Higher education institutions (HEIs) have been permeated by the technological advancement that the Industrial Revolution 4.0 brings with it, and forces institutions to deal with a digital transformation in all dimensions. Applying the approaches of digital transformation to the HEI domain is an emerging field that has aroused interest during the recent past, as they allow us to describe the complex relationships between actors in a technologically supported education domain. The objective of this paper is to summarize the distinctive characteristics of the digital transformation (DT) implementation process that have taken place in HEIs. The Kitchenham protocol was conducted by authors to answer the research questions and selection criteria to retrieve the eligible papers. Nineteen papers (1980–2019) were identified in the literature as relevant and consequently analyzed in detail. The main findings show that it is indeed an emerging field, none of the found DT in HEI proposals have been developed in a holistic dimension. This situation calls for further research efforts on how HEIs can understand DT and face the current requirements that the fourth industrial revolution forced.

With the coronavirus (COVID-19) outbreak in China, the Chinese government decided to ban any type of face-to-face teaching, disrupting classes and resulting in over 270 million students being unable to return to their universities/schools. Therefore, the Ministry of Education (MoE) launched an initiative titled ‘Ensuring learning undisrupted when classes are disrupted’ by reforming the entire educational system and including an online education component. However, this quick reform in this unexpected critical situation of widespread COVID-19 cases harbours several challenges, such as the lack of time and teacher/student isolation. This paper discusses the possibility of using open educational resources (OER) and open educational practices (OEP) as an effective educational solution to overcome these challenges. Particularly, this study presents a generic OEP framework built on existing open-practice definitions. It then presents, based on this framework and based on the challenges reported by several Chinese education specialists during two national online seminars, a set of guidelines for the effective use of OER and OEP for both teaching and learning. Finally, this study presents some recommendations for the better adoption of OER and OEP in the future. The findings of this study can help researchers and educators apply OER and OEP for better learning experiences and outcomes during the COVID-19 outbreak.

Software-Defined Network (SDN) has become a promising network architecture in current days that provide network operators more control over the network infrastructure. The controller, also called as the operating system of the SDN, is responsible for running various network applications and maintaining several network services and functionalities. Despite all its capabilities, the introduction of various architectural entities of SDN poses many security threats and potential targets. Distributed Denial of Services (DDoS) is a rapidly growing attack that poses a tremendous threat to the Internet. As the control layer is vulnerable to DDoS attacks, the goal of this paper is to detect the attack traffic, by taking the centralized control aspect of SDN. Nowadays, in the field of SDN, various machine learning (ML) techniques are being deployed for detecting malicious traffic. Despite these works, choosing the relevant features and accurate classifiers for attack detection is an open question. For better detection accuracy, in this work, Support Vector Machine (SVM) is assisted by kernel principal component analysis (KPCA) with genetic algorithm (GA). In the proposed SVM model, KPCA is used for reducing the dimension of feature vectors, and GA is used for optimizing different SVM parameters. In order to reduce the noise caused by feature differences, an improved kernel function (N-RBF) is proposed. The experimental results show that compared to single-SVM, the proposed model achieves more accurate classification with better generalization. Moreover, the proposed model can be embedded within the controller to define security rules to prevent possible attacks by the attackers.

Blended Learning (BL) is one of the most used methods in education to promote active learning and enhance students’ learning outcomes. Although BL has existed for over a decade, there are still several challenges associated with it. For instance, the teachers’ and students’ individual differences, such as their behaviors and attitudes, might impact their adoption of BL. These challenges are further exacerbated by the COVID-19 pandemic, as schools and universities had to combine both online and offline courses to keep up with health regulations. This study conducts a systematic review of systematic reviews on BL, based on PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, to identify BL trends, gaps and future directions. The obtained findings highlight that BL was mostly investigated in higher education and targeted students in the first place. Additionally, most of the BL research is coming from developed countries, calling for cross-collaborations to facilitate BL adoption in developing countries in particular. Furthermore, a lack of ICT skills and infrastructure are the most encountered challenges by teachers, students and institutions. The findings of this study can create a roadmap to facilitate the adoption of BL. The findings of this study could facilitate the design and adoption of BL which is one of the possible solutions to face major health challenges, such as the COVID-19 pandemic.

Automatic scoring and feedback tools have become critical components of online learning proliferation. These tools range from multiple-choice questions to grading essays using machine learning (ML). Learning environments such as massive open online courses (MOOCs) would not be possible without them. The usage of this mechanism has brought many exciting areas of study, from the design of questions to the ML grading tools’ precision and accuracy. This paper analyzes the findings of 125 studies published in journals and proceedings between 2016 and 2020 on the usages of automatic scoring and feedback as a learning tool. This analysis gives an overview of the trends, challenges, and open questions in this research area. The results indicate that automatic scoring and feedback have many advantages. The most important benefits include enabling scaling the number of students without adding a proportional number of instructors, improving the student experience by reducing the time between submission grading and feedback, and removing bias in scoring. On the other hand, these technologies have some drawbacks. The main problem is creating a disincentive to develop innovative answers that do not match the expected one or have not been considered when preparing the problem. Another drawback is potentially training the student to answer the question instead of learning the concepts. With this, given the existence of a correct answer, such an answer could be leaked to the internet, making it easier for students to avoid solving the problem. Overall, each of these drawbacks presents an opportunity to look at ways to improve technologies to use these tools to provide a better learning experience to students.