The continuous intention to use e-learning, from two different perspectives

During the recent vast growth of digitalization, e-learning methods have become the most influential phenomenon at higher educational institutions. E-learning adoption has proved able to shift educational circumstances from the traditional face-to-face teaching environment to a flexible and sharable type of education. An online survey was conducted, consisting of 30 teachers and 342 students in one of the universities in the United Arab Emirates. The results show that teachers’ and students’ perceived technology self-efficacy (TSE), ease of use (PEOU), and usefulness (PU) are the main factors directly affecting the continuous intention to use technology. Instructors’ technological pedagogical content knowledge (TPACK) and perceived organizational support (POS) positively affect the intention to use the technology, whereas students’ controlled motivation (CTRLM) has a greater influence on their intention to use the technology, due to the type of intrinsic and extrinsic motivation that they have and which they can develop throughout the process of learning. The findings support the given hypotheses. In addition, they provide empirical evidence of a relationship between perceived organizational support and perceived pedagogical content knowledge. In fact, they are considered the key factors that support the use of technology continuously

Investigating a theoretical framework for e-learning technology acceptance

E-learning has gained recognition and fame in delivering and distributing educational resources, and the same has become possible with the occurrence of Internet and Web technologies. The research seeks to determine the factors that influence students' acceptance of E-learning and to find out the way these factors determine the students' intention to employ E-learning. A theoretical framework was developed based on the technology acceptance model (TAM). To obtain information from the 270 university students who utilized the E-learning system, a questionnaire was formulated. The results revealed that “social influence, perceived enjoyment, self-efficacy, perceived usefulness, and perceived ease of use” are the strongest and most important predictors in the intention of and students towards E-learning systems. The outcomes offer practical implications for practitioners, lawmakers, and developers in effective E-learning systems implementation to improve ongoing interests and activities of university students in a virtual E-learning atmosphere, valuable recommendations for E-learning practices are given by the research findings, and these may turn out to be as guidelines for the efficient design of E-learning systems

The Karstic Corrosion in Al-Dalieh region

 Al-Dalieh region lies in the Western slopes of the Syrian coastal mountains series,. and the area is lies near the Assian-African fault, that caused rising The oldest Jurrasic Layers which formed the tops of the mountains, while the newest Kretassic Layers lied on the slopes, In addition there are three-Nugeen Layers covers a small area .And as a result of permeability of these rocks, the water go towards underground through holes This lithological and hydrogeological installation caused forming distinctive karstic surface and groundwater forms by the dissolution and melting of the limestone rocks. So this research seeks to study the characteristics which returned to the Karstic Corrosion in Al-Dalieh region and knowing the Rate of Karastic Corrosion in the water of Al-Dalieh spring, and thus estimate the time of the emergence of the karstic forms in this region

A systematic review on sequence-to-sequence learning with neural network and its models

We develop a precise writing survey on sequence-to-sequence learning with neural network and its models. The primary aim of this report is to enhance the knowledge of the sequence-to-sequence neural network and to locate the best way to deal with executing it. Three models are mostly used in sequence-to-sequence neural network applications, namely: recurrent neural networks (RNN), connectionist temporal classification (CTC), and attention model. The evidence we adopted in conducting this survey included utilizing the examination inquiries or research questions to determine keywords, which were used to search for bits of peer-reviewed papers, articles, or books at scholastic directories. Through introductory hunts, 790 papers, and scholarly works were found, and with the assistance of choice criteria and PRISMA methodology, the number of papers reviewed decreased to 16. Every one of the 16 articles was categorized by their contribution to each examination question, and they were broken down. At last, the examination papers experienced a quality appraisal where the subsequent range was from 83.3% to 100%. The proposed systematic review enabled us to collect, evaluate, analyze, and explore different approaches of implementing sequence-to-sequence neural network models and pointed out the most common use in machine learning. We followed a methodology that shows the potential of applying these models to real-world applications

A systematic review of text classification research based on deep learning models in Arabic language

Classifying or categorizing texts is the process by which documents are classified into groups by subject, title, author, etc. This paper undertakes a systematic review of the latest research in the field of the classification of Arabic texts. Several machine learning techniques can be used for text classification, but we have focused only on the recent trend of neural network algorithms. In this paper, the concept of classifying texts and classification processes are reviewed. Deep learning techniques in classification and its type are discussed in this paper as well. Neural networks of various types, namely, RNN, CNN, FFNN, and LSTM, are identified as the subject of study. Through systematic study, 12 research papers related to the field of the classification of Arabic texts using neural networks are obtained: for each paper the methodology for each type of neural network and the accuracy ration for each type is determined. The evaluation criteria used in the algorithms of different neural network types and how they play a large role in the highly accurate classification of Arabic texts are discussed. Our results provide some findings regarding how deep learning models can be used to improve text classification research in Arabic language

The Impact of Big Data Quality Analytics on Knowledge Management in Healthcare Institutions: Lessons Learned from Big Data's Application within The Healthcare Sector

It is widely acknowledged that knowledge management is critical to an organization's survival and growth. Every day, higher education institutions that are considered knowledge centers generate massive volumes of data. When this data is analyzed using appropriate computational methods and technology, it can provide knowledge to improve organizational performance and students' academic experience. Healthcare organizations create massive volumes of data as a result of the usage of digital technologies to manage patient information and the organization's operations. When used successfully, this data aids in the creation of information that improves patient health and everyday organizational functioning, as well as the prevention of unfavorable public health scenarios such as the spread of infectious illnesses. This is where big data analytics comes in, providing rational methods for navigating enormous quantities of data to disclose knowledge that assists businesses and analysts in making faster and better decisions. Higher education, like healthcare, creates large amounts of heterogeneous data that hides useful knowledge. As a result, the strategies used by healthcare companies to improve their performance using big data are replicable in the education domain as well. This article examines the use of big data for knowledge management in healthcare using case studies incorporating various analytics and draws parallels to be applied in higher education. As a result, it highlights the possibility of adapting analytics technology and tools from healthcare to higher education with appropriate revisions and adaptations

Factors Affecting Medical Students’ Acceptance of the Metaverse System in Medical Training in the United Arab Emirates

Aim: Medical training activities have been disrupted in many regions following the outbreak and rapid spread of the coronavirus disease 2019 (COVID-19) across the globe. The most affected areas include organizations’ process of leveraging high-tech medical equipment from abroad to facilitate a practical approach to learning. Also, as countries implemented COVID-19 safety regulations, it became difficult for organizations to conduct face-to-face training. Consequently, non-face-to-face learning methods have been introduced in the medical field to enable instructors to remotely engage with learners. The current research investigated the students' perceptions of the use of metaverse systems in medical training within the medical community of the United Arab Emirates (UAE).   Methods: A conceptual model comprising the adoption properties of personal innovativeness, perceived enjoyment, and Technology Acceptance Model concepts was utilised. The current research targeted students in UAE medical universities. Data was obtained by conducting online surveys that were implemented in the winter semester of 2021/2022 between 15th February and 15th May 2022. 500 questionnaires were issued to students following their voluntary participation and 435 questionnaire responses were obtained i.e. an 87% response rate. The research team tested the measurement model employing Structural Equation Modeling using Smart Partial Least Squares Version (3.2.7).   Results: Statistically significant associations were confirmed to exist between Personal Innovativeness (PI) influenced by both the Perceived Ease of Use (PEOU), and Perceived Usefulness (PU) (β= 0.456) and (β= 0.563) at P<0.001. The statistically significant associations involving Perceived Enjoyment (EJ) and PEOU and PU (β= 0.554, P<0.05), (β= 0.571, P<0.05) were further confirmed. Additionally, PEOU had a relationship with PU (β= 0.863, P<0.001). Eventually, PEOU and PU significantly influenced the participants’ inclination to use the metaverse technology with (β= 0.745, P<0.001) and (β= 0.416, P<0.001), respectively.     Conclusion: Conclusions made during the research add to the existing literature regarding technology adoption by demonstrating how adoption properties, perceived enjoyment, and personal innovativeness influence students’ perceptions concerning innovational technologies used in education.   Conflicts of interest: None declared

The Impact of Hospital Demographic Factors on Total Quality Management Implementation: A Case Study of UAE Hospitals

Aim:  Maintaining service quality and value using quality and management tools is crucial in any organization. In essence, improving service quality boosts both efficiency of organizations and consumer pleasure. The deployment of quality development programs such as Total Quality Management (TQM) is one technique that businesses may employ to deliver exceptional customer service. The health sector, in particular, is one of the industries that require TQM adoption due to its complexity and the need for constant service improvement. TQM helps to improve service quality in health facilities through advanced clinical and administrative procedures. This research comprehensively assesses TQM levels and the impact of hospital demographics on its implementation process in hospitals in the United Arab Emirates (UAE).   Methods:  The study used a quantitative research strategy based on a survey study design. Questionnaires were used to gather primary data from respondents deployed a self-administered technique. 1850 questionnaires were delivered to the hospital's senior staff based on their number in each hospital. Of the 1850 questionnaires distributed, 1238 usable questionnaires were analyzed, yielding a response rate of 66.9%. The study used a binary logistic regression model to determine if hospital demographics affected TQM implementation. The study data were examined and analysed using version 25.0 of the SPSS software.   Results: The results show that most of the health facilities with an overall TQM between 4.12 and 4.82 were utilized, governmental, accredited and utilized and large hospitals, while the hospitals with a mean between 2.91 and 3.45 were small, unaccredited private, and non-specialised. Thus, large hospitals have a higher TQM utilization rate than small hospitals. In addition, the findings of the t-test revealed that a high TQM is represented by means of 4.68, 4.67, 4.43, and 4.12 for accredited, utilized, governmental and large hospitals. The binary regression analysis also reveals similar results: large, governmental, utilized and accredited hospitals have greater chances of TQM adoption than other categories of hospitals (Exp (B): 1.2; 95%CI: 1.001 – 1.421, P< .05); (Exp (B): 1.3; 95%CI: 1.012 – 1.721, P< .05); (Exp (B): 1.5; 95%CI: 1.127 – 2.051, P< .01); and (Exp   (B): 1.5; 95%CI: 1.102 – 2.012, P< .05); correspondingly. Another observation from the results is that hospitals that implemented technological tools had a greater chance of successfully executing the TQM program than hospitals that did not utilize advanced technologies due to the limited availability of resources (Exp (B): 1.7; 95%CI: 1.332 – 2.187, P< .01). Conclusion: Even though health facilities need to adopt TQM, its implementation depends on the hospital size and demographics that significantly influence the adoption of TQM programs. However, this study will help bridge the current gap on the usage of TQM in the health context by examine the influence of demographic factors on adopting TQM in hospitals. Hence, provide adequate information to help the UAE hospital administrators appropriately execute the TQM program in the hospitals and enhance the efficacy of their operations. &nbsp

The acceptance of social media video for knowledge acquisition, sharing and application: A comparative study among YouYube users and TikTok users’ for medical purposes

YouTube and TikTok have gained increasing recognition as social network sites to support online knowledge acquisition, sharing, and application via social media platforms in the medical field. This study examines which aspect of TikTok and YouTube stimulates doctors, nurses, and any other YouTube and TikTok in the medical setting, to rely on them as sources of knowledge acquisition and sharing to keep their medical repertoire updated. A hybrid model is designed to investigate users’ acceptance of YouTube and TikTok as social media platforms. The model focuses on four main external factors: Content richness, innovativeness, satisfaction, and enjoyment. These factors are connected with two TAM constructs which are perceived ease of use and perceived usefulness. The results have shown that both YouTube and TikTok are affected by PEOU, PU, personal innovativeness, flow theory, and content richness. Both social media networks provide up-to-date sources described as useful, enjoyable, and relevant. Nevertheless, the comparative results have shown that YouTube has deeply influenced users’ medical perception and knowledge compared to TikTok. It is created for the very mere purpose of socialization and self-expression. In contrast, YouTube is used for educational and non-educational purposes due to the type of uploaded content and time management. Therefore, TikTok developers and influencers should initiate highly specialized videos and create content that raises awareness of medical field issues

Factors Affecting Medical Students’ Acceptance of the Metaverse System in Medical Training in the United Arab Emirates

Aim: Medical training activities have been disrupted in many regions following the outbreak and rapid spread of the coronavirus disease 2019 (COVID-19) across the globe. The most affected areas include organizations’ process of leveraging high-tech medical equipment from abroad to facilitate a practical approach to learning. Also, as countries implemented COVID-19 safety regulations, it became difficult for organizations to conduct face-to-face training. Consequently, non-face-to-face learning methods have been introduced in the medical field to enable instructors to remotely engage with learners. The current research investigated the students' perceptions of the use of metaverse systems in medical training within the medical community of the United Arab Emirates (UAE). Methods: A conceptual model comprising the adoption properties of personal innovativeness, perceived enjoyment, and Technology Acceptance Model concepts was utilised. The current research targeted students in UAE medical universities. Data was obtained by conducting online surveys that were implemented in the winter semester of 2021/2022 between 15th February and 15th May 2022. 500 questionnaires were issued to students following their voluntary participation and 435 questionnaire responses were obtained i.e. an 87% response rate. The research team tested the measurement model employing Structural Equation Modeling using Smart Partial Least Squares Version (3.2.7). Results: Statistically significant associations were confirmed to exist between Personal Innovativeness (PI) influenced by both the Perceived Ease of Use (PEOU), and Perceived Usefulness (PU) (β= 0.456) and (β= 0.563) at P<0.001. The statistically significant associations involving Perceived Enjoyment (EJ) and PEOU and PU (β= 0.554, P<0.05), (β= 0.571, P<0.05) were further confirmed. Additionally, PEOU had a relationship with PU (β= 0.863, P<0.001). Eventually, PEOU and PU significantly influenced the participants’ inclination to use the metaverse technology with (β= 0.745, P<0.001) and (β= 0.416, P<0.001), respectively