Student Perspective of Online Pedagogy Post COVID-19. A Missed Opportunity?

The COVID-19 pandemic has significantly changed education and caused unprecedented disruptions. These changes may disappear once the schools resume face-to-face classes in full force. Likewise, a positive change may not be necessarily what we want in education. This may be due to the existence of digital divide among students which cannot be ignored. During the COVID-19 pandemic, OneNote Class Notebook is used as an interactive digital whiteboard and has been evident as one of the best alternatives to the traditional whiteboard in the teaching and learning process. In this study, we aim to analyze students' perceptions of OneNote Class Notebook and the level of their continuous intention to use OneNote Class Notebook as an interactive digital whiteboard to replace the traditional whiteboard when school reopens with face-to-face lessons in the classroom in full force. The findings show that the students perceived that OneNote Class Notebook is indeed a useful tool to be used for calculus learning. But, it cannot be perceived as suitability to continue to be used during post COVID-19 period, when school reopens with physical classes in full force. In this regard, it reminds educators of the importance of rethinking education in the new normal post COVID-19 era from the perspective of curriculum studies. © 2023 IEEE.

Explainable Mortality Prediction Model for Congestive Heart Failure with Nature-Based Feature Selection Method

A mortality prediction model can be a great tool to assist physicians in decision making in the intensive care unit (ICU) in order to ensure optimal allocation of ICU resources according to the patient's health conditions. The entire world witnessed a severe ICU patient capacity crisis a few years ago during the COVID-19 pandemic. Various widely utilized machine learning (ML) models in this research field can provide poor performance due to a lack of proper feature selection. Despite the fact that nature-based algorithms in other sectors perform well for feature selection, no comparative study on the performance of nature-based algorithms in feature selection has been conducted in the ICU mortality prediction field. Therefore, in this research, a comparison of the performance of ML models with and without feature selection was performed. In addition, explainable artificial intelligence (AI) was used to examine the contribution of features to the decision-making process. Explainable AI focuses on establishing transparency and traceability for statistical black-box machine learning techniques. Explainable AI is essential in the medical industry to foster public confidence and trust in machine learning model predictions. Three nature-based algorithms, namely the flower pollination algorithm (FPA), particle swarm algorithm (PSO), and genetic algorithm (GA), were used in this study. For the classification job, the most widely used and diversified classifiers from the literature were used, including logistic regression (LR), decision tree (DT) classifier, the gradient boosting (GB) algorithm, and the random forest (RF) algorithm. The Medical Information Mart for Intensive Care III (MIMIC-III) dataset was used to collect data on heart failure patients. On the MIMIC-III dataset, it was discovered that feature selection significantly improved the performance of the described ML models. Without applying any feature selection process on the MIMIC-III heart failure patient dataset, the accuracy of the four mentioned ML models, namely LR, DT, RF, and GB was 69.9%, 82.5%, 90.6%, and 91.0%, respectively, whereas with feature selection in combination with the FPA, the accuracy increased to 71.6%, 84.8%, 92.8%, and 91.1%, respectively, for the same dataset. Again, the FPA showed the highest area under the receiver operating characteristic (AUROC) value of 83.0% with the RF algorithm among all other algorithms utilized in this study. Thus, it can be concluded that the use of feature selection with FPA has a profound impact on the outcome of ML models. Shapley additive explanation (SHAP) was used in this study to interpret the ML models. SHAP was used in this study because it offers mathematical assurances for the precision and consistency of explanations. It is trustworthy and suitable for both local and global explanations. It was found that the features that were selected by SHAP as most important were also most common with the features selected by the FPA. Therefore, we hope that this study will help physicians to predict ICU mortality for heart failure patients with a limited number of features and with high accuracy.

Through the Lens of Students: MS Teams as a Sustainable Pedagogical Tool

The COVID-19 pandemic has highlighted the need for higher education institutions to modernize and embrace the post-digital age. This study evaluates students' perspectives of utilizing MS Teams as a means of facilitating remote learning during the pandemic. The Technology Acceptance Model (TAM) was employed as the theoretical framework to examine students' views on self-efficacy, facilitating conditions, ease of use, usefulness, and intention to use. The results showcase positive views of MS Teams, with self-efficacy rated the highest among the five constructs, followed by ease of use, facilitating conditions, intention to use, and usefulness. Additionally, no significant differences were found in students' perceptions based on gender. MS Teams has proven to be a successful platform for delivering online learning and communicating, bridging the divide of distance and time in teaching and learning. As discussions about the future of higher education in the post-pandemic world have commenced among academia and university officials, it is crucial to consider the impact of COVID-19 on student learning and provide suggestions for a more sustainable and effective post-pandemic education. © 2023 IEEE.

Convolutional Neural Network Based Feature Hybridization For Ct Scan Images: Covid-19 Detection Perspective

Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. Early diagnosis is only the proactive process to resist against the unwanted death. However, machine vision-based diagnosis systems show unparalleled success with higher accuracy and low false diagnosis rate. Working with the proposed method, this research has found that Computed Tomography (CT) provides more satisfactory outcomes regarding all the performance metrics. The proposed method uses a feature hybridization technique of concatenating the textural features with neural features. The literature review suggests that medical experts recommended chest CT in covid diagnosis rather than chest X-ray as well as RT-PCR. It is found that chest CT is more effective in diagnosis for being low false-negative rate. Moreover, the proposed method has used segmentation technique to dig the potential region of interest and obtain accurate features. Compared with different CNN classifier, such as, VGG-16, AlexNet, VGG-19 or ResNet50 and scratch model also. To obtain the satisfactory performance VGG-19 was used in this study. The Proposed machine learning based fusion technique achieves superior performance according to COVID-19 positive or negative with the accuracy of 98.63%, specificity of 99.08% and sensitivity of 98.18%. © 2022 IEEE.

Acute Kidney Injury after Covid 19 Infection in Chronic Kidney Disease Patients of Bangladesh: Retrospective Study

Introduction: COVID 19 pandemic has caused unprecedented devastation worldwide. Spectrum of Covid 19 illness is wide and variable. Risk of mortality is increased in chronic kidney disease patients, during coronavirus disease. CKD is an independent risk factor for poor outcome. AKI is also common in COVID-19 patients who are hospitalized. This study was undertaken to see the outcome of Covid-19 infection in CKD patients. Method(s): This retrospective observational study was carried out in the Kidney Foundation Hospital and Research Institute, Bangladesh from January 2021 to July 2022. One hundred CKD patients who were on regular follow up in the outpatient department and developed COVID-19 as confirmed by reverse transcription polymerase chain reaction (RT-PCR) test underwent chart review after they consented to be part of the study. Their clinical parameters, treatment regiments and laboratory investigations were noted in a data collection sheet. Data was analyzed by Statistical Analysis Software. Result(s): The mean age of the patients was 55.2 years. Of them 43% were female. Diabetes mellitus was the most common comorbidity, seen in 65% of the patients. 24% were CKD stage 4 or 5 prior to the onset of COVID-19, rest were of earlier stage. Hospitalization was required in 65.3% patients;41.1% required oxygen, steroid given in 19.8% patients,8.4% required ICU transfer. 7 patients died, all of respiratory failure. Treatment with antiviral, biologics like Tocilizumab and plasma exchange was not commonly done. AKI developed in 28% of the patients during the course of the illness. Males were more prone to develop AKI (p = 0.23). People with longer duration of symptoms had higher incidence of AKI (p < 0.0001). AKI incidence did not vary according to baseline eGFR (p = 0.16). Among those who developed AKI, 17.9% required temporary dialysis and 7.1% went on to develop end stage kidney disease. Interim outcomes such as hospitalization, oxygen requirement, ICU transfer and death did not vary according to development of AKI. Conclusion(s): People with chronic kidney disease and other comorbid conditions are at higher risk for more serious COVID-19 illness. In our study it has been shown that a significant proportion of CKD patients developed AKI after COVID 19 infection of which a number of patients develop end stage kidney disease and required renal replacement therapy. No conflict of interestCopyright © 2023

iConDet2: An Improved Conjunctivitis Detection Portable Healthcare App Powered by Artificial Intelligence

Conjunctivitis is one of the common and contagious ocular diseases which affects the conjunctiva of the human eye. Both the bacterial and viral types of it can be treated with eye drops and other medicines. It is important to diagnose the disease at its early stage to realise the connection between it and other diseases, especially COVID-19. Mobile applications like iConDet is such a solution that performs well for the initial screening of Conjunctivitis. In this work, we present with iConDet2 which provides an advanced solution than the earlier version of it. It is faster with a higher accuracy level (95%) than the previously released iConDet. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Information Dissemination During the COVID-19 Pandemic in Bangladesh

The main aim of this study is to identify the role of information dissemination on urban and rural citizens of Bangladesh during the COVID-19 pandemic and the role of misinformation in this process. The study also aimed at finding appropriate counter misinformation strategies regarding COVID-19. An online questionnaire was prepared to collect the viewpoints of the urban and rural citizens of Bangladesh regarding dissemination of information during COVID-19, misinformation regarding COVID-19, and counter misinformation strategies. Along with demographic and general information, a five-point Likert scale was used to measure COVID-19 related misinformation beliefs and how to counter them. Chi square tests were used to determine the association between current residency, information sources, the importance of information dissemination, reactions after getting COVID related information, and evaluative steps after getting information and before disseminating it. Additionally, nonparametric Mann–Whitney U and Kruskal–Wallis tests were conducted to know the significance of difference in respondents' assessment on COVID-19 related misinformation in terms of their demographic characteristics. Cronbach's alpha score was obtained to see the reliability of the questionnaire items. The current study reveals that both urban and rural citizens of Bangladesh are influenced by information dissemination regarding COVID-19 and they have lower level of misinformation belief. The respondents have differences in misinformation belief by different demographic groups. Respondents' educational status, information literacy, sources of getting information, and evaluative steps after getting information have significant differences in misinformation belief. The study also noticed the support of respondents for countering misinformation strategies regarding COVID-19 © Abu Sayed, Md. Ziaul Haque, Md. Rifat Mahmud, 2022

Utilization of telehealth services in low- and middle-income countries amid the COVID-19 pandemic: a narrative summary.

BACKGROUND: During the current period of the pandemic, telehealth has been a boon to the healthcare system by providing quality healthcare services at a safe social distance. However, there has been slow progress in telehealth services in low- and middle-income countries with little to no evidence of the cost and effectiveness of such programmes. OBJECTIVE: To provide an overview of the expansion of telehealth in low- and middle-income countries amid the COVID-19 pandemic and identify the challenges, benefits, and costs associated with implementing telehealth services in these countries. METHODS: We performed a literature review using the search term: '*country name* AND ((telemedicine[Title][Abstract]) OR (telehealth[Title][Abstract] OR eHealth[Title][Abstract] OR mHealth[Title][Abstract]))'. Initially, we started with 467 articles, which were reduced to 140 after filtering out duplicates and including only primary research studies. Next, these articles were screened based on established inclusion criteria and 44 articles were finalised to be used in the review. RESULTS: We found telehealth-specific software being used as the most common tool to provide such services. Nine articles reported patient satisfaction of greater than 90% with telehealth services. Moreover, the articles identified the ability to make a correct diagnosis to resolve the condition, efficient mobilisation of healthcare resources, increased accessibility for patients, increased service utilisation, and increased satisfaction as benefits of telehealth services, whereas inaccessibility, low technological literacy, and lack of support, poor security standards and technological concerns, loss of interest by the patients, and income impacts on physicians as challenges. The review could not find articles that explored the financial information on telehealth programme implementation. CONCLUSION: Although telehealth services are growing in popularity, the research gap on the efficacy of telehealth is high in low- and middle-income countries. To better guide the future development of telehealth services, rigorous economic evaluation of telehealth is needed.

The effects of misinformation on COVID-19 vaccine hesitancy in Bangladesh

Purpose: The main purpose of this study is to assess the prevalence of COVID-19 vaccine hesitancy among the general population in Bangladesh and the role of misinformation in this process. Design/methodology/approach: An online survey was conducted to assess COVID-19 vaccine hesitancy among ordinary citizens. In addition to demographic and vaccine-related information, a five-point Likert scale was used to measure vaccine-related misinformation beliefs and how to counter them. Chi-square tests were used to examine the relationship between demographic variables and vaccine acceptance. A binary logistic regression analysis was conducted to identify vaccine hesitancy by different demographic groups. Nonparametric Mann–Whitney and Kruskal–Wallis tests were performed to determine the significance of difference between demographic groups in terms of their vaccine-related misinformation beliefs. Finally, the total misinformation score was computed to examine the correlation between vaccine hesitancy and the total score. Findings: This study found that nearly half of the respondents were willing to receive COVID-19 vaccine, whereas more than one third of the participants were unsure about taking the vaccine. Demographic variables (e.g., gender, age and education) were found to be significantly related to COVID-19 vaccine acceptance. The results of binary logistic regression analysis showed that respondents who were below 40 years of age, females and those who had lower education attainments had significantly higher odds of vaccine hesitancy. There were significant differences in participants' vaccine-related misinformation beliefs based on their demographic characteristics, particularly in the case of educational accomplishments. A highly significant negative correlation was found between total misinformation score and vaccine acceptance. Research limitations/implications: The survey was conducted online, and therefore, it automatically precluded non-internet users from completing the survey. Further, the number of participants from villages was relatively low. Overall, the results may not be representative of the entire population in Bangladesh. Practical implications: The findings of this paper could guide government agencies and policymakers in devising appropriate strategies to counter COVID-related misinformation to reduce the level of vaccine hesitancy in Bangladesh. Originality/value: To the authors' best knowledge, this study is the first to measure the level of COVID-19 vaccine hesitancy and the influence of misinformation in this process among the general public in Bangladesh. © 2021, Emerald Publishing Limited.

Detecting COVID-19 from Lung Computed Tomography Images: A Swarm Optimised Artificial Neural Network Approach

COVID-19 has affected many people across the globe. Though vaccines are available now, early detection of the disease plays a vital role in the better management of COVID-19 patients. An Artificial Neural Network (ANN) powered Computer Aided Diagnosis (CAD) system can automate the detection pipeline accounting for accurate diagnosis, overcoming the limitations of manual methods. This work proposes a CAD system for COVID-19 that detects and classifies abnormalities in lung CT images using Artificial Bee Colony (ABC) optimised ANN (ABCNN). The proposed ABCNN approach works by segmenting the suspicious regions from the CT images of non-COVID and COVID patients using an ABC optimised region growing process and extracting the texture and intensity features from those suspicious regions. Further, an optimised ANN model whose input features, initial weights and hidden nodes are optimised using ABC optimisation classifies those abnormal regions into COVID and non-COVID classes. The proposed ABCNN approach is evaluated using the lung CT images collected from the public datasets. In comparison to other available techniques, the proposed ABCNN approach achieved a high classification accuracy of 92.37% when evaluated using a set of 470 lung CT images. Author

Students' Learning Obstacles in Solving Math Story Problems on Number Material during the Covid-19 Outbreak

The Covid-19 outbreak greatly impacts education, particularly the ineffective learning process, with some pupils having learning obstacles. This study analyses the learning obstacle experienced by madrasah ihtidaiyah pupils in solving math story problems on numbers material during the Covid-19 outbreak. The research used a descriptive qualitative method. The data took from 19 fourth-grade pupils at one of the madrasah ibtidaiyah m Bandung district tough tests and interviews. The data analysis through me stages of data reduction, data display, and conclusion. The results showed that pupils' learning obstacles were ontogemc, didactical, and epistemological. In the ontogemc obstacle, some pupils did not get the concept of summing fractions The didactical obstacle that occurs in pupils due to learning during the outbreak held alternately onlme and offline, onlme learning held through the WhatsApp group, which was less effective, and no learning design combined onlme and offline methods effectively The epistemological obstacle experienced by pupils was when the teacher gave questions in a different context from the examples It happened due to the limited time of mathematics lessons during the outbreak. © 2022 American Institute of Physics Inc.. All rights reserved.

Sentiment Analysis of Public on Social Media about Covid-19 origin theory using Machine Learning

Covid-19 has been found in Wuhan, China, for approximately a year and a half ago, and the virus's origin remains a mystery. However, it has been in the news in recent weeks, with reports suggesting that an infectious disease was spilled in a Chinese laboratory, which was previously refuted by a hoax in the area. In this research paper, we have presented a model where there will be a sentimental analysis based on users' comments on social media about the origin of corona virus. Nowadays most people express their feelings and the truth around them and many lies on social media. And we are taking this opportunity to do a sentimental analysis of the true, false, and confusing feelings that people have expressed on social media about the origin of corona virus. We used 20000 data (comments) taken from corona virus-related popular Facebook news posts. In order to achieve the maximum results, we used five distinct machine learning classifiers, and our support vector machine and logistic regression model outscored them all. The support vector model had a testing accuracy rate of 83.73 %, whereas logistic regression had an accuracy rate of 81.39 %. The important thing about our research is that at the end of the whole work, thousands of people's personal feelings, truths, hesitations, and confusion come together to know a strong possibility about the origin of the corona virus. © 2022 IEEE.

Augmented Reality (AR) and Virtual Reality (VR) Applications during Covid-19 Pandemic among Preclinical Medical and Dentistry Students: A Mini-Review

The efficacy of online learning in delivering theoretical knowledge with appropriate content to students is imperative, especially in the Covid-19 pandemic era. Substantial interactive teaching materials were developed for higher education. However, some were designed immensely general, especially in fulfilling the syllabus of preclinical medical and dentistry students. Augmented reality (AR) is an interactive three-dimension (3D) experience that uses computers to overlay virtual information in the real world whereas virtual reality (VR) is a computer-generated artificial recreation of a real-life experience or situation. Interestingly, both can be complemented and integrated into online and traditional teaching methods. Implementation of these technologies will increase the learning efficacy in understanding the human body's anatomical and physiological changes in the normal or pathological state. As AR and VR technologies are continuously evolving, this review provides the preview and current updates on AR and VR applications in medical and dentistry education which may benefit the educators within these specialities. © 2022 UPM Press. All rights reserved.

Information and Communications Technology (ICT) as a Teaching and Learning Tool: A Study of Students' Readiness and Satisfaction

Students and academics in higher education institutions (HEIs) were perilously hit by the unparalleled changes due to the COVID-19 pandemic. Within a span of less than a month, teaching and learning activities were shifted online to warrant continuity. This study intends to probe the online learning readiness and satisfaction among university students within the scope of students' prior ICT knowledge and the university's ICT infrastructure. This study employs a quantitative approach with a questionnaire as the research instrument. A sample size of 1,692 Sunway University students in the Ministry of Education (MOE) General Studies subjects were chosen. The data were analysed descriptively, and the results revealed that students are generally ready for online learning, and they are satisfied with the ICT amenities provided. As a result, both students and Sunway University are wellprepared, with the major implication that student preparation and satisfaction, as well as infrastructures, are critical to scaffold the accelerated transition in the use of online learning. ©Authors.

Emotional intelligence, spiritual intelligence and psychological well-being: Impact on society

Mental health issues are frequently ignored and overlooked by society because they cannot be physically expressed. Furthermore, this perception has long been entrenched in the Malaysian society as the stigma against individuals who suffer from this problem remains at the same level even though awareness about mental health issues has spread significantly. Therefore, this study was conducted to evaluate the relationship between emotional intelligence, spiritual intelligence and psychological well-being among counseling clients in one of the government agencies in Pahang, Malaysia, during the Covid-19 pandemic. A total of 157 counseling clients answered the questionnaire. SmartPLS was used to evaluate the data content of the questionnaire and test the research hypotheses. The results of the analysis yielded several significant findings. First, there was a positive and significant relationship between emotional intelligence (i.e., self-awareness, social awareness and emotional receptivity) and spiritual intelligence. Second, there is a positive and significant relationship between emotional intelligence and psychological well-being. Third, there is a positive and significant relationship between spiritual intelligence and psychological well-being. Fourth, there is a positive and significant relationship between emotional intelligence, spiritual intelligence and psychological well-being. The findings of this study confirmed the important role of spiritual intelligence as a mediating variable in the relationship between emotional intelligence and psychological well-being. Furthermore, the findings of this study can be used as essential recommendations to help practitioners understand the diversity of perspectives on the construct of emotional intelligence and develop a spiritual intelligence management plan in counseling sessions to help those with mental issues achieve and maintain their emotional well-being in daily life.

Impact of COVID-19 Pandemic on Mental Health among Health Sciences Students: A single centre study

COVID-19 pandemic has caused psychological impact on human being. This study is aimed to determine the prevalence and predictors of depression, anxiety and stress among undergraduate students during COVID-19 pandemic. A cross-sectional survey using Depression Anxiety Stress Scale 21 was conducted on 319 students. The results demonstrated that 21.6% -33% of the students had moderate to extremely severe depression, anxiety, and stress. The number of close friends and number of persons living at home were identified as their most significant predictors. These findings provide preliminary awareness towards understanding the mental health issue among college students during the COVID-19 pandemic.

Impact on teaching strategies in Saudi Arabian universities owing to COVID-19 pandemic

The COVID-19 pandemic emerged with an unparalleled emergency in every walk of life. In the area of education, this emergency has led to the massive termination of face-to-face activities. This affected schools and universities in more than 190 countries globally in order to prevent the spread of the virus. The Ministry of Education of Saudi Arabia issued a directive to stop holding face-to-face classes to mitigate the widespread of the novel coronavirus. This directive led to three main areas of action: (1) placement of distance learning modes via a variety of methods and technological platforms, (2) support and deployment of educational workers and communities, and (3) a great concern for the well-being and general health of students. The aim of this chapter is to highlight impacts that these actions had on educational communities. Key recommendations are offered based on student's feedback and training sessions. Students from 14 different universities were sampled including the host university, i.e., Imam Abdulrahman Bin Faisal University, to see pre-training and post-training responses with respect to their experiences and community engagement while learning via online resources. Over 400 responses were received and analyzed to gain the understanding of the current state of how Saudi universities tackled the impacts caused by the COVID-19 pandemic. The training-based research was conducted to measure the actual level of teaching strategies used at Saudi Arabian universities. © KONINKLIJKE BRILL NV, LEIDEN, 2022.

Exploring Public Attitude Towards Children by Leveraging Emoji to Track Out Sentiment Using Distil-BERT a Fine-Tuned Model

Sentiment analysis is a computational method that extracts emotional keywords from different texts through initial emotion analysis (e.g., Happy, Sad, Positive, Negative & Neutral). A recent study by a human rights organization found that 30% of children in Bangladesh are being abused on online in the COVID-19 epidemic by various obscene comments. The main goal of our research is to collect textual data from social media and classify the way children are harassed by various abusive comments online through the use of emoji in a text-mining method and to expose to society the risks that children face online. Another goal of this study is to set a precedent through a detailed study of child abuse and neglect in the big data age. To make the work effective, 3373 child abusive comments are collected manually from online (e.g. Facebook, Newspapers and various Blogs). At present, there is still a very limited number of Bengali child sentiment analysis studies. Fine-tuned general purpose language representation models, such as the BERT family model (BERT, Distil-BERT), and glove word embedding based CNN and Fast-Text models have been used to successfully complete the study. We show that Distil-BERT defeated BERT, Fast-Text, and CNN by 96.09% (relative) accuracy, while Bert, Fast-Text and CNN have 93.66%, 95.73%, and 95.05%, respectively. But observations show that the accuracy of the Distil-BERT does not differ much from the rest of the models. From our analysis, it can be said that the pre-trained models performed outstanding and in addition, child sentiment analysis can serve as a potential motivator for the government to formulate child protection policies and build child welfare systems. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Role of Medical Libraries for Implementing Knowledge Management During Covid-19 Pandemic: An Empirical Study

The aim of this study was to examine the state of Knowledge Management (KM) in medical libraries in Bangladesh. It also explores the awareness and viewpoints of the doctors, researchers, students and librarians regarding KM during the pandemic. The questionnaire survey was adopted for this study. The Data gathering procedure was performed through Google forms. The questionnaire was distributed using email and social networking sites. Among 29 public medical colleges, this quantitative research was conducted in three medical college libraries in Bangladesh. The selected medical libraries were: Dhaka medical college library, Chittagong medical college library and Sylhet MAG Osmani medical college library. This study explored the barriers to KM in medical libraries and the possible implementation areas of KM. The study found that participants are aware of the concept of KM. They are also aware of the necessary KM tools and the probable KM implementation areas in the medical libraries. Present paper also revealed that lack of institutional policy, inadequate support from top management, scarcity of resources as barriers towards implementing KM in the medical libraries in Bangladesh. Further studies should be conducted on the same issues in other sectors as present study was limited to three medical libraries. This study is novel, in the sense that it is the first of its kind to examine KM implementation in medical libraries during pandemic situations in Bangladesh. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Teacher Technostress and Coping Mechanisms During COVID-19 Pandemic: A Systematic Review

The COVID-19 pandemic has struck many countries around the world. Most countries declared a health emergency to halt the spread of COVID-19 cases, putting all citizens on lockdown. This has caused schools to implement distance learning strategies with little or no prior experience. The COVID-19 pandemic has pushed the world’s education system into an unstructured, emergency remote education mode. New issues arise as a result of the change from offline to online instruction, as well as achieving work-life balance. Hence this systematics review was guided by Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) in order to identify the current research on teacher technostress. The factors that contribute to the said factor and ways of overcoming teachers’ technostress during online learning would be investigated. A number of 52 related studies were accessed from Jun 2021 until September 2021 for this study. Articles published between 2019 and 2021 were sought from two leading databases which are Web of Science (WOS) and Scopus. Thus, this review systematically identifies teacher technostress and coping mechanisms during COVID-19 pandemic. It was found that several effects were caused by the technology use. In light of the result, stakeholders need to prepare a proactive way to make sure teachers are less stressed with this new norm of teaching and learning. © 2022