Abstract

Background

     The outbreak of Coronavirus disease (COVID-19) in 2019 and the resulting quarantine may have increased the prevalence of mental health problems in adolescents. The aim of this study was to explore the association between the effects of home-based learning during the pandemic and the risks of depression, anxiety, and suicidality among junior and senior high school students.

Methods

     An online survey using Patient Health Questionnaire (PHQ-9) and Generalized Anxiety Disorder (GAD-7) was conducted between 12 to 30 April 2020, on a total of 39,751 students. Multivariable logistic regression analysis was used to analyze the risk factors of associated depression, anxiety and suicidality during the pandemic.

Results

     Prevalence of depression, anxiety symptoms and suicidality found was 16.3% (95% CI: 16.0, 16.7), 10.3% (95% CI: 10.0, 10.6) and 20.3% (95% CI: 19.9, 20.7), respectively. Participants with female gender and in junior high school, with poor overall sleep quality and poor academic performance and very worried about being infected during COVID-19 were highly associated with the risk of depression, anxiety symptoms and suicidal ideation (all P<0.001).

Conclusions

     Prevalence of self-reported mental health problems for adolescents using home-based distance learning was high. Implementing measures (e.g., wearing face masks) and spending only moderate time focusing on COVID-19-related information could be protective factors for mental health. These results provide suggestions for teachers and policy makers regarding adolescent improving sleep quality (sufficient sleep) and academic performance and reducing worry about pandemic during quarantine to prevent mental health problems.

Introduction

     Since the first outbreak of the Coronavirus disease (COVID-19) at the end of 2019, the COVID-19 pandemic was listed as a global public health emergency of international concern by the World Health Organization (WHO, 2020). In order to contain the spread of COVID-19, most recently, the Chinese Government issued a stay-at-home order, which meant that mass quarantine was mandatory during the initial outbreak of COVID-19 (Brooks et al., 2020). For example, only residents were allowed to enter residential communities, the wearing of face masks was made compulsorily, and non-essential community services were shut down (Leung et al., 2020). Meanwhile, 107 countries as estimated by the UN Educational, Scientific and Cultural Organization (UNESCO) on 18 March 2020, had implemented national school closures related to COVID-19, affecting 862 million children and adolescents (UNESCO, 2020). In particular, there were more than 220 million children and young people confined to their homes in China, according to the Ministry of Education of China (Ministry of Education of China, 2020). Normal daily life shifted for adolescents to home-based distance learning models, which has resulted in substantial disruption to the lives of students and their families and which may have longer-term consequences for adolescent health (Golberstein et al., 2020). There are obvious advantages to remote learning, e.g., accessibility and comfort during online learning, however, the limitations of inefficient learning, lack of eye contact and feedback from teachers, inattention and the difficulty in maintaining academic integrity are also of concern (Mukhtar et al., 2020). Although adolescence is a transitional and critical developmental period with brain and body maturation, students still faced further challenges in terms of losing structure and support, routine social connections, social distancing and loneliness, less physical activity, spending more time on screens, increased irregular sleep patterns, and less favorable diets, resulting collectively in weight gain and a loss of cardiorespiratory fitness (Brazendale et al., 2017; Loades et al., 2020). These challenges have been associated with increased mental health problems in adolescents, including an increase in anxiety, depression, loneliness and a loss of motivation and purpose (Caspi et al., 2006). Therefore, adolescent mental health needs to be prioritized during the COVID-19 pandemic.

Conclusions

     The current findings indicate that the prevalence of depression, anxiety symptoms and suicidal ideation is relatively high, particularly for respondents who are female and in junior high school, with poor academic performance and negative health status, spending more than three hours focusing on COVID-19 information, very worried about being infected and those who did not implement preventive measures. Additionally, high risk of mental health problems is indicated in students who had poor overall sleep quality, little or no physical activity and who were spending more than five hours on the internet. These study findings contribute to a greater understanding of mental health problems during pandemics in both junior and high school students, especially in terms of suicidal ideation. The research will help to identify risk factors in the mental health of adolescents and provides some suggestions to prevent and manage according to the above factors, such as spending less time online, taking enough physical exercise (30–60 minutes per day), and improving sleep quality (for example, taking sufficient sleep, keeping exercise). Further longitudinal studies of the long-term effects of the COVID-19 pandemic and quarantine on adolescents are needed to expand on this research.

 Keywords

Problem

Approach

Outcomes

Discussion

References

Article Info

Related Articles

The Children’s Mercy Kansas City Neurology Division has 113 division members (23 faculty, 16 advanced practice nurses (APRNs), five residents/fellows, 27 nurses, and 42 other learners) across five locations, which creates obstacles for engaging learners in educational conferences. Although previously some conferences were live-streamed, lectures were not recorded, and most were attended solely by trainees and a few select faculty. However, coronavirus disease 2019 (COVID-19) and social distancing prompted rapid development and implementation of a complete virtual curriculum.

Approach

We created a neurology education group using Microsoft Teams, including all clinical and nonclinical members in our division. Within one week of beginning social distancing, we offered an average of four live virtual lectures per week, increased from two weekly lectures before COVID-19. Leaders promoted participation of all neurology division members, and decreased clinical load (secondary to COVID-19 restrictions) facilitated time for lecture preparation and attendance. Lectures covered diverse topics, including quality improvement and leadership development, in addition to clinical neurology. We tracked attendance and recorded and stored all lectures and supplementary materials in Microsoft Teams. We emailed a REDCap survey1,2 at the end of the third and fourth weeks of virtual education, and again after three months, with personalized reminders to complete the survey every two days for one week. Questions addressed participation and contribution to education, as well as perceived benefits and drawbacks of the curriculum.

Abstract

Keywords

1. Introduction

2. eLearning in Romanian economics higher education during the COVID-9 pandemic

3. Brief Literature Review

4. Data and methods

5. Results and discussion

6. Conclusion

Funding

CRediT authorship contribution statement

Declaration of Competing Interest

Acknowledgments

Appendix A

References

Vitae

Abstract

In this paper, we aim to identify the determinants of online effective learning in the emergency situation created by COVID19 pandemic. Further, we test which of the learning methods (traditional, online, and hybrid) is preferred by Romanian students in economics in this unusual context. Using a sample of 1415 students from five major Romanian faculties of economics and applying ordinal and bivariate logit regressions models, we found that psychological distress and increased concerns about COVID-19 pandemic have a negative effect on learning effectiveness. Also, our results revealed that the students who face problems related to unsatisfactory internet access, insufficient time due to other familial issues, who have inadequate working space at home and also the male ones are more likely to be less effective in their online learning process. Finally, the university infrastructure for online activities decreases the likelihood that students will perceive online studies as less effective.

1. Introduction

Any online learning environment is considered to be a framework that “uses the Internet to deliver some form of instruction to learners separated by time, distance, or both” (Dempsey and Van Eck, 2002, p. 283). At the institutional level, eLearning in exceptional times, such as the case of the COVID-19 pandemic, should be seen as a backup educational system to continue the learning process. This kind of pandemic has spread almost all over the world, although it was not until December 2019 that the first official case in China was confirmed and hospitalized (Huang et al., 2020). Meanwhile, the situation has escalated so much due to the coronavirus outbreak that led the World Health Organization (WHO) to declare a Public Health Emergency of International Concern (Lai et al., 2020) (Figs. 1 and 2).

The impossibility to predict the stopping of this pandemic leaves room for numerous scenarios related to the lack of predictability. This status quo generates extremely much uncertainty and confusion that affect the economic, social and political institutions, also the interpersonal connection. All these changes have tremendous effects on humans and their relationships with other people and with institutions. When it comes to the educational issue, the unexpected health crisis made, on 15th of May 2020, more than 1.2 billion learners, about 70 percent of total enrolled ones, from 158 countries, to be highly affected by the global lockdown (UNESCO, 2020a). Hundreds of million students worldwide were and still are affected by “social distancing” or “self-isolation” measures promoted by their countries’ governments, because of the impossibility to attend physical classes which may influence their school performance (UNESCO, 2020a). Especially in the case when those students don’t have the proper infrastructure for online learning, it is obvious that their grades are critically marked. Also, homeschooling hurts the parents’ mobility, performance at work and, overall, on economic productivity of firms (UNESCO, 2020b). The same previous report emphasizes that a low level of familial wealth, reduces the level of democratization of access to technology, due to the difficulty to establish an internet connection, is a powerful barrier towards eLearning and a major source for exclusion, isolation and inequality.

Highlights

Abstract

Graphical abstract

Keywords

1. Introduction

2. Literature Review

3. Conceptual model and hypotheses formulation

4. Research methodology

5. Results

6. Discussion

7. Conclusion

References

Abstract

Understanding service branding from the customer perspective is helpful for various organizations to attain a competitive edge and build a strong customer base. This research work aims to evaluate the various dimensions and constructs that affect brand equity and user’s willingness to undertake courses from various e-Learning providers. Data for the quantitative study was obtained using a questionnaire based survey. Analysis of the 378 responses reveals that consumer experience is a strong predictor of both brand meaning and customer satisfaction. Although brand meaning positively impacts brand equity, brand awareness demonstrates a negative effect on brand equity. However, brand equity and customer satisfaction have a significant and positive impact on intention to undertake courses from online learning platforms. The study concludes with discussion on different implications, shortcomings, and directions for future research.

1. Introduction

In this modern era, technological innovations play an important role in shaping individuals’ careers (Fernandes et al., 2020; Vivian et al., 2014). The increasing popularity of online learning services (or e-learning services) has seen a rise in the number of online courses and even online degrees. The available massive open online courses (MOOC) have helped students or professionals in their career growth (von Schmieden et al., 2019). However, to choose the better course rather than a mediocre course (Ray et al., 2019a) from over 9400 available courses (Shah, 2018) is a confusing task and the students or professionals generally opt for courses from organizations which have a better brand image. Branding helps service providers compete on features other than pricing (Bailey and Ball, 2006). In this era of dynamic marketing and constant competition, for developing an emotional bonding with the users’, it is essential to develop a strong brand image (Buil et al., 2013; Gonzalez-Mansilla ´ et al., 2019). However, there is a paucity of research for assessing the factors that affect brand equity and user’s willingness to undertake courses from eLearning platforms. From the business aspect, the sustainability of various e-Learning providers is dependent on mainly three aspects: First the online learning platforms have to satisfy the needs and expectations of users (Ray et al., 2019a, Ray et al., 2020c). Hence, it is essential to find out the predictors of customer satisfaction which will affect their willingness to undertake courses from the online providers. Second the e-Learning providers should be able to provide courses useful for future career and can be trust-worthy (Ray et al., 2019a). It is necessary for organizations to find out how the company presents their brand, external brand communications and consumer’s experience with the e-Learning provider on consumer’s willingness to undertake online courses. Third, in this modern era with the rising popularity of various social-media platforms (Chatterjee, 2019), it will be interesting to assess the effect of online-reviews and online-ratings on brand awareness and brand meaning. We feel that understanding the impact of different predictors of brand equity of e-Learning providers can be significantly linked with the consumer’s intention to undertake online courses from that specific provider.

E-learning, as a direct result of the integration of technology and education, has emerged as a powerful medium of learning particularly using Internet technologies. The undeniable significance of e-learning in education has led to a massive growth in the number of e-learning courses and systems offering different types of services. Thus, evaluation of e-learning -systems is vital to ensure successful delivery, effective use, and positive impacts on learners. Based on an intensive review of the literature, a comprehensive model has been developed which provides a holistic picture and identifies different levels of success related to a broad range of success determinants. The model has been empirically validated by fitting the model to data collected from 563 students engaged with an e-learning system in one of the UK universities through a quantitative method of Partial Least Squares – Structural Equation Modelling (PLS-SEM). The determinants of e-learning perceived satisfaction are technical system quality, information quality, service quality, support system quality, learner quality, instructor quality, and perceived usefulness, which together explain 71.4% of the variance of perceived satisfaction. The drivers of perceived usefulness are technical system quality, information quality, support system quality, learner quality, and instructor quality, and these explain 54.2% of the variance of perceived usefulness. Four constructs were found to be the determinants of e-learning use, namely educational system quality, support system quality, learner quality, and perceived usefulness, and together they account for 34.1%. Finally, 64.7% of the variance of e-learning benefits was explained by perceived usefulness, perceived satisfaction, and use.

Introduction

The development of Information Technology (IT) has motivated improvements in various fields such as finance, business, health, and education. As a result, education has grown rapidly and stimulated the adoption of e-learning, which is a direct result of the integration of education and technology and is deemed to be a powerful medium for learning (Al-Fraihat, Joy, & Sinclair, 2017). E-learning has become mainstream in the education sector and has been massively adopted in higher education. According to Dahlstrom, Brooks, and Bichsel (2014), p. 99% of institutions have Learning Management Systems (LMSs) in place, and 85% of them have been utilized, and in the UK, 95% of higher education institutes have adopted LMSs to support their educational services (McGill & Klobas, 2009). Accordingly, the quality of e-learning systems has received a considerable amount of research attention and a large number of researchers have attempted to identify e-learning success factors to maximize the effectiveness of these systems (e.g., Ali & Ahmad, 2011; Fathema, Shannon, & Ross, 2015; Islam, 2013; B. C. Lee, Yoon, & Lee, 2009; J. K. Lee & Lee, 2008; M. C. Lee, 2010; Mohammadi, 2015; Mtebe & Raphael, 2018; Park, 2009; Wahab, 2008; Wang, 2003)). Broadly, the majority of these studies have examined individual parts of key determinants of e-learning systems success ignoring the synergistic effects of the success variables interacting together (Eom & Ashill, 2018). Another direction of research has dealt with the direct relationships between e-learning quality factors and usage or satisfaction (e.g., Selim, 2003; Ozkan & Koseler, 2009).

1- Introduction

2- Background

3- Methods

4- Results

5- Discussion

6- Conclusions

References

Abstract

In low- and middle-income countries (LMICs), e-learning for medical education may alleviate the burden of severe health worker shortages and deliver affordable access to high quality medical education. However, diverse challenges in infrastructure and adoption are encountered when implementing e-learning within medical education in particular. Understanding what constitutes successful e-learning is an important first step for determining its effectiveness. The objective of this study was to systematically review e-learning interventions for medical education in LMICs, focusing on their evaluation and assessment methods. Nine databases were searched for publications from January 2007 to June 2017. We included 52 studies with a total of 12,294 participants. Most e-learning interventions were pilot studies (73%), which mainly employed summative assessments of study participants (83%) and evaluated the e-learning intervention with questionnaires (45%). Study designs, evaluation and assessment methods showed considerable variation, as did the study quality, evaluation periods, outcome and effectiveness measures. Included studies mainly utilized subjective measures and custom-built evaluation frameworks, which resulted in both low comparability and poor validity. The majority of studies self-concluded that they had had an effective e-learning intervention, thus indicating potential benefits of e-learning for LMICs. However, MERSQI and NOS ratings revealed the low quality of the studies’ evidence for comparability, evaluation instrument validity, study outcomes and participant blinding. Many e-learning interventions were small-scale and conducted as short-termed pilots. More rigorous evaluation methods for e-learning implementations in LMICs are needed to understand the strengths and shortcomings of e-learning for medical education in low-resource contexts. Valid and reliable evaluations are the foundation to guide and improve e-learning interventions, increase their sustainability, alleviate shortages in health care workers and improve the quality of medical care in LMICs.

Introduction

To achieve the 2030 Sustainable Development Goals (SDGs) for health–specifically, goal 3 (Good Health and Well-Being) and goal 4 (Quality Education)–low- and middle-income countries (LMICs) need effective and affordable education strategies to address critical health worker shortages (Al-Shorbaji, Majeed, & Wheeler, 2015, p. 156; Bollinger et al., 2013). This is particularly crucial considering the significant challenges most LMICs face with limited health care budgets (World Health Organization, 2014), insufficient qualified health workers (Mills, 2014), limited access to equipment and infrastructure, and a lack of continuing professional development and continuing medical education programs (Mack, Golnik, Murray, & Filipe, 2017). Training facilities and resources for health workers in LMICs are insufficient, and a substantial number of the few trained professionals are subject to “brain drain” when regularly recruited to high-income countries (Aluttis, Bishaw, & Frank, 2014). E-learning interventions for medical education could be of great benefit for targeting good health and well-being and quality education, especially in low-resource settings (Al-Shorbaji, Atun, Car, Majeed, & Wheeler, 2015; Dawson, Walker, Campbell, & Egede, 2016; Liu et al., 2016; World Health Organization, 2016a,b).

Introduction

Literature review

Theoretical framework

Methodology

Data analysis

Discussion and recommendations

Contribution of the study

Limitations of the study and future direction

References

Abstract

Purpose – The purpose of this study was to investigate the factors that might influence the intention and use behaviour of e-learning systems by students in state universities in Sri Lanka . Design/methodology/approach – The theoretical model for this study was primarily drawn from unified theory of acceptance and Use of Technology 2 (UTAUT2). Exogenous variables included performance expectancy, effort expectancy, social influence, work life quality, hedonic motivation, internet experience and facilitating condition, and their influence on behavioural intention and use behaviour were studied. Instrument was developed using validated items from past literature. Data for this quantitative study were collected from undergraduate and postgraduate students from 15 Sri Lankan state universities by selfadministering and Web-form during second quarter of 2018. Structural equation modelling was used to see the insights from the valid data using IBM’s SPSS 25 and AMOS 22.
Findings – Results of the confirmatory factor analysis and subsequent evaluation of the structural model confirmed the proposed hypotheses, and it was found that constructs of UTAUT2 have a significant impact on and play an important role in behavioural intention to use and use behaviour of e-learning system by state university students in Sri Lanka.
Originality/value – The adoption of an e-learning system in Sri Lankan state universities is fairly low. Hence, investigation of what determinants might be contributing for adoption is important to enhance the learning experience of students and help them improve their knowledge. This paper contributes by delineating the factors that influence the acceptance and use of e-learning systems by students of state universities in Sri Lanka.

Introduction

As society has evolved to be technology-driven, vigorous development of information and communications technology (ICT) and its application in all facets of human life have seen immense changes in the landscape of education globally (Vidakis and Charitakis, 2018). This has led educational institutions to implement new technologies in teaching and learning processes. Electronic learning (e-learning) is a powerful tool that transforms the traditional method of learning because it strengthens the capacity of teaching and learning (Alfraih and Alanezi, 2016) through provision of an efficient and effective channel for educational institutions for teaching and learning among students, as well as efficient knowledge delivery. E-learning is defined as the delivery of learning materials and methods online by using information technologies to learn, teach or acquire knowledge anytime from anywhere (Turban et al., 2015, p. 218). Learning processes can be online, offline or both (Al-Busaidi, 2013). In terms of time and location, e-learning provides learners convenience and flexibility. Learners acquire and make use of knowledge disseminated digitally (Tetteh, 2016). E-learning does not exclusively refer to Web-based learning; it also includes mobilelearning, where materials are delivered to wireless devices such as smartphones, tablets or other mobile devices; hence, the term e-learning covers a wide spectrum including computerbased training, computer-based instructions, online education and other terms, and it is useful to facilitate learning at educational institutions and to provide a conducive atmosphere for efficient and effective corporate training (Turban et al., 2015). According to Driscoll (2010), e-learning was said to have strategic and technical benefits. E-learning helps people to keep themselves updated through lifelong learning in non-traditional ways of learning (Turban et al., 2015).

In e-learning, most content-based (CB) recommender systems provide recommendations depending on matching rules between learners and learning objects (LOs). Such learner-oriented approaches are limited when it comes to detecting learners’ changes, furthermore, the recommendations show low adaptability and diversity. In this study, in order to improve the adaptability and diversity of recommendations, we incorporate an LO-oriented recommendation mechanism to learner-oriented recommender systems, and propose an LO self-organization based recommendation approach (Self). LO self-organization means LO interacts with each other in a spontaneous and autonomous way. Such self-organization behavior is conducive to generating a stable LO structure through information propagation. The proposed approach works as follows: firstly, LOs are simulated as intelligent entities using the self-organization theory. LOs can receive information, transmit information, as well as move. Secondly, an environment perception module is designed. This module can capture and perceive learner’s preference drifts by analyzing LOs’ self-organization behaviors. Finally, according to learners’ explicit requirements and implicit preference drifts, recommendations are generated through LOs’ self-organization behaviors. Based on application to real-life learning processes, the ample experimental results demonstrate the high adaptability, diversity, and personalization of the recommendations.

Introduction

E-learning recommender systems aim to recommend a sequence of items to learners, that is, to suggest the most efficient or effective paths through a plethora of learning resources to achieve a certain competence [1, 2]. However, over specification and excessive searching in e-learning recommender systems result 5 in information overload. Learners do not have enough time to deal with these massive recommendations. In addition, the adaptability and diversity of recommendations are desirable in e-learning recommender systems, because learners’ preferences and abilities keep changing, and also because the functionality of some learning resources for active learners keeps changing. The diverse and adaptive recommendations should be presented to motivate learning potential of learners and ensure a long-term learning experience. 10 In this study, we aim to improve the adaptability and diversity of content-based (CB) recommendations. Generally, learning resources are filtered in three ways: CB, collaborative filtering (CF), and hybrid filtering (HF) [3, 4]. CF recommender systems compute the similarity computation between learners based on their rating and then make predictions[5, 6]. CF methods are known to suffer from the rating sparsity problem which occurs when the users or items have insufficient rating records [7]. In e-learning, 15 the rating information is extremely sparse. The main reasons for this are a lack of motivation for learners to rate, a lack of scoring mechanisms, and the scheduled but limited learning time of learners [1]. The high data sparsity makes it difficult to apply CF techniques. CB recommender systems recommend relevant learning contents which are highly matched to learners’ learning goals and preferences. In addition, CB recommender systems do not usually suffer from the first-rater problem, therefore, CB recommendation 20 approaches are the only ones capable of recommending items not previously rated by any learner [8]. The learner’s ability, goal, mood, and cognitive style are often used as criteria in CB recommendation systems [8, 9].

Currently, the process of teaching-and-learning (TL) is strongly supported by educational technologies, which are used in educational environments both in traditional face-to-face classrooms and in online learning platforms (i.e. e-learning). In the last years, technological advancements have given assistance in providing relevant information for all educational actors (students, teachers, coordinators, and administrators) acting in different educational environments, and thereby promoting the quality and innovations in the educational context as highlighted by Wang et al. (2012); Qiu and Lee (2013); Liu and Peng (2013); Yu and Jo (2014). In order to consolidate and achieve the better quality levels in the e-learning environment, it is necessary to continuously evaluate the teaching and learning process by observing its different aspects, such as the level of interaction of the actors involved, the effectiveness of pedagogical actions from the student’s own response and initiative, the use of multimedia resources, the administrative management responsible for institutional policies, and other aspects that influence the learning and development of better cognitive skills and collaborative work, as emphasized by Sankar and Clayton (2010). Due to its relevance, the problem of evaluation of e-learning has attracted the attention of several authors, among them we can cite Liu (2005); Chen (2005); Mahlow et al. (2007); Calinger and Howard (2008). For example, in Liu (2005) the author emphasizes the need to evaluate the learning process in e-learning through factors such as achievement, motivation, and level of student anxiety during the learning process. In Chen (2005), the authors evaluate the student’s learning through their experience, perception, and attitude during learning. The web-based instruction requires a broad and continuous process of evaluation of teaching and learning to ensure its effectiveness, as highlighted by Yubing and Jianping (2010). Nowadays, it is possible to generate a great volume and wide variety of data from educational environments such as those related to academic records, monitoring and assessment, and the records of interactions by e-mails between students and educators/teachers, discussion forums and chats (Romero and Ventura, 2010; Zaiane and Luo, 2001; Adeva et al., 2006; Wang and Meinel, 2007; Sun and Zhao, 2009; Liu et al., 2010; Kechaou et al., 2011). The generated data by educational environments, specifically the data generated by the e-learning platforms, are important sources of information for decision support and improvement of the TL. The improvements can be reached from the students’ data analysis through of their behavior, satisfaction, and performance. The objectives are always addressed to the expansion and advancement of the educational horizons, in order to contribute to the evolution of the educational process.

We start with the question: is Technology Management Education (TME) a requirement for the success of Virtual Learning Environment (VLE)? Honestly, this work does not really answer this question, but regarding that success in the context of a VLE is effective and efficient learning, then our hypothesis is: Education of Technology Management is necessary for the success of a Virtual Learning Environment. Despite the fact that it might seem logically straightforward that using TME in managing and utilizing VLE should enhance instructors and learners performance, as far as the authors knows, this question has not been addressed in scientific literature. Hence, a framework to approach the matter may work as a baseline for future studies, and this is the purpose of this work. For instance, Pusnik et al. in [1] in regards to VLE claim that the success of new technologies and services must have a positive impact and be adopted by end users, that is, teachers and learners. A more recent study by Dominguez and Aguilar in [2] review the processes of Information Technology (IT) required for educational organizations, because e-Learning is “deeply affected by the changing environment and technological support”, hence they remarked that a satisfactory delivery of service to the user is obtained with intensive monitoring and management of applications and infrastructure. What we may observe from the former two references is an indirect and not clear criterion for VLE’s success, and an implicit argument for the requirement of the Management of Technology (MOT) for the VLE, respectively. Clearly, the existence of a correlation between MOT and the success of VLE is established, because without a satisfactory service delivery, the adoption of the VLE by the users is compromised. Another work highlights the requirement of management regarding VLE in institutions of higher education, by Segooa and Kalema in South Africa [3]. Their emphazis is in developing countries, which is close to our heart because we share similar context, barriers and challenges. Basically, they recommend [3]: 1) To provide enough computers to their students. 2) To ensure network coverage and maintenance that hinder effective use of VLE. 3) To embed VLE programs in the institutional policies and strategy in order to stipulate that both students and lecturers use the tools. 4) To provide continuous training on how VLE can be efficient and beneficial to students and lecturers. 5) To extend the facilities or library hours to allow the student that are underprivileged to access the facilities even after hours like evenings. 6) The encouragement to the newly introduced culture by supporting the program with incentive.

With the rapid advancements in Information and Communications Technologies (ICT), the popularity of learning on the Internet and construction of e-learning environments is growing rapidly. Today, there are lots of paradigms for getting knowledge or learn through Internet. One of the most promising paradigms for education is e-learning. Elearning refers to the use of networked information and communications technologies and offers a wide range of new opportunities for the development of education and brought profound impact to teaching and learning methods. Today, e-learning has become one of the most popular teaching and learning methods by stretching the spatial and temporal barriers (Dong and Huang 2011). However, with the increasing access to ICT and the huge growth of the number of users, data and educational resources generated, e-learning systems have become more and more expansive in terms of hardware and software resources (computing, storage …) and many educational institutions cannot afford such investments (Paul and Santhi 2014). Indeed, with the daily rising trend on requirement’s dynamic changes in service, e-learning systems deployed in educational institution are facing many issues and challenges of optimizing large-scale resource management and provisioning (Paul and Santhi 2014). On the one hand, a poor or insufficient infrastructure can cause more damage than good to users and the learning experience (Bora and Ahmed 2013). Thus, educational institutions are obliged to evolve their e-learning infrastructure. Nevertheless, the infrastructure provisions that are necessary to provide a competitor service for a large amount of users clearly exceed the capabilities of a simple web server and. This leads to high the costs of the infrastructure establishment and institutions are unable to afford the costs needed. On the other hand, the demand of the teaching and learning resources (hardware and software resources) usually vary in a dynamic and very quick way and presents high peaks of activity. To attend flexible and dynamic requests of resources, it will be necessary to prepare a quite superior e-learning system than that required for the regular working of educational institutions (Fernández et al. 2012). To overcome these issues, cloud computing, that has been recently emerging as a key paradigm of the present century, is a new model for hosting resources and provisioning of services to e-learning systems. It provides a convenient, on-demand access to a centralized shared pool of computing resources that can be deployed by a minimal management overhead and with a great efficiency (El-Sofany et al. 2013). Cloud computing rises as a natural platform to provide support to elearning systems. Today, there is a growing trend regarding the research and exploitation of cloud computing to support e-learning process and many research works has been carried out in the domain of cloud-based e-learning (Huang and Liu 2013; Li et al. 2011; El Mhouti et al. 2016).