The learning environment usually raises various types of anxiety based on the student’s abilities to use technology and their abilities to overcome the negative feelings of an individual being watched all the time and criticized. Hence, learners still feel anxious while using computers and socializing in an e-learning environment. Learners who are faced with computer and AI tools are confused and frustrated. The uneasiness stems from anxiety or uneasiness, which is highly evident in daily interaction with computers and artificial intelligence tools or devices in e-learning contexts. The uneasiness stems from anxiety or uneasiness, which is highly evident in the daily interaction with computers and artificial intelligence tools or devices in e-learning contexts. To investigate this phenomenon empirically, a questionnaire was distributed among a group of undergraduate students who are studying different majors. This study aims to investigate the role of social anxiety and computer anxiety in an e-learning environment at the university level. Universities in the Gulf area are among those implementing e-learning systems. In spite of this, recent studies have shown that most students at Gulf universities are still resistant to using online systems; hence, it is necessary to determine the type of anxiety that creates such resistance and their relationship with other external variables such as motivation, satisfaction and self-efficacy. Students would be more likely to use e-learning tools and participate more effectively in their courses using the accessible electronic channels when the degree of anxiety is low. In this study, we have proposed a theoretical framework to investigate the role of social anxiety and computer anxiety in e-learning environments in the Gulf region. We examined how different variables such as satisfaction, motivation and self-efficacy can negatively or positively affect these two types of anxiety.

In this study, we present the results of an assessment of an initiative that seeks to transcend the application of digital information in the higher education sector by recommending an integrative approach that quantifies both the flow of digital information and tutors’ quality impacts concerning technology acceptance model (TAM) constructs and the perceived experience of digital information in education (DIE). There is a mounting evidence that the educational institutions that prioritize the perceived experience and the quality of the tutors do not, generally, take into account the limited exposure to digital information and technologies. Data gathered from a survey of 485 college students were used to evaluate the model and hypotheses. The findings show that users’ perceptions of the value of DIE may depend on several extrinsic conditions that improve their experiences of learning and teaching. The user’s traits, such as technological preparedness, are vital in determining perceived ease of use. In some cultures, the superior quality of the tutor may further increase perceptions of the technology’s perceived usefulness. The intention to adopt technology may also be highly influenced by other variables such as information flow. Therefore, academic institutions must reevaluate the usefulness of digital information technology as a tool for improving educational sections. This research limited its focus to educational environments in which DIE has a significant impact on the teaching and learning setting. Future works may concentrate on health or monetary organizations.

The development of mobile learning apps might fail due to poor selection of the suitable technical requirements for mobile devices. This will affect the quality of mobile learning applications and, thus, will increase the development cost of mobile learning apps. Due to the above issues, we need to determine the most appropriate technical quality requirements for the development of mobile learning apps that meet user requirements. To achieve that, we propose a comprehensive framework to capture the most suitable technical quality requirements for mobile learning apps. A Delphi method was used to collect, evaluate, and analyze the data for this study. As a result of our Delphi study, we have identified nineteen technical quality requirements, divided into six quality dimensions, for the development of mobile learning applications. The proposed framework is expected to be a guideline for mobile apps designers and developers to successfully develop mobile learning apps.

IoT (Internet of Things) devices are increasingly being used in healthcare to collect and transmit patient data, which can improve patient outcomes and reduce costs. However, this also creates new challenges for data security and privacy. Thus, the major demand for secure and efficient data-sharing solutions has prompted significant attention due to the increasing volume of shared sensor data. Leveraging a data-fusion-based paradigm within the realm of IoT-protected healthcare systems enabled the collection and analysis of patient data from diverse sources, encompassing medical devices, electronic health records (EHRs), and wearables. This innovative approach holds the potential to yield immediate benefits in terms of enhancing patient care, including more precise diagnoses and treatment plans. It empowers healthcare professionals to devise personalized treatment regimens by amalgamating data from multiple origins. Moreover, it has the capacity to alleviate financial burdens, elevate healthcare outcomes, and augment patient satisfaction. Furthermore, this concept extends to fortifying patient records against unauthorized access and potential misuse. In this study, we propose a novel approach for secure transmission of healthcare data, amalgamating the improved context-aware data-fusion method with an emotional-intelligence-inspired enhanced dynamic Bayesian network (EDBN). The findings indicated that F1 score, accuracy, precision, recall, and ROC-AUC score using DCNN were 89.3%, 87.4%, 91.4%, 92.1%, and 0.56, respectively, which was second-highest to the proposed method. On the other hand, the F1 score, accuracy, precision, recall, and ROC-AUC scores of FRCNN and CNN were low in accuracy at 83.2% and 84.3%, respectively. Our experimental investigation demonstrated superior performance compared with existing methods, as evidenced by various performance metrics, including recall, precision, F measures, and accuracy.