVID-19 pandemic. The system was tested on a novel dataset of audio samples, provided by the company Blu Pantheon, which developed virtual agents capable of autonomously performing contacts tracing for individuals positive to COVID-19. The dataset provided was unlabelled for the emotions associated to the conversations. Therefore, the work was structured using a sort of transfer learning strategy. First, the model is trained using the labelled and publicly available Italian-language dataset EMOVO Corpus. The accuracy achieved in testing phase reached 92%. To the best of their knowledge, thiswork represents the first example in the context of chatbot speech emotion recognition for contact tracing, shedding lights towards the importance of the use of such techniques in virtual assistants and chatbot conversational contexts for psychological human status assessment. The code of this work was publicly released at: https://github.com/fp1acm8/SER. © 2023 The Authors. Cognitive Computation and Systems published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology and Shenzhen University.

Coherent Phonons in Antimony: An Undergraduate Physical Chemistry Solid-State Ultrafast Laser Spectroscopy Experiment

Ultrafast laser pump-probe spectroscopy is an important and growing field of physical chemistry that allows the measurement of chemical dynamics on their natural time scales, but undergraduate laboratory courses lack examples of such spectroscopy and the interpretation of the dynamics that occur. Here we develop and implement an ultrafast pump-probe spectroscopy experiment for the undergraduate physical chemistry laboratory course at the University of California Berkeley. The goal of the experiment is to expose students to concepts in solid-state chemistry and ultrafast spectroscopy via classic coherent phonon dynamics principles developed by researchers over multiple decades. The experiment utilizes a modern high-repetition-rate 800 nm femtosecond Ti:sapphire laser, split pulses with a variable time delay, and sensitive detection of transient reflectivity signals using the lock-in technique. The experiment involves minimal intervention from students and is therefore easy and safe to implement in the laboratory. Students first perform an intensity autocorrelation measurement on the femtosecond laser pulses to obtain their temporal duration. Then, students measure the pump-probe reflectivity of a single-crystal antimony sample to determine the period of coherent phonon oscillations initiated by an ultrafast pulse excitation, which is analyzed by fitting to a sine wave. Students who completed the experiment in-person obtained good experimental results, and students who took the course remotely due to the COVID-19 pandemic were provided with the data they would have obtained during the experiment to analyze. Evaluation of student written and oral reports reveals that the learning goals were met, and that students gained an appreciation for the field of ultrafast laser-induced chemistry. © 2022 American Chemical Society and Division of Chemical Education, Inc.

Predicting Credit Ratings using Deep Learning Models - An Analysis of the Indian IT Industry

Due to the complexity of transactions and the availability of Big Data, many banks and financial institutions are reviewing their business models. Various tasks get involved in determining the credit worthiness like working with spreadsheets, manually gathering data from customers and corporations, etc. In this research paper, we aim to automate and analyze the credit ratings of the Information and technology industry in India. Various Deep-Learning models are incorporated to predict the credit rankings from highest to lowest separately for each company to find the best fit Margin, inventory valuation, etc., are the parameters that contribute to the credit rating predictions. The data collected for the study spans between the years FY-2015 to FY-2020. As per the research been carried out with efficiencies of different Deep Learning models been tested and compared, MLP gained the highest efficiency for predicting the same. This research contributes to identifying how we can predict the ratings for several IT companies in India based on their Financial risk, Business risk, Industrial risk, and Macroeconomic environment using various neural network models for better accuracy. Also it helps us understand the significance of Artificial Neural Networks in credit rating predictions using unstructured and real time Financial data consisting the influence of COVID-19 in Indian IT industry.

How do students deal with forced digitalisation in teaching and learning? Implications for quality assurance

Purpose: This paper aims to investigate student subgroups' responses to the coercive digitalisation of teaching and learning processes during the pandemic. Respective variance is discussed in terms of digital inequality and is interpreted as a need to individualise teaching and learning and quality assurance practices. Design/methodology/approach: This study uses data from surveys (N = 955) on student perceptions of the introduction of emergency digitalisation – an important aspect of higher education. The authors perform latent class analyses to identify student subgroups. The students were asked to rate digital learning processes and their overall learning experiences. Findings: The identified student subgroups are proponents, pragmatics and sceptics of digitalised teaching and learning processes. These subgroups have different preferences with regard to teaching and learning modes of delivery, which implies the relevance of individualised educational services and respective quality assurance practices to reflections on improvement needs. Research limitations/implications: The data are from a single, typical German university;therefore, the scope of the results may be limited. However, this study enriches future research on the traits of student subgroups and students' coping strategies in an ever-changing learning environment. Practical implications: The findings may help individualise universities' counselling services to enhance overall teaching performance and quality assurance practices in a digitalised environment. Originality/value: The findings provide insights into students' responses to the COVID-19 pandemic and its impact on teaching and learning. This paper enriches the research on student heterogeneity and relates this to development needs of quality assurance practice. © 2022, Philipp Pohlenz, Annika Felix, Sarah Berndt and Markus Seyfried.

Pedagogical support in on-site and virtual professional practice

We frame our research on future teachers' need for accompani-ment during their formative process. Until 2019, most profes-sional trainings were face-to-face in the Practice Centers and UCSH's workshop activities. Derived from the COVID-19 pan-demic, a new form of educational accompaniment appeared in 2020, online work with students. As references to this study, we established aspects we learned during the health crisis, such as collaborative work, adaptation, and flexibility. The UCSH For-mative Model considers the three fundamental pillars: identity, quality, and responsibility. The dimensions of Salesian accompa-niment are anthropological, educational, and theological. Final-ly, the relevant competencies proposed by CASEL are considered from the socioemotional area. Our work methodology follows participatory action research, which considers the review of background information related to the research topic, the elabo-ration, and application of questionnaires, and online interviews, to then continue analyzing comparative and qualitative data. We aim to arrive at the proposal of pedagogical accompaniment and the evaluation phase that would correspond once implemented. Our main results show that there has been an evolution of the student's perceptions about professional practice since 2019, where this instance was associated with stress, insecurity, and even failure. During the years 2020 and 2021, the students value the accompaniment and feedback positively from both the col-laborating teachers (guides) and the internship teachers (super-visors).

Systematic review on mobile collaborative learning for engineering education

Our daily lives have been transformed by mobile smart devices. Due to the sudden impact of the coronavirus (Covid-19) on education, the importance of mobile devices for communicating with teachers and students has risen to a new level of prominence. The Web of Science and Scopus databases were used to conduct a systematic review of the research on mobile collaborative learning in engineering education. The purpose of this review is to ascertain the degree to which research on mobile collaborative learning has been conducted in the field of engineering education between 2010 and 2020. A total of 48 articles were reviewed to ascertain the research methodologies and area of study, as well as to provide an updated review of studies on mobile collaborative applications, particularly in the field of engineering education. Among the most significant findings is that the majority of publications make use of augmented reality and mobile application development. According to the review, the majority of studies were conducted in the fields of computer sciences, electronic engineering, and artificial intelligence. © 2022, Beijing Normal University.

Perspectives of non-expert users on cyber security and privacy: An analysis of online discussions on twitter

Many researchers have studied non-expert users' perspectives of cyber security and privacy aspects of computing devices at home, but their studies are mostly small-scale empirical studies based on online surveys and interviews and limited to one or a few specific types of devices, such as smart speakers. This paper reports our work on an online social media analysis of a large-scale Twitter dataset, covering cyber security and privacy aspects of many different types of computing devices discussed by non-expert users in the real world. We developed two new machine learning based classifiers to automatically create the Twitter dataset with 435,207 tweets posted by 337,604 non-expert users in January and February of 2019, 2020 and 2021. We analyzed the dataset using both quantitative (topic modeling and sentiment analysis) and qualitative analysis methods, leading to various previously unknown findings. For instance, we observed a sharp (more than doubled) increase of non-expert users' tweets on cyber security and privacy during the pandemic in 2021, compare to in the pre-COVID years (2019 and 2020). Our analysis revealed a diverse range of topics discussed by non-expert users, including VPNs, Wi-Fi, smartphones, laptops, smart home devices, financial security, help-seeking, and roles of different stakeholders. Overall negative sentiment was observed across almost all topics in all the three years. Our results indicate the multi-faceted nature of non-expert users' perspectives on cyber security and privacy and call for more holistic, comprehensive and nuanced research on their perspectives. © 2022

Telematic Education at the University: A Study in Future Teachers

The emergence of Information and Communication Technologies in the educational area has allowed the expansion of learning through telematic means. In addition, the Covid-19 pandemic, and the consequent closure of school institutions, led to the massive application of fully online or combined training (face-to-face and with telematics support). The objective of this research was to know the evaluations of university students who intend to be teachers, about the training they are receiving by telematic means. To do this, an online questionnaire was designed, validated and applied to 523 students from two universities from Spain and one from Portugal. Descriptive and multivariate analyzes were performed, obtaining statistically significant differences. The results show that telematic education is not evaluated in a particularly positive way by the students, highlighting that they miss socializing with their classmates, although they recognize that its implementation also offers advantages such as greater flexibility and a reduction in economic expenses. Finally, the need to transform teaching methodologies for an efficient transition from face-to-face to telematic learning is discussed, and concludes with guidelines to improve the quality and effectiveness of online and combined training plans for higher education students.

Play and learn: Children's agency through the COVID‐19 pandemic in Mozambique

Social distancing, one of the measures adopted in the context of the COVID‐19 pandemic, profoundly impacted on the lives of children. The consequences were, however, not homogenous. By focusing on the daily practices of 41 Mozambican children aged 3–10, we consider how differences in socioeconomic backgrounds led children to respond to the social restrictions in ways that made sense to them. Inspired by Abebe (2019), we identify how the interruptions of daily routines enabled specific instances of agency on children's part. These, we argue, produce new forms of continuity.

Perceptions on the use of distance learning by families of children with visual impairment and deafblindness

The purpose of our qualitative study was to explore what distance-based teaching and learning practices have been supportive to students with visual impairments and their families. Using purposive sampling, interviews, and qualitative analysis, we found that supportive approaches for distance learning (DL) included parental involvement and participation, as well as tailored instructional approaches and accommodations for the student. In some instances, DL was identified as being more supportive for immune-compromised children. Negative facets of the practice included diminished richness in socializing, and the lack of certain strengths of in-person education. Families' experiences ranged from finding DL helpful, to considering the practice as unfit for their child's education, as well as a poor fit for family life. Flags for future research include family preparation for future DL needs, including culturally-diverse families in research opportunities, and evaluating what DL supports lead to improved outcomes for children and families. © The Author(s) 2023.