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.

The growing number of security threats has prompted the use of a variety of security techniques. The most common security tools for identifying and tracking intruders across diverse network domains are intrusion detection systems. Machine Learning classifiers have begun to be used in the detection of threats, thus increasing the intrusion detection systems’ performance. In this paper, the investigation model for an intrusion detection systems model based on the Principal Component Analysis feature selection technique and a different Support Vector Machine kernels classifier is present. The impact of various kernel functions used in Support Vector Machines, namely linear, polynomial, Gaussian radial basis function, and Sigmoid, is investigated. The performance of the investigation model is measured in terms of detection accuracy, True Positive, True Negative, Precision, Sensitivity, and F-measure to choose an appropriate kernel function for the Support Vector Machine. The investigation model was examined and evaluated using the KDD Cup’99 and UNSW-NB15 datasets. The obtained results prove that the Gaussian radial basis function kernel is superior to the linear, polynomial, and sigmoid kernels in both used datasets. Obtained accuracy, Sensitivity, and, F-measure of the Gaussian radial basis function kernel for KDD CUP’99 were 99.11%, 98.97%, and 99.03%. for UNSW-NB15 datasets were 93.94%, 93.23%, and 94.44%.

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 growing use of the Internet of Things (IoT) around the world has encouraged researchers to investigate how and why the IoT is implemented in colleges and universities. Previous studies have focused on individual attitudes rather than the integration of attitudes from two different perspectives. Furthermore, other studies have investigated the use of the IoT in non-educational settings, ignoring the effect of the IoT related to the technology acceptance model (TAM) and technological pedagogical content knowledge (TPACK) model. The present work aims to address this research gap by determining the main factors that influence acceptance of the IoT, leading to increased awareness in collaborative learning, where technology forms the core tool in enhancing the use of the IoT. A questionnaire was used to collect data from teachers and students from colleges and universities in Oman and the United Arab Emirates (UAE). The data were analyzed through the structural equation modeling (SEM) method. The findings indicated that there are two levels of positive effects on the intention to use IoT. The first level is technology features, which are represented by technology optimism and technology innovation; these factors are crucial to using the IoT. The second level is learning motivation, which has a close relationship with teachers’ knowledge, and content pedagogy, which has a significant effect on the familiarity with IoT tools and applications. TAM constructs have a positive and direct impact on the intention to use IoT. The practical and managerial implications show that teachers, educators, and students can obtain benefits from these results to help IoT features to suit users’ needs.

There is increasing evidence that the lack of access to digital information and technologies is not widely considered in the educational sectors when focusing on the perceived experience, tutor quality and students’ satisfaction. In this paper, we report on an evaluation of a project aiming to bridge the use of digital information in the educational sector by proposing an integrated model that measures teachers’ quality, uncertainty avoidance effects and students’ satisfaction concerning TAM constructs and the perceived experience of digital information in education (DIE). The model and hypotheses were validated using data collected from a survey of 553 students at a college level. The results revealed that users may perceive the importance of DIE based on several external factors that enhance their learning and teaching experiences. The personal characteristics of the user including his/her readiness to use technology are crucial in correlation with the perceived ease of use. In addition, the high quality of the tutor in some cultures may enhance the perceived usefulness of the technology. Other factors such as flow of information, uncertain avoidance and satisfaction may strongly assess the continuous intention to use the technology.

Smart mobile learning (M-learning) applications have shown several new benefits for higher educational institutions during the COVID-19 pandemic, during which such applications were used to support distance learning. Therefore, this study aims to examine the most important drivers influencing the adoption of M-learning by using the technology acceptance model (TAM). The structural equation modelling (SEM) method was used to test the hypotheses in the proposed model. Data were collected via online questionnaires from 520 undergraduate and postgraduate students at four universities in Saudi Arabia. Partial least squares (PLS)–SEM was used to analyse the data. The findings indicated that M-learning acceptance is influenced by three main factors, namely, awareness, IT infrastructure (ITI), and top management support. This research contributes to the body of knowledge on M-learning acceptance practices. Likewise, it may help to facilitate and promote the acceptance of M-learning among students in Saudi universities.