Factors affecting the public higher education institution (PHEI) acceptance of online meetings applications during COVID-19 pandemic: an empirical study

PurposeIn order to ensure effectiveness of staff's performance using online meetings applications during coronavirus disease (COVID-19), having the behavioural intention is mandatory for staff to measure, test, and manage the staff's data. Understanding of Public Higher Education Institution (PHEI) staffs' intention and behaviour toward online meetings platforms is needed to develop and implement effective and efficient strategies. The objectives of this paper to identify the factors that affect staff to use online meetings applications, to develop a model that examining the factors that affect PHEI staff to online meetings applications and to validate the proposed model. This study used a cross-sectional quantitative correlational study with using UTAUT2 model by validating the model and mediating variables to enhance the model's explanatory power and to make the model more applicable to PHEI staff's behavioural intention.Design/methodology/approachThe data were collected in Malaysia from March to May 2021. The survey took place using Google form and was send to PHEI staff for answer. This research particularly chooses PHEI as the location to carry out the research due to two main factors. Statistical analysis and hypotheses were tested using structural equation modelling based on the optimisation technique of partial least squares. SmartPLS software, Version 3.0 (Hair et al., 2010) was used to conduct the analysis. A conceptualised estimation model was "drawn in” the partial least squares structural equation modeling (PLS-SEM) to analyse the consequences of the variables' relationships. In essence, the PLS-SEM simulation was carried out in a model by assessing and computing various parameters that included elements like validity, durability, and item loading. Henseler et al. (2009) suggested a two-step method that includes PLS model parameter computing. This is accomplished by first solving the estimation model in the structural model independently before calculating the direction coefficients. The results of data analysis using SmartPLS findings and interpretation of the data are addressed. The questionnaire was extensively examined to ensure that the data obtained were presented in a clear and intelligible manner, with the use of figures, and graphs.FindingsThis current study found that the usability of the material, the reliability of operating, the impact of the PHEI staff's views on its usage, and finally the familiarity with the online meetings platforms influenced PHEI staff's behavioural intention for adoption and long-term use of online meeting platforms using UTAUT2. The staff's behavioural intention for using online meeting platforms was significantly influenced by the effort expectancy, facilitating conditions and habit of online meeting platforms. There was a clear association between "Habit” and "Behavioural Intention” for the usage of information technology in learning in several studies (El-Masri and Tarhini, 2017;Uur and Turan, 2018;Mosunmola et al., 2018;Venkatesh et al., 2003). As a consequence of the utility of online meeting platforms in daily staff meetings and learning activities, this technology has been adopted.Originality/valueThis study used UTAUT2 and structural equations modelling in this study to assess respondents' perspectives on the use of online meetings platforms in PHEI, since users' perspective is a significant factor in the adoption and acceptance of online meeting applications. Staff's behavioural intention to use online meeting platforms was effectively enhanced by "Effort Expectancy,” "Facilitating Conditions” and "Habit” in this study. The study shows that identifying PHEI staff's perspectives will effectively increase the staff's aversion to utilising online meeting platforms for online meetings purposes.

Multitasking: Is It Worth the Risk?

[...]the nurse may be triaging a patient who is reporting symptoms and at the same, a staff member gives the nurse a critical lab result. Despite the perception that multitasking is an impressive skill, it is actually detrimental and, in some cases, risky. Since human multitasking became a phenomenon, it has been studied by many scientists. For the well-being of you and your patients, slow down, pause, and focus. * Kathryn Koehne, DNP RN, AMB-BC, C-TNP is Director of Nursing and Operations, Crescent Cove, Minneapolis, MN;Consultant and Presenter, Telephone Triage Consulting, Inc.;and Adjunct Faculty, Viterbo University, La Crosse, WI. Sg2 Health Care Intelligence. https://www.sg2.com/health-care-intelligence-blog/2021/06/sg2-2021-impact-ofchange-forecast/ Merriam-Webster.

How Motivation, Nomophobic Design and Environmental Demands Predict Students' Media Multitasking when Participating in Online Courses During COVID-19: An Empirical Study with a HCI Time and Temporality Lens

There is an emerging shift in human-computer interaction (HCI) research from things to events and towards time and temporality as a design material, which is made even more urgent by the unique time of the COVID-19 period. This paper pushes this shift forwards by investigating factors and the way that these shape online media multitasking behaviour over time during COVID-19. We model the factors along the WHAT and HOW dimensions of the HCI-over-Time model (HCIoT) with self-report data from 117 university students and objective behavioural data from 40 university students, who participated in an online course over 2 weeks during COVID-19. The results indicated a pervasiveness of media multitasking behaviour over time in an online course, driven by individual factors and enhanced by their mutual fit. Based on interpretation of our data, we suggest conceptualizing the COVID-19 period as the larger temporal environment in the HCIoT model. The discussion further explains how the broader idea of human-computer-environment fit is significant to understand HCIoT through an interaction lens. We discuss methodological issues related to differentiating between self-report and behavioural measures when applying the HCIoT model. The conclusion supports the feasibility and significance of conceptualizing media multitasking during COVID-19 as temporal HCI and of further developing and operationalizing the HCIoT model by using both behavioural and self-report measures.

Videos and podcasts for delivering agricultural extension: achieving credibility, relevance, legitimacy and accessibility

PurposeTo explore the perceived credibility, relevance, legitimacy and accessibility of videos and podcasts in farm extension.MethodsA two-phase mixed methods approach consisting of a pre-COVID online survey of farmers (n = 221), farmer telephone interviews (n = 60) and in-person focus groups of farmers (n = 4) followed by an analysis of how viewers interact with Agricology videos and podcasts, a further online survey (n = 141) and online farmer focus groups (n = 4) during the COVID-19 pandemic.FindingsIf they are to be perceived as effective extension methods, videos should be short, concise, practical, advert-free and visualise how to implement a practice. Podcasts can be longer, more detailed, and allow multitasking. Both should use farmer-friendly language, be easily accessible, high quality, non-biased, and be created by someone whom farmers respect.Practical implicationsHelps policy-makers and extensionists understand the potential of videos and podcasts and the trade-offs in using them with other forms of extension. The findings are also of use to global advisory services seeking to offer hybridised advice as a result of the ongoing COVID pandemic.Theoretical implicationsElucidates the trade-offs of using videos and podcasts when face-to-face extension is not possible and develops the CRELE framework.OriginalityDiscusses the role of podcasts in farm extension and re-evaluates the role of videos when face-to-face extension is impossible.

Integrating Two-Level Reinforcement Learning Process for Enhancing Task Scheduling Efficiency in a Complex Problem-Solving Environment

Task scheduling scenarios require the system designers to have complete information about the resources and their capabilities, along with the tasks and their application-specific requirements. An effective task-to-resource mapping strategy will maximize resource utilization under constraints, while minimizing the task waiting time, which will in-turn maximize the task execution efficiency. In this work, a two-level reinforcement learning algorithm for task scheduling is proposed. The algorithm utilizes a deep-intensive learning stage to generate a deployable strategy for task-to-resource mapping. This mapping is re-evaluated at specific execution breakpoints, and the strategy is re-evaluated based on the incremental learning from these breakpoints. In order to perform incremental learning, real-time parametric checking is done on the resources and the tasks;and a new strategy is devised during execution. The mean task waiting time is reduced by 20% when compared with standard algorithms like Dynamic and Integrated Resource Scheduling, Improved Differential Evolution, and Q-learning-based Improved Differential Evolution;while the resource utilization is improved by more than 15%. The algorithm is evaluated on datasets from different domains like Coronavirus disease (COVID-19) datasets of public domain, National Aeronautics and Space Administration (NASA) datasets and others. The proposed method performs consistently on all the datasets. © 2023 IETE.

Meet Lynda Enos: The Occupational Health Master

The article focuses on Lynda Enos a certified professional ergonomist who has worked in various healthcare and occupational health settings. Topics include discussing her background and how she became interested in occupational health, preventing violence in healthcare settings, and her work for a nursing union.

Are South Korean College Students Benefitting from Digital Learning?

With COVID-19, the advancement of mobile devices (e.g., smartphones, laptops, tablets) has brought a welcoming change to education: digital learning. This study addresses the relationship between mobile device use and academic performance through three different models by controlling demographic data, technological infrastructure conditions, and daily total multi-tasking time. The first model emphasized the daily total mobile device use time. The second model divided the daily total mobile device use time into academic and non-academic oriented uses. The final model divided the overall mobile device use into seven specific usage types. The study found that an increase in the daily total mobile device use time negatively affected GPA;only non-academic purpose use time had a negative significance toward GPA;none of the seven usage types were significant in predicting GPA. Based on the findings, suggestions on improvements for the future digital learning policy were proposed.

Forecasting Trend of Coronavirus Disease 2019 using Multi-Task Weighted TSK Fuzzy System

Artificial intelligence-(AI) based fog/edge computing has become a promising paradigm for infectious disease. Various AI algorithms are embedded in cooperative fog/edge devices to construct medical Internet of Things environments, infectious disease forecast systems, smart health, and so on. However, these systems are usually done in isolation, which is called single-task learning. They do not consider the correlation and relationship between multiple/different tasks, so some common information in the model parameters or data characteristics is lost. In this study, each data center in fog/edge computing is considered as a task in the multi-task learning framework. In such a learning framework, a multi-task weighted Takagi-Sugeno-Kang (TSK) fuzzy system, called MW-TSKFS, is developed to forecast the trend of Coronavirus disease 2019 (COVID-19). MW-TSKFS provides a multi-task learning strategy for both antecedent and consequent parameters of fuzzy rules. First, a multi-task weighted fuzzy c-means clustering algorithm is developed for antecedent parameter learning, which extracts the public information among all tasks and the private information of each task. By sharing the public cluster centroid and public membership matrix, the differences of commonality and individuality can be further exploited. For consequent parameter learning of MW-TSKFS, a multi-task collaborative learning mechanism is developed based on ϵ-insensitive criterion and L2 norm penalty term, which can enhance the generalization and forecasting ability of the proposed fuzzy system. The experimental results on the real COVID-19 time series show that the forecasting tend model based on multi-task the weighted TSK fuzzy system has a high application value. © 2021 Association for Computing Machinery.

An Optimized Modelling and Simulation on Task Scheduling for Multi-Processor System using Hybridized ACO-CVOA

Task allocation on the multi-processor system distributes the task according to capacity of each processor that optimally selects the best. The optimal selection of processor leads to increase performance and this also impact the makespan. In task scheduling, most of the research work focused on the objective of managing the power consumption and time complexity due to improper selection of processors for the given task items. This paper mainly focusses on the modelling of the optimal task allocation using a novel hybridization method of Ant Colony Optimization (ACO) with Corona Virus Optimization Algorithm (CVOA). There are several other methods that estimate the weight value of processors and find the best match to the task by using the traditional distance estimation method or by using standard rule-based validation. The proposed algorithm searches the best selection of machines for the corresponding parameters and weight value iteratively and finally recognizes the capacity of it. The performance of proposed method is evaluated on the parameters of elapsed time, throughput and compared with the state-of-art methods. © 2022 Institute of Information Science. All rights reserved.

Media multitasking, depression, and anxiety of college students: Serial mediating effects of attention control and negative information attentional bias.

Background: The COVID-19 epidemic provides an environment for frequent media multitasking, which might associate with an increase in depression and anxiety. Since many studies have found that media multitasking negatively affects cognitive capacity, we propose a cognitive perspective to explore how media multitasking may associate with mental health. This study examined the potential mediating role of attention control and negative information attentional bias in the relationship between media multitasking and anxiety and depression. Methods: Participants (n = 567) were recruited from college students in China. They completed an online survey that included the Media Multitasking Inventory (MMI), Attention Control Scale (ACS), Attention to Positive and Negative Information Scale (APNI), Generalized Anxiety Disorder Scale (GAD-7), and Patient Health Questionnaire (PHQ-9). After exploring the correlations between the measures, serial mediation models were examined. Results: The results indicated significant positive correlations between media multitasking and anxiety and depression. Media multitasking, anxiety, and depression were negatively correlated with attention focusing, while positively correlated with negative information attention bias. Media multitasking did not correlate with attention shifting. Mediation modeling demonstrated that attention focusing and negative information attention bias played a serial mediating role in the relationship between media multitasking and anxiety and depression. However, the results did not support the serial mediation model through attention shifting and negative information attention bias. Conclusion: Media multitasking does not directly influence anxiety and depression, while attention focusing and negative information attention bias play serial mediating roles in their relationship. This study highlights the potential cognitive mechanisms between media multitasking and anxiety and depression, providing theoretical support for interventions in individual mental health during the epidemic.

Revitalizing Regression Tasks Through Modern Training Procedures: Applications in Medical Image Analysis for Covid-19 Infection Percentage Estimation

In order to establish the correct protocol for COVID-19 treatment, estimating the percentage of COVID-19 specific infection within the lung tissue can be an important tool. This article describes the approach we used in order to estimate the COVID-19 infection percentage on lung CT scan slices within the Covid-19-Infection-Percentage-Estimation-Challenge. Our method frames the regression problem as a multi-tasking process and is based on modern training pipelines and architectures that correspond to state of the art models on image classification tasks. It obtained the best score on the validation dataset and ranked third in the testing phase within the competition. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Sound and Silence: The Effects of Environmental Conditions on State Boredom in an Online Study during the COVID-19 Pandemic.

Boredom is a negative emotion commonly experienced in mundane situations. Boredom is thought to arise from a mismatch between individuals and their expectation for environmental stimulation. People attempt to reduce boredom by increasing the stimulation in their environment (e.g., turning on TV or music). Theories of boredom suggest external stimulation may cue the individual to expect more stimulation than the mundane task offers-thereby increasing boredom. Researchers adapted lab-based tasks to online during the COVID-19 pandemic, which allowed participants to set the study's environmental conditions. Our method involved data collected online during the COVID-19 pandemic. We tested whether 137 college-age participants who reported being alone in a noisy room experienced more boredom after a mundane task than those who were alone in a quiet room. Results showed individuals in a noisier environment reported more boredom following a repetitive task than those in a quieter environment. Some people, high in trait boredom, experience boredom more frequently or cannot tolerate it. Our results revealed that the effects of environmental condition remained after controlling for the influence of trait boredom. In the discussion, we describe links to extant boredom research and implications for researchers collecting data online and individuals attempting to mitigate boredom.

DISTRACTORS IN REMOTE CLASSES DURING COVID-19 IN ECUADOR: AN ANALYSIS OF PERCEPTIONS

This article makes a diagnosis about distractors and problems presented by students during virtual classes in the confinement caused by the COVID-19. A non-experimental ex-post-facto methodology was used, and an ad hoc questionnaire was applied to 60 students from a higher education institution in Ecuador. Data were summarized using descriptive statistics and tests of association were performed. Subsequently, a logistic regression model was fitted to explain the number of distractors as a function of predictors. Students identified several technical problems and a myriad of distractions when receiving remote classes at home. No association was found between variables, but a high presence of distracters was determined, regardless of career, gender, age and type of device. The number of distracters to which students were exposed reflects a possible ecosystem of negative impacts on the learning process during online classes.

Explainable COVID-19 Infections Identification and Delineation Using Calibrated Pseudo Labels

The upheaval brought by the arrival of the COVID-19 pandemic has continued to bring fresh challenges over the past two years. During this COVID-19 pandemic, there has been a need for rapid identification of infected patients and specific delineation of infection areas in computed tomography (CT) images. Although deep supervised learning methods have been established quickly, the scarcity of both image-level and pixel-level labels as well as the lack of explainable transparency still hinder the applicability of AI. Can we identify infected patients and delineate the infections with extreme minimal supervision? Semi-supervised learning has demonstrated promising performance under limited labelled data and sufficient unlabelled data. Inspired by semi-supervised learning, we propose a model-agnostic calibrated pseudo-labelling strategy and apply it under a consistency regularization framework to generate explainable identification and delineation results. We demonstrate the effectiveness of our model with the combination of limited labelled data and sufficient unlabelled data or weakly-labelled data. Extensive experiments have shown that our model can efficiently utilize limited labelled data and provide explainable classification and segmentation results for decision-making in clinical routine.

Doom and zoom: How the tsunami of distractions from the rapid collision of home and work floods video call interactions

The world flipped to remote work overnight with the COVID-19 pandemic. As such, current literature on the pandemic video call work environment is limited and is mainly trade articles. Previous literature used many terms, with one term per study, to evaluate deliberate behaviors where one engaged in an unrelated task with or without a conversation partner. Therefore, this study identified divided presence as the umbrella term to aggregate these behaviors. At this point, divided presence is defined as one's deliberate behavioral choice to divide one's presence between a live conversation partner and at least one other unrelated task simultaneously. This narrative study examined how 21 pharmaceutical or biotechnology professionals who worked remotely at least two days per week and experienced receiving divided presence from colleagues on work video calls in the COVID-19 remote work environment made sense of this experience. This research used real-life scenarios in video calls with 3 participants per call and a follow-up survey to validate themes. Psychological meaningfulness, safety, and availability served as this study's theoretical framework. Ten themes emerged across the call groupings. Findings suggest that when participants received divided presence, they experienced negative, empathetic, and variable emotional impact. The nature of the colleague relationship and the unrelated task were potential mitigating or compounding factors. Power dynamics had an impact and, repeat engagers in divided presence were detrimental to working relationships. Lastly, poorly organized meetings increased undesirable impact from a participant's receiving and propensity to engage in divided presence. This study's findings validated pre-COVID-19 literature, showed that the theoretical framework still works today, and provided challenges to literature with siloed lenses. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Multitasking Behavior and Perceptions of Academic Performance in University Business Students in Mexico during the COVID-19 Pandemic

The current study measures the influence of multitasking behavior and self-efficacy for self-regulated learning (SESRL) on perceptions of academic performance and views in university students during the COVID-19 pandemic in Mexico. 264 university students fulfilled an online questionnaire. It was observed that multitasking behavior negatively influences SESRL (−0.203), while SESRL showed a positive influence of 0.537 on perceptions of academic performance, and multitasking behavior had an influence of −0.097 on the perception of academic per-formance. Cronbach’s alpha and Average Variance Extracted values were 0.809 and 0.577 (multitasking behavior), 0.819 and 0.626 (SESRL), 0.873 and 0.725 (perceptions of academic performance), respectively. The results of the bootstrapping test showed that the path coefficients were significant. The study outcomes can support new plans in universities to ensure the best academic outcomes. Our study showed evidence of the COVID-19 impact on education behavior. This study’s novelty is based on using the partial least square structural equation modeling (PLS-SEM) technique to evaluate these variables. © 2022, Tech Science Press. All rights reserved.

Negative effects of distance learning accentuated by COVID-19 outbreak: a perspective of learners and teachers

Set in a French higher education context, this paper contributes to the knowledge management literature by arguing that the digital transformation of knowledge transfer via distance learning includes negative outcomes, in addition to many benefits. Based on quantitative and qualitative data, via an online survey from learners and instructors, our findings show that while online modes of delivery are convenient and cost-effective, they overlook many aspects that enable users to engage in knowledge transfer.

Air Pollution Prediction in Mass Rallies With a New Temporally-Weighted Sample-Based Multitask Learner

With the rapid development of industrialization, the environmental pollution issue is becoming increasingly serious, especially the air pollution problem. As the core of the prevention and control of air pollution, air pollution prediction plays a very significant role in human survival and development. Therefore, it is highly essential to develop an accurate air pollution prediction model for mass rallies (e.g., playground and bazaar). Recent studies have suggested that multiple air contaminants, e.g., PM2.5 and PM10, which belong to a kind of aerosol, can carry the Covid-19 virus and spread it rapidly through the atmosphere, and this dramatically increases the risk of Covid-19 infection, particularly in the crowded and enclosed environment. Nevertheless, most existing air pollution prediction methods, which rely on large amounts of historical data for modeling and assume that the crowd flows relatively slow, are difficult to apply well to predict air pollution in mass rallies. To solve the aforementioned problem and better assist the decision-makers in managing environmental risk to human beings, in this article, we come up with a novel air pollution prediction model for mass rallies. More specifically, we first propose a temporally weighting matrix to differentiate the significance of training samples in the time domain. Then, we construct a temporal support vector regressor (TSVR), which puts more emphasis on the adjacent samples by considering the fact that the crowd usually flows promptly and disorderly in mass rallies. Finally, based on the extended TSVR, we develop a multitask TSVR (MTSVR) that simultaneously considers the related tasks. Since different air contaminants are correlated with each other, all the tasks can benefit by sharing information. The results of comparison experiments demonstrate that our presented MTSVR outperforms state-of-the-art single-task learners, multitask learners, and air pollution predictors when applied for air pollution prediction in mass rallies. Particularly, when under the six-task condition, the error values of the prediction of PM2.5, PM10, and O-3 obtained by our proposed method are relatively lower, outperforming the most advanced method tested by 15.2%, 6.1%, and 4.3%, and the precision values of the predicted values outperform the advanced method tested by 28.3%, 25.1%, and 24.8%.

SHAMS-Smart Hospital Assisting Multitasking System

Advancements in robotics and artificial intelligence technologies have added a prominent contribution in healthcare services. Due to the exponential increase in synthetic viruses specifically these days, the Covid-19 pandemic caused a high rate of infection and death of hospitals' staff. SHAMS-Smart Hospital Assisting Multitasking System was proposed as the first line of defense between hospital staff and this epidemic. Using cutting edge technologies, SHAMS utilized robotics, embedded systems, electrical engineering, electronics, computer vision, natural language processing, and software engineering to facilitate its services. SHAMS can autonomously navigate, and sterilize the hospital, recognize hospital staff, understand speech commands, deliver medicines to patient room, and afford services to patients. Moreover, making self-sterilizing. This paper studies the development and the impact of utilizing autonomous robots in the healthcare crisis. This research paper discusses the main modules of SHAMS robot. SHAMS showed its efficiency, reliability, and scalability in the medical domain. SHAMS also paved the way for robotics to be utilized in various fields in the middle east. © 2021 IEEE.

‘Should I Turn on My Video Camera?’ The Students’ Perceptions of the use of Video Cameras in Synchronous Distant Learning

One of the challenges teachers and students face in online synchronous learning is not turning on their video cameras. The reasons are multitasking, being concerned about the background, psychological barriers, and poor internet connection. In this study, social presence theory (SPT) was employed as the theoretical lens to understand the possible impacts of video cameras in synchronous online learning. Social presence allows individuals to make personal characteristics visible to the community. Students experience greater levels of trust and rapport because of verbal and nonverbal cues that occur when video cameras are turned on in video conferencing. The use of video cameras in synchronous distant learning creates intimacy and immediacy, leading to teacher–learner social presence, which leads to dialog. The phenomenographic study was carried out to analyze the students’ perceptions of the phenomena. The eighty-two first-year undergraduate and doctoral students took part in the study. It showed that students perceive a video camera as a tool for cooperation, as well as for self-discipline and self-control. The students relate the use of video cameras with quality studies, the ability to interact, and to be a part of the process. They feel less inclined to participate when their cameras are off. That leads to the weaker student–teacher relationship, which is achieved with a higher social presence. It is essential to see one other to strengthen students’ motivation, sense of belonging, and community in the courses for first-year students who are still developing learning habits and social networks.