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Abstract-This paper aims to identify the most important and significant factors in two different areas of learning: combined and traditional learning. Several critical issues have not yet been resolved to achieve the full potential of the learning outcomes in the two domains. The objective of this paper is to review the critical factors that have a great influence on academic performance. The document focuses specifically on a set of factors such as the use of technology, the interaction processes, the characteristics of the students and the class. These identified factors were classified and discussed. The document also determines the technical and pedagogical limitations of the two declared domains. The technical and pedagogical challenges were proposed and future works were recommended.

The Internet of Things (IoT) has become a popular computing technology paradigm. It is increasingly being utilized to facilitate human life processes through a variety of applications, including smart healthcare, smart grids, smart finance, and smart cities. Scalability, interoperability, security, and privacy, as well as trustworthiness, are all issues that IoT applications face. Blockchain solutions have recently been created to help overcome these difficulties. The purpose of this paper is to provide a survey and tutorial on the use of blockchain in IoT systems. The importance of blockchain technology in terms of features and benefits for constituents of IoT applications is discussed. We propose a blockchain taxonomy for IoT applications based on the most significant factors. In addition, we examine the most widely used blockchain platforms for IoT applications. Furthermore, we discuss how blockchain technology can be used to broaden the spectrum of IoT applications. Besides, we discuss the recent advances and solutions offered for IoT environments. Finally, we discuss the challenges and future research directions of the use of blockchain for the IoT.

The use of unmanned aerial vehicles or drones are a valuable technique in coping with issues related to life in the general public’s daily routines. Given the growing number of drones in low-altitude airspace, linking drones to form the Internet of drones (IoD) is a highly desirable trend to improve the safety as well as the quality of flight. However, there remain security, privacy, and communication issues related to IoD. In this paper, we discuss the key requirements of security, privacy, and communication and we present a taxonomy of IoD based on the most relevant considerations. Furthermore, we present the most commonly used commercial case studies and address the latest advancements and solutions proposed for the IoD environments. Lastly, we discuss the challenges and future research directions of IoD.

The evolution of recent malicious software with the rising use of digital services has increased the probability of corrupting data, stealing information, or other cybercrimes by malware attacks. Therefore, malicious software must be detected before it impacts a large number of computers. Recently, many malware detection solutions have been proposed by researchers. However, many challenges limit these solutions to effectively detecting several types of malware, especially zero-day attacks due to obfuscation and evasion techniques, as well as the diversity of malicious behavior caused by the rapid rate of new malware and malware variants being produced every day. Several review papers have explored the issues and challenges of malware detection from various viewpoints. However, there is a lack of a deep review article that associates each analysis and detection approach with the data type. Such an association is imperative for the research community as it helps to determine the suitable mitigation approach. In addition, the current survey articles stopped at a generic detection approach taxonomy. Moreover, some review papers presented the feature extraction methods as static, dynamic, and hybrid based on the utilized analysis approach and neglected the feature representation methods taxonomy, which is considered essential in developing the malware detection model. This survey bridges the gap by providing a comprehensive state-of-the-art review of malware detection model research. This survey introduces a feature representation taxonomy in addition to the deeper taxonomy of malware analysis and detection approaches and links each approach with the most commonly used data types. The feature extraction method is introduced according to the techniques used instead of the analysis approach. The survey ends with a discussion of the challenges and future research directions.