Fear from COVID-19 and technology adoption: the impact of Google Meet during Coronavirus pandemic

This study seeks to explore the effect of fear emotion on students' and teachers' technology adoption during COVID-19 pandemic. The study has made use of Google Meet© as an educational social platform in private higher education institutes. The data obtained from the study were analyzed by using the partial least squares structural equation modeling (PLS-SEM) and machine learning algorithms. The main hypotheses of this study are related to the effect of COVID-19 on the adoption of Google Meet as COVID-19 rise s various types of fear. During the Coronavirus pandemic, fear due to family lockdown situation, fear of education failure and fear of losing social relationships are the most common types of threat that may face students and teachers/educators. These types of fears are connected with two important factors within TAM theory, which are: perceived ease of use (PEOU) and perceived usefulness (PU), and with another external factor of TAM, which is subjective norm (SN). The results revealed that both data analysis techniques have successfully provided support to all the hypothesized relationships of the research model. More interesting, the J48 classifier has performed better than the other classifiers in predicting the dependent variable in most cases. [ FROM AUTHOR] Copyright of Interactive Learning Environments is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Factors Indicating Media Dependency and Online Misinformation Sharing in Jordan

Although social media is a vital platform in our life, it is blamed for poor efforts to moderate content included mis/disinformation and fake news. This could have an impact on its legacy and on sustainability in society in the long term. This research examined the role of social media in spreading misinformation during the COVID-19 outbreak in Jordan. A cross-sectional design questionnaire (350 responses) was used. The results revealed that social media played a key role in updating users with COVID-19 information. However, the availability of misinformation remained highly prevalent. Respondents revealed that they relied heavily on social media for information gathering and knowledge sharing about COVID-19 updates. The role of behavioural intention remained prominent and highly significant for these two reasons. Their behavioural intention was linked to the sharing of unchecked information, suggesting that online information in Jordan needs greater regulation to reduce the spread of misinformation.

SEM-ANN-based approach to understanding students' academic-performance adoption of YouTube for learning during Covid.

A hybrid analysis of Structural Equation Modeling (SEM) and Artificial Neural Network (ANN), through SmartPLS and SPSS software, as well as the importance-performance map analysis (IPMA) were used to examine the impact of YouTube videos content on Jordanian university students' behavioral intention regarding eLearning acceptance, in Jordan. According to the evaluation of both ANN and IPMA, performance expectancy was the most important and, theoretically, several explanations were provided by the suggested model regarding the impact of intention to adopt eLearning from Internet service determinants at a personal level. The findings coincide greatly with prior research indicating that users' behavioral intention to adopt eLearning is significantly affected by their performance expectancy and effort expectancy. The paper contributed to technology adoption e.g., YouTube in academia, especially in Jordan. Respondents showed a willingness to employ and adopt the new technology in their education. Finally, the findings were presented and discussed through the UTAUT and TAM frameworks.

USING E-LEARNING FACTORS TO PREDICT STUDENT PERFORMANCE IN THE PRACTICE OF PRECISION EDUCATION

The emergence of Covid-19 has accelerated digital transformation, while simultaneously disrupting traditional education. The implementation of distance education practices produced a massive amount of data generated by the deployed learning management systems. Educational data mining tools using machine learning methods can produce thorough student-level insights into what has become known as precision education. This study aims to investigate a convenient approach to analyze a data set of 480 students in the Middle East using three supervised machine learning methods (artificial neural networks, decision trees, and Naive Bayes) to predict overall performance using SPSS. The findings indicate that the naive Bayes algorithm achieved the highest accuracy of 89.85%, while the artificial neural networks algorithm achieved the lowest variance, with a standard deviation of 2.37. Besides, there are more valuable insights beyond accuracy that other Machine learning models can provide in the SPSS environment, such as visual representation and normalized importance of independent variables. Moreover, in the context of missing student data, the data set was evaluated if e-learning parameters alone can predict student performance. The findings suggest that e-learning parameters alone can predict student performance with an average accuracy of 84.49%. This study contributes to limit grade inflation in the age of online learning due to educational malpractices.

Factors Affecting the Use of Smart Mobile Examination Platforms by Universities’ Postgraduate Students during the COVID-19 Pandemic: An Empirical Study

Recent years have seen an increasingly widespread use of online learning technologies. This has prompted universities to make huge investments in technology to augment their position in the face of extensive competition and to enhance their students’ learning experience and efficiency. Numerous studies have been carried out regarding the use of online and mobile phone learning platforms. However, there are very few studies focusing on how university students will accept and adopt smartphones as a new platform for taking examinations. Many reasons, but most recently and importantly the COVID-19 pandemic, have prompted educational institutions to move toward using both online and mobile learning techniques. This study is a pioneer in examining the intention to use mobile exam platforms and the prerequisites of such intention. The purpose of this study is to expand the Technology Acceptance Model (TAM) by including four additional constructs: namely, content quality, service quality, information quality, and system quality. A self-survey method was prepared and carried out to obtain the necessary basic data. In total, 566 students from universities in the United Arab Emirates took part in this survey. Smart PLS was used to test the study constructs and the structural model. Results showed that all study hypotheses are supported and confirmed the effect of the TAM extension factors within the UAE higher education setting. These outcomes suggest that the policymakers and education developers should consider mobile exam platforms as a new assessment platform and a possible technological solution, especially when considering the distance learning concept. It is good to bear in mind that this study is initial and designed to explore using smartphones as a new platform for student examinations. Furthermore, mixed-method research is needed to check the effectiveness and the suitability of using the examination platforms, especially for postgraduate higher educational levels.

Acceptance of Google Meet during the Spread of Coronavirus by Arab University Students

The COVID-19 pandemic not only affected our health and medical systems but also has created large disruption of education systems at school and universities levels. According to the United Nation’s report, COVID-19 has influenced more than 1.6 billion learners from all over the world (190 countries or more). To tackle this problem, universities and colleges have implemented various technologically based platforms to replace the physical classrooms during the spread of Coronavirus. The effectiveness of these technologies and their educational impact on the educational sector has been the concern of researchers during the spread of the pandemic. Consequently, the current study is an attempt to explore the effect of Google Meet acceptance among Arab students during the pandemic in Oman, UAE, and Jordan. The perceived fear factor is integrated into a hybrid model that combines crucial factors in TAM (Technology acceptance Model) and VAM (Value-based Adoption Model). The integration embraces perceived fear factor with other important factors in TAM perceived ease of use (PEOU) and perceived usefulness (PU) on the one hand and technically influential factor of VAM, which are perceived technicality (PTE) and perceived enjoyment (PE) on the other hand. The data, collected from 475 participants (49% males and 51% females students), were analyzed using the partial least squares-structural equation modelling (PLS-SEM). The results have shown that TAM hypotheses of usefulness and easy to use have been supported. Similarly, the results have supported the hypotheses related to VAM factors of being technically useful and enjoying, which helps in reducing the atmosphere of fear that is created due to the spread of Coronavirus.

The Role of Technology Acceptance in Healthcare to Mitigate COVID-19 Outbreak

The recent decade has included huge achievements in the development for information technologies in healthcare. Now, these technologies can be employed as part of the response to the COVID-19 pandemic. Information technologies in healthcare are crucial to store, manage and exchange the clinical data. On the other hand, the success or failure of a specific technology relies on the acceptance to use that technology. There is a need to assess the user’s technology acceptance prior to the development or improvements for that technology. The study objective is to systematically review the studies that empirically had evaluated the acceptance of technology in healthcare through the technology acceptance model (TAM), its extensions and integrated models based on it. Also, the study will highlight the various studied technologies in healthcare arena, and how these technologies can be utilized to provide the health services, as a respond to the on-going pandemic. PRISMA guidelines were used to perform the review;and the search process has been completed using six digital libraries: Google Scholar, PubMed, IEEE Xplore, Springer Link, ACM, and Science Direct. Out of 1768 studies, a total of 99 empirical studies were found to be eligible and included in this study. A thorough statistical analysis was achieved, to understand the situation of technology acceptance as in the recent decade. The analysis included the key factors, as they were extensively utilized to clarify the technology acceptance, along with the key confirmed hypotheses to build robust and valid technology acceptance models in healthcare. It was found that electronic records, tele-medicine and mobile health solutions have attracted the most of researchers in the last ten years. Where the acceptance of those solutions was explored, through various user types and settings, within different countries particularly Taiwan and the United States;who are leading this research domain.

Fear from covid-19 and technology adoption: The impact of google meet during coronavirus pandemic

ABSTRACT This study seeks to explore the effect of fear emotion on students' and teachers' technology adoption during COVID-19 pandemic. The study has made use of Google Meet© as an educational social platform in private higher education institutes. The data obtained from the study were analyzed by using the partial least squares structural equation modeling (PLS-SEM) and machine learning algorithms. The main hypotheses of this study are related to the effect of COVID-19 on the adoption of Google Meet as COVID-19 rise s various types of fear. During the Coronavirus pandemic, fear due to family lockdown situation, fear of education failure and fear of losing social relationships are the most common types of threat that may face students and teachers/educators. These types of fears are connected with two important factors within TAM theory, which are: perceived ease of use (PEOU) and perceived usefulness (PU), and with another external factor of TAM, which is subjective norm (SN). The results revealed that both data analysis techniques have successfully provided support to all the hypothesized relationships of the research model. More interesting, the J48 classifier has performed better than the other classifiers in predicting the dependent variable in most cases. (PsycInfo Database Record (c) 2020 APA, all rights reserved)