Content area
Purpose
This paper aims to provide a literature review on the cloud-based platforms for the education sectors. The several aspects of cloud computing adoption in education, remote/distance learning and the application of cloud-based design and manufacturing (CBDM) have been studied and theorised.
Design/methodology/approach
A four-step methodology was adopted to analyse and categorise the papers obtained through various search engines. Out of 429 research articles, 72 papers were shortlisted for the detailed analysis.
Findings
Many factors that influence cloud computing technology adoption in the education sector have been identified in this paper. The research findings on several research items have been tabulated and discussed. Based on the theoretical research done on cloud computing for education, cloud computing for remote/distance learning and CBDM, cloud computing could enhance the educational systems in mainly developing countries and improve the scope for remote/distance learning.
Research limitations/implications
This study is limited to papers published only in the past decade from 2011 to 2020. Besides, this review was unable to include journal articles published in different languages. Nevertheless, for the effective teaching and learning process, this paper could help understand the importance and improve the process of adopting cloud computing concepts in educational universities and platforms.
Originality/value
This study is a novel one as a research review constituting cloud computing applications in education and extended for remote/distance learning and CBDM, which have not been studied in the existing knowledge base.
1. Introduction
Technological advances are significant in shaping legal education developments by providing and exercising various means of learning while the delivery and communication are carried out at lower costs (Zareie and Navimipour, 2016). Especially when considering developing countries, there is always a need for capacity building and scope for distance learning. With the developments in technology and the establishment of Industry 4.0, cloud computing has been in talks for modernising and improving all sectors’ establishment (Arowoiya et al., 2020; Gamil et al., 2020; Hellas et al., 2020; Riaz et al., 2017; Talatappeh and Lakzi, 2019). The educational sector benefits from such an establishment, especially in developing countries where education is affected by socio-economic and geographical constraints. In this context, Information Technology (IT)-based platforms help improve education quality.
As a successful platform, cloud computing avidly assists distance learning and provides an imperative technology for advancement in e-learning (Al-Samarraie and Saeed, 2018). Mainly three types of cloud systems exist, i.e. “Private”, “Public” and “Hybrid” (Chiregi and Navimipour, 2017). The first is exclusively used by a sole organisation that may comprise multiple consumers. The public cloud infrastructure can be openly used by the public and is usually maintained only by the provider. However, the hybrid cloud combines two or more different cloud infrastructures (private, community or public) that are distinctly exclusive individuals. However, they are tied together by either proprietary-based technology or standardisation, allowing data along with an application to be ported across several platforms (Andreadis et al., 2015). The available cloud services are categorised as “Platform as a Service” (PaaS), “Infrastructure as a Service” (IaaS) and “Software as a Service” (SaaS) (Bhaskarwar et al., 2021; Creeger, 2009; Narkhede et al., 2020; Narwane et al., 2020; Naseri and Navimipour, 2019; Souri et al., 2018). SaaS is a cloud service where the consumer can usually access software applications on the internet, which can be used for a wide range of tasks and are extensively hosted on the cloud that can be done for both organisations and individuals. PaaS usually delivers a platform alongside an environment to allow its developers to provide services and build applications produced using programming languages and tools (Zanbouri and Navimipour, 2020). This service helps users to use tools that the provider usually supplies to create software applications. Whereas, IaaS provides access to computing resources in a virtualised environment. The computing resources provided here are usually the virtualised hardware, i.e. computing infrastructure (Saini and Kaur, 2017). On the other hand, new web-based technologies and computational frameworks like social networking, Voice-over-internet protocol, online/on-demand multi-point video telecollaboration, digital communication, instant messaging and virtual worlds allow people from all around the world to work together.
Internet-fueled ideas of distributed design, distributed manufacturing and mass collaboration help realise next-generation design and manufacture (Lyu et al., 2017; Schaefer et al., 2012). Many conventional enterprises specialising in the design and manufacturing of products follow vertically integrated business models. On the other hand, modern product development organisations are greatly integrated with the complex systems of cross-domain technologies and they are cross layered (Schaefer et al., 2012). To develop this product realisation, isolated units of engineering design processes need to be integrated. This can be enabled partly or entirely with the help of the cloud.
To understand cloud computing adoption for education, various literature survey studies have been carried out. Some of the significant studies were collaborative learning in Malaysia (Al-Samarraie and Saeed, 2018), cloud computing in education (CCE) pedagogy in Italy (Baldassarre et al.,2018), drivers and barriers of CCE in Spain (González-Martínez et al., 2015) and higher education colleges of Malaysia (Qasem et al., 2019). However, these studies fail to discuss CCE for remote/distance learning and cloud-based design and manufacturing (CBDM). This develops a research gap and a halt to the adoption of technology in our daily lives. Also, different databases exploit different aspects of cloud development. As far as we know, no research has been conducted to compare and process the development across these various journals. Cloud-based learning is packed with the potential to take education to the next level. Still, most studies focus on the technological aspect of cloud computing rather than presenting how it would be adopted into the different systems in the current scenario. It is a significant differentiator for developed, developing and underdeveloped countries, for each would react to such advances differently. This potential reaction has not been studied considering geographical, political, socio-economical and several other factors. Furthermore, cloud computing education in applications, such as computer aided design (CAD) and CBDM has not been fully explored.
Thus, this study focusses on providing analysis and a survey of the literature on cloud computing for education. In totality, this paper draws one’s attention to the following research questions.
What previous research has been conducted for CCE, and what is the importance of cloud computing in the education system?
How does cloud computing impact remote and distance learning?
What is CBDM, and what is the need for CBDM in design education?
How are the prominent-related studies classified based on year, country and journals?
The paper is further propagated as follows: Section 2 discusses the methodology adopted in this study. Whereas the intensive literature survey, which addresses the RQs, is covered in Section 3. Section 4 of the paper gives the findings and Section 5 covers the discussion. Limitations, future scope and implications of the research are discussed in Sections 6 and 7. Finally, Section 8 covers the conclusion. It may be noted that various terminologies used in this review are indicated in Table 1.
2. Methodology
A systematic review of the literature was planned to address the research questions. Systematic literature survey helps as evidence-based research (Tranfield et al., 2003). Scopus and Web of Science are two popularly used databases. However, Scopus is most prominently used (Falagas et al., 2008), and thus Scopus database was selected for the search. Keyword finalisation is vital for any search. Based on the study’s objectives, the list of keywords finalised was cloud computing and remote learning, CCE, cloud computing and distance learning, and CBDM. The period considered for the search was 2011 to August 2020. The detailed methodology adopted for the literature review is as shown in Figure 1.
The number of papers for each keyword combination was shown in the bracket. Keyword combination “Cloud Computing and Education” had the highest count of 138 articles, followed by “Cloud Computing and Distance Learning” with 127 articles, “Cloud-based Design and Manufacturing” with 105 papers and “Cloud Computing and Remote Learning” with 59 papers. A further category of remote learning and distance learning were combined (59 + 127 = 186 papers). Title, Abstract and Keyword (TAK) principle is most commonly used for finalising the articles for detailed study (Vieira and Kumar, 2004; Chechurin, 2016). Thus, through TAK, 72 articles were shortlisted from 429 research articles. Further, the shortlisted articles were then categorised into three groups and studied extensively. A broad classification of the research articles is expressed in Figure 2.
The first category, CCE, was considered to understand the importance of cloud computing in the education system. Further, to understand the impact of cloud computing impact on remote and distance learning, the second category was considered. Finally, the third category elaborates the need for CBDM in design education.
3. Literature survey
3.1 Cloud computing in the education system
Cloud computing is a vital component of Industry 4.0. It is an evolutionary concept and a virtualised technology involving a network of informational resources, where computing can effectively reach 10 trillion times per second (Nie et al., 2018; Oke et al., 2021; Rajabion et al., 2019). Furthermore, technology is a continuously changing platform using tools and techniques to upgrade the standard of living. Thus, cloud computing would prove an effective way for the learning-teaching paradigm to keep up with the ever-changing technology (Al-Harthi et al., 2018).
The cloud works over the internet and helps access data from different geographical locations, thus improving connectivity and data sharing (Milani and Navimipour, 2016; Raut et al., 2019). This concept is crucial to the educational system. The students and teachers can connect through the cloud and internet even in difficult times when travelling to a common institution is not necessarily possible. Thus, cloud computing has been an essential topic of research and discussion for educational researchers to overcome implicit knowledge being transformed in an online type of setting (Uden et al., 2014).
The literature survey on the applications of CCE is categorised into theoretical studies and studies based on implications. The overview of the academic research articles on CCE is shown in Table 2, demonstrating that several authors have carried out several theorised research types in CCE. Further, various decision factors have been identified, as shown in Appendix 1. Whereas Table 3 shows the research articles on CCE based on implementation. Tables 2 and 3 mainly intend to highlight the type of study, its objective, tools and techniques used for analysis.
3.2 Cloud computing and remote and distance learning
In “Remote Learning”, there is an exchange of knowledge between the lecturers/teaching faculty or the information sources and the student who is not physically present for a traditional classroom setting. The transfer of informational data occurs via various technologies like video conferences, online assessments, whiteboards (discussion boards) and online laboratories. There can exist a real-time interaction between students and the lecturer or students can have more self-paced learning activities that can take place without any professional guidance.
Distance learning is a study method where the students take part in the learning process using video lectures and assignments. Distance learning can be adequately supported by cloud-based learning. It improves the situation in developing countries as there is a deficit of sustainable facilities, and cloud-based learning does not require much additional infrastructure. With cloud computing, a larger audience can be targeted through a more singular medium, and there is a broad scope for learning various courses. These courses can be extended to different forms of learning as well as teaching. An example of Distance Learning is MOOC (Massive Open Online Courses) (Neuhaus et al., 2014). Table 4 lists the research articles on cloud computing applications in the remote and distance learning domain.
3.3 Cloud-based design and manufacturing and design education
Many conventional enterprises specialising in the design and manufacturing of products follow vertically integrated business models. On the other hand, organisations that focus on product development are highly complex systems that skillfully integrate cross-layered and cross-domain technology (Schaefer et al., 2012). To achieve this product realisation, integration of isolated engineering design process units is mandatory. It can be enabled partly or entirely with the help of the cloud. CBDM concept is realised by integrating cloud resources, physical and virtual and human resources in product development with the intrinsic aid of systems for knowledge management. The virtual resources include simulation software (CAD/Computer Aided Manufacturing), collaboration software, design software alongside Office. The physical resources include the manufacturing processes, suppliers, packaging, etc. The human resources create their human-centric network, including design teams, students and social networks (Wu et al., 2015a).
CBDM can also be referred to as a product development model that helps network with a service-oriented platform that allows the customer to select, configure and use personalised product realisation services and resources (Wu et al., 2015a). These services range from Computer Aided Engineering software to a programmable manufacturable system. It requires the four essential cloud service models to be well integrated, i.e. IaaS, PaaS and SaaS. CBDM enables collaborative open innovation and rapid development of the product at the least cost with the help of crowdsourcing platforms and social networking and shareable designs, and manufacturable components and resources (Schaefer et al., 2012).
CBDM comprises two parts: cloud-based design and cloud-based manufacturing. The first is a model based on a design that integrates engineering design services with cloud computing in a distributable and collaborative environment. However, the second is a networked manufacturing model as a collected distributed manufacturing resource and programmable production line that extends benefits to increased efficiency, reduced cost and product life cycle time satisfying varying customer demands (Wu et al., 2015a).
The vital objective of CBDM is the realisation of the efficiency of product development processes. The CBDM functionality is essential to consumers from both the industrial and the educational sectors. Academic and industrial needs are tightly bound. Manufacturing industries use this technology to fabricate goods and services. Any industrial sector relies on educational institutions for these reasons:
Imparting students with knowledge of the foundation and basic principles of CBDM systems and the objective of realising economic goals.
Conducting further research and development in the CBDM processes (Schaefer et al., 2012). Table 5 enlists the research articles surveyed for CBDM.
3.4 Classifications based on year, country and journal
This section shows the classifications of the research items based on the year of publication, country and journal in the three categories mentioned in Section 2 from 2011 to 2020. The year-wise distribution of the papers is stated in Table 6 and Figure 3 gives a bar graph indicating the same.
It is very clear from Table 6 and Figure 3 that more papers were published in 2015 onwards than in the past half of the decade, with its peak being in 2015. However, it can also be seen that not much research work was carried out regarding the selected aspects of cloud computing in the years 2011 and 2012.
The number of papers published in each journal is presented in Table 7. Also, it shows the abbreviation of 54 journals considered for the literature review and their annual frequency distribution is shown in Table 8.
It is prominent from Table 8 that out of 72 articles, the maximum count of papers was six and published in the journal, namely, “Smart Learning Environments”. Apart from this, a significant number of documents are also published in the journals entitled “Future Generation Computer Systems” (4 papers), “IEEE Access” (3 papers), “International Journal of Computer Integrated Manufacturing” (3 papers) and “Journal of Manufacturing Science and Engineering” (3 articles). Also, it is evident that in 2015, three articles were published in J MANUF SCI E-T ASME and SMART LEARN ENV published two papers in each in the years 2016, 2018 and 2019. Table 9 states the number of articles published in each country, and Table 10 shows the annual frequency distribution of papers published in each country.
4. Key findings obtained from the literature survey
4.1 Cloud computing in education
Based on the theoretical study on CCE, several implicational studies have also been successfully carried out. Cloud computing successfully boosts availability and accessibility in several regions at reduced rates and can help several communities in revolutionising the education system, especially higher education (Boja et al., 2013; Banait et al., 2015; Daim et al., 2016; Kim et al., 2016; Liao et al., 2016; Saleh et al., 2018; Wang, 2019). In this area, several models have been established and tested, which include clubbing e-learning and social technology (Al-Shammari, 2014), mobile network integration (Zurita et al., 2014) and the Hadoop framework (Zhang et al., 2015). The cloud computing resource management for robotics modelled using the Semi-Markov Decision process has many scopes (Liu et al., 2018). In addition, several collaborative networking, such as collaborative ciphertext (Li et al., 2017), SLEs (Zhu et al., 2016) and two cloud approaches (Sotsenko et al., 2016), have proven to be highly effective. The AHP method also seemed useful for ranking specific sector decision factors (Hu, 2016). Several systems of higher education were positively affected by these. The methods include STEM (Caglar et al., 2015), machine learning (Ahad et al., 2018), PADS (Barve et al., 2017), e-learning (Amor et al., 2020) and virtual (FAB-LABS) labs (Cornetta et al., 2019). Furthermore, personalised adaptive learning could be constructed in four aspects, namely, learner profiles, personal knowledge, competency-based progression and flexible learning environments (Peng et al., 2019). Futuristically, cloud computing adoption for decision-making shows significant potential (Sabi et al., 2016).
With the developments in technology and the establishment of Industry 4.0, cloud computing has gained a lot of attention in modernising and improving all sectors’ establishment. The educational industry would also benefit from such an establishment, especially in developing countries where education quality is affected by socio-economic and geographical constraints. The drivers, such as cost reduction, more extensive accessibility, availability of varied courses, larger reach, high storage capacity and all-time availability seem to outweigh the barriers, such as the need for a platform, cost of setup, security and physiological barriers.
4.2 Cloud computing in remote learning and distance learning
One of the many profound benefits of cloud computing is its ability to aid remote learning and distance learning. Based on this, several types of research have been conducted to understand the dynamics of this better. Cloud computing is a path that will provide distance learners better services (Karak and Adhikary, 2015) and can change the way e-learning is in today’s date (Patil, 2016). Thus, several models have been developed. The Remote Collaboration Tutor (RECT) model is specifically for Master of Software Engineering students to aid them with a practical project developing experience and construct their programming skills (Ding and Cao, 2017). A few successful demonstrations have shown that the technology (InstantLab) aids in teaching operating systems classes (Neuhaus et al., 2014). Some experiments also suggest that the self-organised laboratories are viable (Celdrán et al., 2019). However, there is a need to expand the knowledge base in mechatronics and cloud e-learning using suitable supporting methods (Chao et al., 2015). But, Cloud-based e-learning is the new age of traditional e-learning that will increase the efficiency of e-learning in the future (Ahmed, 2015).
Cloud computing plays an essential part in the development and growth of Remote, as well as Distance Learning. It completely changes the idea of a traditional student-teacher classroom ideology. It enhances the performance and quality of education delivered, which is a must in this era. Not only that but also in case of a pandemic, remote learning and distance learning with cloud computing will ensure that the students will never stop learning and their education is not hindered. The flexibility provided by cloud computing is one of the most notable aspects.
4.3 Cloud-based design and manufacturing
Cloud-based CAD supports conceptual design based on user needs and open architecture (Lyu et al., 2017). Cloud-based intelligent UI gives salient characteristics like a naturalist, self-configuration, flexible customisation and mobility; more importantly, multi-tenancy achieved by virtualisation improves computational performance and network communication (Ren et al., 2015; Wang et al., 2015). Furthermore, CBDM can help learners learn, practice, use and understand collaborative design by social and technical networking (Wu et al., 2013b). However, the primary concern here is security, which can be resolved to a greater extent by introducing a centralised architecture rather than a peer-to-peer architecture (Wu et al., 2015b). Thus, the cloud facilitates modern collaborative and resource-sharing manufacturing, which helps solve complex manufacturing problems, serving as an upcoming platform for digital innovation and digital manufacturing (Adamson et al., 2017; Liu et al., 2019; Wu et al., 2015a).
Cloud computing can support CAD apps giving increased functionality in industrial and educational sectors while reducing costs (Andreadis et al., 2015; Wu et al., 2017). Mass customisation and collaborative learning are enabled for students by offering cloud-based CAD in academics (Schaefer et al., 2012). College students have shown better adaptability, according to a case study conducted in Taiwan (Jou and Wang, 2013). Cloud manufacturing technologies have the potential to transform manufacturing industries (He and Xu, 2015). It enables job allocation and scheduling quickly and effectively by implementing optimisation (Brintha and Benedict, 2018). In the future, CBDM will play a vital role in the world economy for SMMs and SMEs as switching to the cloud is much more economical for SMEs by adopting techniques like pay-per-use, make or buy, etc. (Ghomiet al., 2019; Wu et al., 2015c). Hence, CBDM paves a new path for business by providing flexible and scalable services through the internet (Malladi and Potluri, 2018).
CBDM can serve as a valuable platform in revising today’s industrial and educational sector by providing many benefits over traditional designing and manufacturing practices. The characteristics offered by cloud computing helps manufacturers adopt and use technological advances and compete globally. Over the past decade, various CAD software providers have made CAD services available on the cloud, allowing the collaborative and distributive design of products and their analysis and simulation in virtual environments. Along with CNC, automated additive manufacturing and three-dimensional printing, manufacturing software for scheduling, planning, control, packing and shipping, etc., are also available. Although several manufacturing industries and educational institutes have already incorporated cloud-based CAD; however, cloud-based manufacturing is still emerging.
4.4 Research gaps and directions
Based on an extensive literature survey, factors affecting CCE were determined. Appendix 1 shows the list of factors affecting CCE. Figure 4 shows the frequency distribution of the elements.
The findings can be summarised as follows:
Security and privacy concerns- As cloud computing provides an online platform, data security is the most prominent concern among all the stakeholders. Therefore, authentication mechanisms must ensure security and privacy concerns.
Involvement of teachers and students- Resistance to the change is a prime concern for adopting new technology such as cloud computing. Socio-economic differences in countries like India is a significant hurdle in the adoption of CCE.
Skill requirements- Students and teachers need to adapt to cloud computing. It may be noted that CC is a new technology; it is not easy to get familiar with the platform. Hence, training sessions may be conducted for all the stakeholders.
Role of top management- Top management of educational institutes need to allocate sufficient funds for technology adoption. Further, they must showcase their commitment and vision. Also, trust is an essential factor in the adoption of CCE.
Investment and infrastructure- Initial cost and infrastructure support are always a concern for the implementation of CCE. However, hardware and software support are the most essential.
Quality of service (QoS)- Cloud service providers must provide a quality capability, storage and platform. QoS is the most crucial aspect for teachers and students.
Academicians and researchers must work on the above points for the effective adoption of CCE. Then, the 43 factors mentioned in Figure 4 can be taken for further studies of CCE.
5. Discussion
5.1 Cloud computing in education
For cloud computing-based e-learning, the teachers can conduct various assignments, lectures and many other activities over the cloud. The cloud would store the data and retrieve it whenever necessary. The data can be protected or shared with the concept of private, public and hybrid clouds. However, private clouds would provide a better platform to avoid several data-sharing-based issues (Alcattan, 2014). Furthermore, this would ensure that data accessibility is well controlled. Thus, the teachers would be accessible to student data to view the lecturers and submit assignments. Therefore, the dynamic educational system will shift from geographically-based to virtual internet-based.
Further, multiple functions can also be performed over the cloud at the same time. Thus, the availability of several courses over a singular platform is extended with the help of the cloud. This resourcefulness can prove quite advantageous, especially for developing countries in which the population faces significant educational barriers or geography-based barriers. Additionally, cloud computing enhances ease of accessibility. Moreover, it ensures a platform or software anytime and anywhere with the minimal requirement of the internet. Also, it helps to reduce the need for students to travel miles to gain education like students of rural areas in India are forced to Stein (2013). cloud computing enables multiple users to edit and manage one document/task and do this simultaneously. This improves the platform for group-based projects and reduces operation time (Jose and Christopher, 2019). Furthermore, it also allows one to send and receive data quickly (through the internet). Thus, students can interact as well as to conduct a group-based learning session/discussion.
There is a widened scope for personal cloud computing as it is customised to fit the educational ideology. Cloud computing helps the institution to establish a “user type priority”. Thus, a teacher can be given some precedence and privileges adhering to the education ideology and preserving the students’ data and themselves. The students can personalise and format their assignments, tasks and documents. The private cloud ensures cybersecurity. The documents and functions of each teacher/student are secured through a protection mechanism. The user usually has to undergo a set of security checks before they can access their data. This ensures authenticity and reduces the scope of risk (Ahad et al., 2018; Sotsenko et al., 2016).
Since the cloud is an online platform, the fear of loss of data is drastically reduced. Thus, teachers can view each student’s work personally and point out their errors and strengths. Furthermore, computing provides a large base for storing multiple data formats (Baldassarre et al., 2018). Also, storing this data is very easy and does not require additional measures or knowledge. Thus, it reduces the need for physical space. In addition, this reduces the need for carrying around books and documents. Hence, cloud computing would aid the education system in developing countries to an unfathomable extent.
However, the software, documents and task platforms have to be compatible with the cloud software. This also poses a threat to change, introduce, expand or upgrade the cloud. Thus, many times, systems are forced not to use other platforms. In addition, cloud computing requires the internet, infrastructure and several other aspects that work coherently to create a platform. Minor glitches in one of these cause a drastic effect on the smooth working of the system.
5.2 Cloud computing in remote learning and distance learning
Cloud computing has a huge role in the development and growth of Remote/Distance Learning. It completely changes the idea of a traditional student-teacher classroom ideology. It enhances the performance and quality of education delivered, which is a must in this era. Cloud brings the students and lecturers together on a single platform. The school, college or university need not buy, own or maintain their servers. They also can be sure about their resources being secured on the cloud. These resources become more accessible as the user does not need to download any additional help or applications. This also reduces the need for any other hardware requirement and does not add any additional cost to the users, thus proving that cloud computing massively helps remote learning.
Not only that but also in case of a pandemic, remote learning and distance learning with cloud computing will ensure that the students will never stop learning and their education is not hindered. The flexibility provided by cloud computing is one of the most notable aspects.
5.3 Cloud-based design and manufacturing
CBDM systems are now widely prevalent in industries as well as in education sectors. They hold two significant advantages over the conventional manufacturing processes: Multi-Tenancy and Virtualisation. Multi-tenancy implies the sharing of applications or interfaces with different users at the same time. Many projects require the combined effect of other individuals or teams. Cross-domain processes are now used in product development industries, which require the integration of different design stages. Cross-functional teams can work with the same design simultaneously due to multi-tenancy without version check and any location (Malladi and Potluri, 2018; Wu et al., 2017). Virtualisation gives us a real-time experience for the product to be made. A virtual model of any part or product can be made and deployed in virtual executable environments, which will help refine the product and notice errors in the design stage rather than in the developed product. This will reduce the redundant processes and costs and save time (Malladi and Potluri, 2018; Wang et al., 2015).
Other advantages usually include Application availability, Reduced response lag, Custom solutions, Economic benefits, mobility, etc. Also, several licenses’ requirement is lowered as these depend on the concurrent users of the maximum order instead of the total number of users (Andreadis et al., 2015). As the software and CAD data are saved on the same server, the time lag in response time is reduced (Schaefer et al., 2012). Centralised systems facilitate libraries that are directly accessible by everyone (Andreadis et al., 2015). The application can be accessed through any platform, for example, via an internet browser on a desktop or through apps on smartphones or tablets (Andreadis et al., 2015; Wu et al., 2017). Maintenance and personnel costs can be reduced, and the pay-as-you-go service model also reduces upfront investment on hardware and software. Further, market-entry costs observe significant reduction (Wang et al., 2015; Wu et al., 2015a; Wu et al., 2017).
It may be noted that there are certain limitations to CBDM as well. The major drawback of the CBDM systems is the availability of the internet; however, specific CAD tools like Onshape provide offline access to the cloud-based service, which requires installing the software on Personal Computers, whereas other tools like Autodesk 360 do not require the same. A poor Wi-Fi signal or a faulty network can easily disrupt the workflow (Wu et al., 2017). The on-demand resources and services require cost-saving estimations and robust cost measurement models (Adamson et al., 2017; Malladi and Potluri, 2018). Cloud adoption is also an important issue, as the companies must have a proper cloud strategy, and the employees are needed to be trained with the necessary skills (Adamson et al., 2017; Ghomi et al., 2019). Another significant challenge the CBDM systems face is the data security threat due to unsolved legal issues. As a result, the stakeholders are exposed to risks of data disclosure. Advanced network trafficking techniques may be used to prevent this, such as a secured socket layer and a transportable layer for security (Adamson et al., 2017; Ghomi et al., 2019; Wang et al., 2015). Fluctuating demand influences product planning effectiveness, and thus the software related to planning must be self-adaptable or updated from time to time. Further, CBDM suffers from energy consumption and carbon emissions (Ghomi et al., 2019).
6. Limitations and future scope
The above study is limited to papers published only in the past decade from 2011 to 2020. This study can be extended to previous years and the articles to be published in the upcoming years. A bibliometric survey can be conducted, which will take thousands of papers into consideration rather than just the 72 mentioned above. The research scope can be further extended from the educational sector to all other sectors, including health care, industry, finance and many more. These papers can be studied in further detail to derive the drivers and barriers of cloud computing in each sector, and rankings can be established to help organisations adopt cloud computing more efficiently. Besides, this review was unable to include journal articles published in different languages. Hence, there is a scope for further research and analysis considering the geographical boundaries, development statuses of countries and several other factors. Furthermore, a list of elements shown in Appendix 1 that affect the adoption of cloud computing technologies in the education sector may be analysed and modelled using qualitative and quantitative tools. This could help improve the adoption of CCE universities and platforms, especially for developing countries.
7. Implications of the study
The research presented in this study can help bridge the knowledge gap regarding cloud computing and its capabilities. The literature review conducted can act as a theoretical base for educational institutes, big corporations and SMEs seeking to incorporate cloud computing in their organisations. Furthermore, manufacturing companies in developing or underdeveloped countries aspiring to compete in the global markets need to use the CBDM tools available in the industry. This study can provide them with the required cognisance related to CBDM, along with its capabilities and limitations.
Developing countries like India face several issues in imparting education effectively, especially in remote areas, which deprives several adolescents of the educational background they require to uplift their standard of living and their communities’. Thus, with the adoption of cloud-based learning, we could reach out to communities that do not receive an education due to geographical, economic and many other barriers. Moreover, this internet-based technology would help create jobs and provide instruction in emergencies such as COVID-19. The factors provided in this study shorten the steps to implement the CC for educational purposes. Thus, the CC can guarantee safer and uninterrupted platforms for education.
Moreover, institutes situated in secluded areas are now thriving to provide the inhabitants with the same level of education as the rest of the world. The COVID-19 global pandemic has also caused the world to connect without physical contact, becoming a future necessity. The remote and distance learning medium can facilitate this through cloud computing. Thus, this research forms the first milestone for every sector towards incorporating cloud computing in their organisations.
8. Concluding remarks
Along with their applications, IT and communication technology have emerged to advance communication tools to be skillfully used in daily lives (García-Peñalvo et al., 2014). This paper aims at delivering a comprehensive review of research done in cloud computing about the educational sector with a focus on cloud computing for education, remote/distance learning and CBDM. The period considered for the study is between the years 2011 and 2020. Initially, 829 research items were collected from the Scopus database, and finally, 72 papers were shortlisted using the TAK principle. These were categorised into three categories: cloud computing for education, cloud computing for remote/distance learning and CBDM. Based on these categories, an intrinsic review of each paper was conducted. This review explored the theme, concept and theoretical framework of each article. These categories were impervious to realise the scope of the adoption of CCE in this sector.
Furthermore, the papers were categorised based on the year, journal of publication and country. It was realised that 2015 had the most significant number of articles published in this field. A journal entitled “Smart Learning Environments” published the most papers, and the USA did the most research in these categories for the considered articles. This research helps understand each country’s current scenario and where they stand with their research in cloud computing for educational institutions.
Although cloud computing opens up many possibilities, it is not very well understood by educational organisations and its potential is not being explored to its fullest. This is majorly due to the lack of knowledge and many other factors like cost, security, etc. Thus, it can be inferred from this research that in developing countries like India, cloud computing is still an emerging concept and has many potentials to influence the education system positively.
The authors declare that there is no conflict of interest.
No funding has been received for carrying out this research.
Methodology adopted for literature review
Broad classifications of shortlisted research articles
Number of papers published annually (X-axis: year; and Y-axis: % of documents)
List of factors (X-axis: No. of articles; and Y-axis: factor)
Nomenclatures
| S.N. | Abbreviation | Description |
|---|---|---|
| 1 | AHP | Analytic Hierarchy Process |
| 2 | BDS | Beijing Digital School |
| 3 | CBDM | Cloud-based Design and Manufacturing |
| 4 | CEMS | Cloud-based Educational Management Systems |
| 5 | CBLD | Cloud-based Learning Designs |
| 6 | Cloud | Cloud Computing |
| 7 | CCE | Cloud Computing in Education |
| 8 | CLEM | Cloud E-learning for Mechatronics |
| 9 | C2SuMO | Cloud-based, Collaborative and Scaled-up Modelling and Simulation Framework |
| 10 | CAD | Computer Aided Design |
| 11 | CAE | Computer Aided Engineering |
| 12 | CAM | Computer Aided Manufacturing |
| 13 | CNC | Computer Numeric Control |
| 14 | CNC | Computerised Numerical Control |
| 15 | DEMATEL | Decision making trial and evaluation laboratory |
| 16 | DL | Distance Learning |
| 17 | GDM | Group Decision Making |
| 18 | HaaS | Hardware as a Service |
| 19 | IT | Information Technology |
| 20 | IaaS | Infrastructure as a Service |
| 21 | IoT | Internet of Things |
| 22 | LAS | Learning Analytics Systems |
| 23 | MOOC | Massive Open Online Courses |
| 24 | MSE | Master of Software Engineering |
| 25 | MK-FHE | Multi-key fully homomorphic encryption |
| 26 | NFV | Network function virtualisation |
| 27 | PC | Personal Computer |
| 28 | PaaS | Platform as a Service |
| 29 | PIO | Population, Intervention, Output |
| 30 | RECT | Remote Collaboration Tutor |
| 31 | RL | Remote Learning |
| 32 | REST | Representational State Transfer |
| 33 | STEM | Science, Technology, Engineering and Management |
| 34 | SME | Small and Medium Enterprises |
| 35 | SLE | Smart Learning Environments |
| 36 | SMM | Social Media Marketing |
| 37 | SaaS | Software as a Service |
| 38 | SDN | Software-defined networking |
| 39 | SWOT | Strengths, Weaknesses, Opportunities and Threats |
| 40 | TAM | Technology Acceptance Model |
| 41 | TOE | Technology Organisation Environment |
| 42 | TAK | Title, Abstract and Keyword |
| 43 | UI | User Interface |
| 44 | VLE | Virtual Learning Environment |
Literature survey on CCE (theoretical articles)
| S.N. | Author(s) year | Country | Type of paper | The objective of the study | Tools/techniques used |
|---|---|---|---|---|---|
| 1 | Al-Samarraie and Saeed (2018) | Malaysia | Literature review | To understand how cloud computing tools have shaped collaborative learning and the accessibility of these tools | Qualitative analysis |
| 2 | Baldassarre et al. (2018) | Italy | Literature review | To evaluate the scope of CCE concerning pedagogy and educational processes | Quantitative analysis using PIO (population, intervention, output) paradigm |
| 3 | Bhatiasevi and Naglis (2016) | USA | Case study | To examine the adoption and utilisation of cloud computing in higher education in developing countries | Data collection and structural equation modelling |
| 4 | Bouyer and Arasteh (2014) | Iran | Theoretical paper | To justify the significance of online training | Qualitative analysis |
| 5 | Dron (2018) | USA | Theoretical paper | To systematically understand the difference between smart and not-smart systems | Qualitative analysis based on Cipollis laws |
| 6 | Elgelany and Alghabban (2017) | Sudan | Literature review | To identify critical places of improvement and suggest significant benefits to adopting cloud computing | Qualitative analysis |
| 7 | Gercek et al. (2016) | USA | Case study | To understand how cloud computing can be implemented in a lab-based e-learning system | Data collection and analysis using a phased approach |
| 8 | González-Martínez et al. (2015) | Spain | Literature review | To understand and explore the advantages and challenges of CCE and categorise them | Qualitative analysis based on methodological guidelines by Kitchenham and Charters |
| 9 | Gros (2016) | Spain | Theoretical paper | To effectively determine the main attributes of smart learning and the key challenges | Qualitative analysis |
| 10 | Hew and Kadir (2016) | Malaysia | Survey | The study fills the research gaps on instructional efficiency from the previously ignored novel concept of the cloud-based VLE | Nation-wide survey and analysis by artificial neural network approach |
| 11 | Islam et al. (2017) | Bangladesh | Case study | To recognise how Bangladesh can be benefited by applying cloud in education | Qualitative analysis of case studies and success stories |
| 12 | Jose and Christopher (2019) | India | Case study | To encode and decode data stored in the cloud data centres with minor duplication | Encryption of messages using the Reed Solomon code and its performance analysis |
| 13 | Karim and Rampersad (2017) | Australia | Theoretical paper | To examine technological-based learning adoption techniques in developing countries | Qualitative analysis |
| 14 | Morze et al. (2015) | Ukraine | Survey | To understand how a student uses ICT in the professional world | Statistical analysis of the pedagogical experiments |
| 15 | Mościcki and Mascetti (2018) | Switzerland | Literature review | To study and understand cloud synchronisation and sharing services | Qualitative analysis |
| 16 | Naveed et al. (2019) | Saudi Arabia | Theoretical paper | To evaluate and rank factors that affect cloud-based learning | Analysis by AHP (analytic hierarchy process) Fuzzy AHP, GDM (Group Decision Making) |
| 17 | Qasem et al. (2019) | Malaysia | Literature review | To analyse the current adoption and utilisation of cloud computing in higher education institutions | Literature survey using critical words followed by an intensive study |
| 18 | Ramachandran et al. (2014) | India | Case study | To select an appropriate model for implementing cloud computing in e-learning | A combinatorial approach for evaluation of articles, Fuzzy AHP, GDM |
| 19 | Saini and Kaur (2017) | India | Theoretical paper | To examine the role of cloud computing in the education system | Qualitative analysis |
| 20 | Sarrab et al. (2015) | Oman | Survey | To understand and properly define the requirements for cloud computing and M-learning | Weighting based on the importance of cloud computing requirements |
| 21 | Stein et al. (2013) | USA | Case study | To explore the various ways of cloud designing, especially for education | Qualitative analysis |
| 22 | Tarhini et al. (2018) | Oman | Survey | To examine critical elements that contribute to or discourage the use of cloud computing in higher education | Technology-organisation-environment (TOE) framework, structural equation modelling based on AMOS 22.0 |
| 23 | Wu et al. (2013a) | China | Case study | To put forward a new evaluation framework to investigate the main challenges affecting the university’s acceptance of using internal cloud services | DEMATEL (DEcision MAking Trial and Evaluation Laboratory) |
Literature survey on CCE (implementation articles)
| S.N. | Author year | Country | Type of paper | The objective of the study | Tools/techniques used |
|---|---|---|---|---|---|
| 1 | Ahad et al. (2018) | India | Conceptual framework | To present a secured and agile model based on IoT for the education sector | Learning analytics systems (LAS) model |
| 2 | Al-Harthi et al. (2018) | Oman | Conceptual framework | To develop and validate CBLD created by teachers | Six phased Rubric Development and validation |
| 3 | Al-Shammari (2014) | Kuwait | Conceptual framework | To fuse cloud computing with social e-learning to build a new e-learning model | Social tools, web-based learning elements and interactive features of Web 2.0 |
| 4 | Amor et al. (2020) | Tunisia | Simulation | To propose a solution that extends learning content from the cloud to the edge of the network | Bilinear pairing, identity-based broadcast encryption, Access Tree, Ciphertext-Policy Attribute-based Encryption (CP-ABE), private equality test |
| 5 | Banait et al. (2015) | India | Simulation | To understand and determine the employability of CCE | Browser/Agent/Server model |
| 6 | Barve et al. (2017) | USA | Simulation | To create a prototype and execute the implementations of PADS (Playground of Algorithms for Distributed Systems) | Model-based Engineering and Distributes systems Algorithm |
| 7 | Boja et al. (2013) | Romania | Simulation | Conduct a SWOT analysis on an actual problem dealing with standard university IT infrastructure moving to the cloud | SWOT (strengths, weaknesses, opportunities and threats) analysis |
| 8 | Caglar et al. (2015) | USA | Simulation | To present a cloud-based collaborative and scaled up modelling and simulation framework for STEM education called C2SuMo | Type of cloud: SAAS software: C2SuMO (cloud-based, collaborative and scaled-up modelling and simulation framework) |
| 9 | Cornetta et al. (2019) | Italy | Mathematical model | To put forward a novel concept called Fabrication-as-a-service, which uses IoT | Fab lab gateway, REST (REpresentational State Transfer) APIs |
| 10 | Daim et al. (2016) | USA | Conceptual framework | To build a decision model aiding the valuation and selection of a campus-wide e-Portfolio solution | Hierarchal decision modelling |
| 11 | Hu (2016) | China | Simulation | To develop a platform to help solve the problems of the e-Learning process | Data mining technology, AHP-BP neural network algorithm |
| 12 | Kim et al. (2016) | USA | Conceptual framework | To put forward a public auditing protocol for educational multimedia data outsourced in the cloud storage | Setup, KeyGen, SigGen, Challenge, ProofGen, ProofVerify algorithms |
| 13 | Li et al. (2017) | China | Mathematical model | To propose two schemes to tackle specific challenges regarding the storage and privacy of clouds | Multi-key fully homomorphic encryption (MK-FHE) |
| 14 | Liao et al. (2016) | China | Simulation | To design and implement an adaptive e-learning environment using cloud computing | Load balancing, Honey Bee foraging, Software: BDS |
| 15 | Liu et al. (2018) | China | Simulation | To propose RL-based resource allocation schemes to help the cloud accept or decline a request | Numerical analysis, greedy allocation scheme |
| 16 | Peng et al. (2019) | China | Conceptual framework | To provide a detailed interpretation of personalised adaptive learning | Circular, vertical ellipse and horizontal ellipse strategy, Man-machine collaborative decision-making |
| 17 | Sabi et al. (2016) | USA | Conceptual framework | To propose a model considering technological, economic and contextual influences in the perception and adoption of cloud computing at universities in sub-Saharan Africa | Diffusion of innovation theory and the TAM |
| 18 | Saleh et al. (2018) | Malaysia | Conceptual framework | To propose a framework “Education Cloud”, that considers the flexibility of cloud and supplies institutes with a single point of contact | CEMS |
| 19 | Sotsenko et al. (2016) | Sweden | Simulation | Design a mobile learning application in a cloud-computing environment | Data collection, analysis and visualisation |
| 20 | Wang (2013) | Taiwan | Mathematical model | To launch cloud computing into an e-learning platform to allow the integration of different e-learning standards | The automated standard transformation algorithm |
| 21 | Zhang et al. (2015) | China | Mathematical model | To examine the parallel K-means clustering algorithm based on cloud computing platform Hadoop | Hadoop framework |
| 22 | Zhu et al. (2016) | China | Conceptual framework | To propose a four-tier framework of smart pedagogies and 10 critical features of SLE to foster smart learners | Pedagogical theory |
| 23 | Zurita (2014) | Chile | Simulation | To understand the importance of situated learning | Microblogs, Geo collaboration |
Literature survey on cloud computing in remote learning and distance learning
| S.N. | Author year | Country | Type of paper | The objective of the study | Tools/techniques used |
|---|---|---|---|---|---|
| 1 | Ahmed (2015) | Saudi Arabia | Case study | To understand how cloud computing will play a role in building a sustainable and flourishing e-learning system | Analysis and investigation of e-learning systems |
| 2 | Celdrán et al. (2019) | Ireland | Simulation | To propose an SDN/NFV-based architecture to adjust to the remote labs’ configuration according to customer demand | Network function virtualisation (NFV) and Software-defined networking (SDN) |
| 3 | Chao et al. (2015) | China | Simulation | To provide an overview of the CLEM project, i.e. a new cloud-based e-learning approach | CLEM (Cloud e-learning for Mechatronics) approach |
| 4 | Cvetkovic et al. (2013) | Serbia | Survey | A survey that will help us distinguish between cloud-based e-learning systems and classical server-oriented e-Learning systems | Oracle, MySQL, Postgres, Microsoft SQL Server and SQLite |
| 5 | Ding and Cao (2017) | China | Simulation | To suggest a tool (cloud-based) supporting the practice courses of software engineering in alliance with remote users | Iterative development approach |
| 6 | Karak and Adhikary (2015) | India | Case study | Proposal of how cloud computing can be used in distance learning | Intensive survey and analysis |
| 7 | Neuhaus et al. (2014) | Germany | Simulation | The development of InstantLab, a self-service web platform, is aiding interactive software experiments | Type of cloud: IAAS, Private Cloud Software: HP converged Cloud |
| 8 | Patil (2016) | India | Case study | To understand the importance of cloud computing in e-learning and describe a cloud computing platform design with e-learning | Intensive survey and analysis |
Literature survey on CBDM
| S.N. | Author year | Country | Type of paper | The objective of the study | Tools/techniques used |
|---|---|---|---|---|---|
| 1 | Adamson et al. (2017) | UK | Literature review | To present research issues and future trends with cloud manufacturing | Qualitative analysis |
| 2 | Andreadis et al. (2015) | Greece | Case study | To study the use of cloud computing in engineering design and manufacturing with its application | Private cloud deployment model |
| 3 | Brintha and Benedict (2018) | India | Literature review | To study the optimisation concept to be adopted in cloud manufacturing | Qualitative analysis |
| 4 | Ghomi et al. (2019) | Iran | Literature review | To discuss CM architecture, challenges and future trends | Qualitative analysis |
| 5 | He and Xu (2014) | USA | Literature review | To study the implementation and functioning of CM in SMEs | Qualitative analysis |
| 6 | Jou and Wang (2013) | Taiwan | Case study | To study academic performances and learning attitudes by induction of cloud-based CAD | TAM |
| 7 | Liu et al. (2019) | China | Theoretical paper | To study the relationship between CM and CPS, Smart manufacturing, Industry 4.0 and the Industrial internet | Qualitative analysis |
| 8 | Lyu et al. (2017) | Canada | Literature review | To discuss CAD-based product modelling from perspectives of knowledge, distributed computing and product lifecycle | Qualitative analysis |
| 9 | Malladi and Potluri (2018) | India | Theoretical paper | To discuss features of cloud computing as a requisite for CBDM | Qualitative analysis |
| 10 | Ren et al. (2015) | China | Prototype | To present a sketch-based pad system prototype to search part drawings in the cloud | Intelligent user interface |
| 11 | Schaefer et al. (2012) | USA | Case study | To discuss the motivation and infrastructure of CBDM in education | Qualitative analysis |
| 12 | Wang et al. (2015) | USA | Literature review | To help researchers and manufacturing enterprises in the application of cloud computing in manufacturing processes | Analytic hierarchy process (AHP) |
| 13 | Wu et al. (2013b) | USA | Theoretical paper | To state the potential of cloud computing in collaborative design and distributed manufacturing | Qualitative analysis |
| 14 | Wu et al. (2015a) | USA | Theoretical paper | To define requirements to be satisfied by an ideal CBDM system | Qualitative analysis |
| 15 | Wu et al. (2015b) | China | Case study | To present a service-oriented model for data exchange in CBDM | Qualitative analysis |
| 16 | Wu et al. (2015c) | USA | Case study | To identify critical economic benefits of CBDM over traditional design and manufacturing | Qualitative analysis |
| 17 | Wu et al. (2017) | USA | Theoretical paper | To analyse the chances and risks of cloud-based CAD | Qualitative analysis |
Yearly frequency of papers published
| S.N | Year | No. of papers | (%) |
|---|---|---|---|
| 1 | 2011 | 0 | 0.00 |
| 2 | 2012 | 1 | 1.39 |
| 3 | 2013 | 7 | 9.72 |
| 4 | 2014 | 7 | 9.72 |
| 5 | 2015 | 17 | 23.61 |
| 6 | 2016 | 13 | 18.06 |
| 7 | 2017 | 8 | 11.11 |
| 8 | 2018 | 11 | 15.28 |
| 9 | 2019 | 7 | 9.72 |
| 10 | 2020 | 1 | 1.39 |
| Total | 72 | 100 | |
Number of papers published in each journal
| S.N. | Name of the Journal | Abbreviation | No. of papers |
|---|---|---|---|
| 1 | A Journal for Information Technology, Education Development and Teaching Methods of Technical and Natural Sciences | JITEDTMTNS | 1 |
| 2 | Advances in Engineering Software | ADV ENG SOFTW | 1 |
| 3 | Advances in Intelligent Systems and Computing | ADV INTELL SYS COMPUT | 1 |
| 4 | Artificial Intelligence for Engineering Design, Analysis and Manufacturing | AIEDAM | 1 |
| 5 | Campus-Wide Information Systems | CWIS | 1 |
| 6 | Cluster Computing | CLUSTER COMPUT | 1 |
| 7 | Computer and Information Science | CIS | 1 |
| 8 | Computer-Aided Design | COMPUT AIDED DES | 1 |
| 9 | Computers and Education | COMPUT EDUC | 2 |
| 10 | Computers in Human Behavior | COMPUT HUM BEHAV | 1 |
| 11 | Computers in Industry | COMPUT IND | 1 |
| 12 | Computers in the Schools | COMPUT SCH | 1 |
| 13 | Education Information Technology | EAIT | 1 |
| 14 | Future Generation Computer Systems | FUTURE GENER COMP SY | 4 |
| 15 | IEEE Access | IEEE ACCESS | 3 |
| 16 | IEEE Internet of Things Journal | IEEE INTERNET THINGS J | 1 |
| 17 | IEEE Transactions on Cloud Computing | IEEE T CLOUD COMPUT | 2 |
| 18 | IEEE Transactions on Education | IEEE T EDUC | 1 |
| 19 | IEEE Transactions on Learning Technologies | IEEE TLT | 1 |
| 20 | IEEE Transactions on services computing | IEEE T SERV COMPUT | 1 |
| 21 | Industrial Management and Data Systems | IND MANAGE DATA SYST | 1 |
| 22 | International Journal of Advanced Computer Science and Applications | IJACSA | 1 |
| 23 | International Journal of Advanced Manufacturing Technology | INT J ADV MANUF TECH | 1 |
| 24 | International Journal of Advanced Research in Computer Science | IJARCS | 1 |
| 25 | International Journal of Cloud Applications and Computing | IJCAC | 1 |
| 26 | International Journal of Cloud Computing | IJcloud computing | 1 |
| 27 | International Journal of Computer Aided Engineering and Technology | IJCAET | 1 |
| 28 | International Journal of Computer Integrated Manufacturing | IJCIM | 3 |
| 29 | International Journal of Computer Science and Mobile Computing | IJCSMC | 1 |
| 30 | International Journal of Computer Science, Engineering and Applications | IJCSEA | 1 |
| 31 | International Journal of Emerging Technologies in Learning | IJET | 2 |
| 32 | International Journal of Engineering Sciences and Research Technology | IJESRT | 1 |
| 33 | International Journal of Grid and Distributed Computing | IJGDC | 1 |
| 34 | International Journal of Information Management | IJIM | 1 |
| 35 | International Journal of Information Sources and Services | IJSS | 1 |
| 36 | International Journal of Information Technology | IJIT | 1 |
| 37 | International Journal of Innovation and Learning | IJIL | 1 |
| 38 | International Journal of Mechanical and Production Engineering Research and Development | IJMPERD | 1 |
| 39 | International Journal of Mobile Learning and Organization | IJMLO | 1 |
| 40 | International Journal of Online and Biomedical Engineering | IJOE | 1 |
| 41 | International Journal of Soft Computing | IJSC | 1 |
| 42 | IOSR Journal of Business and Management | IOSR-JBM | 1 |
| 43 | Journal of Computing and Information Science in Engineering | JCISE | 1 |
| 44 | Journal of Integrated Design and Process Science | JIDPS | 1 |
| 45 | Journal of Manufacturing Science and Engineering | J MANUF SCI E-T ASME | 3 |
| 46 | Multimedia Tools and Applications | MULTIMED TOOL APPL | 1 |
| 47 | Procedia-Computer Science | PROCEDIA COMP SCI | 1 |
| 48 | Procedia-Social and Behavioral Sciences | PROCEDIA SOC BEHAV SCI | 2 |
| 49 | Simulation Modeling Practice and Theory | SIMUL MODEL PRACT TH | 1 |
| 50 | Smart Learning Environments | SMART LEARN ENV | 6 |
| 51 | Technology, Innovation and Education | TECH INNOV EDU | 1 |
| 52 | The ASEE Computers in Education Journal | COED | 1 |
| 53 | The Electronic Library | ELEC LIB | 1 |
| 54 | The New Educational Review | TNER | 1 |
| Total | 72 | ||
Annual frequency distribution of papers published in the journals
| S.N. | Jourftnals Name | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | Total |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | JITEDTMTNS | 1 | 1 | |||||||||
| 2 | ADV ENG SOFTW | 1 | 1 | |||||||||
| 3 | ADV INTELL SYS COMPUT | 1 | 1 | |||||||||
| 4 | AIEDAM | 1 | 1 | |||||||||
| 5 | CWIS | 1 | 1 | |||||||||
| 6 | CLUSTER COMPUT | 1 | 1 | |||||||||
| 7 | CIS | 1 | 1 | |||||||||
| 8 | COMPUT AIDED DES | 1 | 1 | 2 | ||||||||
| 9 | COMPUT EDUC | 1 | 1 | |||||||||
| 10 | COMPUT HUM BEHAV | 1 | 1 | |||||||||
| 11 | COMPUT IND | 1 | 1 | |||||||||
| 12 | COMPUT SCH | 1 | 1 | |||||||||
| 13 | EAIT | 1 | 1 | |||||||||
| 14 | FUTURE GENER COMP SY | 1 | 1 | 1 | 1 | 4 | ||||||
| 15 | IEEE ACCESS | 1 | 1 | 1 | 3 | |||||||
| 16 | IEEE INTERNET THINGS J | 1 | 1 | |||||||||
| 17 | IEEE T CLOUD COMPUT | 1 | 1 | 2 | ||||||||
| 18 | IEEE T EDUC | 1 | 1 | |||||||||
| 19 | IEEE TLT | 1 | 1 | |||||||||
| 20 | IEEE T SERV COMPUT | 1 | 1 | |||||||||
| 21 | IND MANAGE DATA SYST | 1 | 1 | |||||||||
| 22 | IJACSA | 1 | 1 | |||||||||
| 23 | INT J ADV MANUF TECH | 1 | 1 | |||||||||
| 24 | IJARCS | 1 | 1 | |||||||||
| 25 | IJCAC | 1 | 1 | |||||||||
| 26 | IJcloud computing | 1 | 1 | |||||||||
| 27 | IJCAET | 1 | 1 | |||||||||
| 28 | IJCIM | 1 | 1 | 1 | 3 | |||||||
| 29 | IJCSMC | 1 | 1 | |||||||||
| 30 | IJCSEA | 1 | 1 | |||||||||
| 31 | IJET | 1 | 1 | 2 | ||||||||
| 32 | IJESRT | 1 | 1 | |||||||||
| 33 | IJGDC | 1 | 1 | |||||||||
| 34 | IJIM | 1 | 1 | |||||||||
| 35 | IJSS | 1 | 1 | |||||||||
| 36 | IJIT | 1 | 1 | |||||||||
| 37 | IJIL | 1 | 1 | |||||||||
| 38 | IJMPERD | 1 | 1 | |||||||||
| 39 | IJMLO | 1 | 1 | |||||||||
| 40 | IJOE | 1 | 1 | |||||||||
| 41 | IJSC | 1 | 1 | |||||||||
| 42 | IOSR-JBM | 1 | 1 | |||||||||
| 43 | JCISE | 1 | 1 | |||||||||
| 44 | JIDPS | 1 | 1 | |||||||||
| 45 | J MANUF SCI E-T ASME | 3 | 3 | |||||||||
| 46 | MULTIMED TOOL APPL | 1 | 1 | |||||||||
| 47 | PROCEDIA COMP SCI | 1 | 1 | |||||||||
| 48 | PROCEDIA SOC BEHAV SCI | 1 | 1 | 2 | ||||||||
| 49 | SIMUL MODEL PRACT TH | 1 | 1 | |||||||||
| 50 | SMART LEARN ENV | 2 | 2 | 2 | 6 | |||||||
| 51 | TECH INNOV EDU | 1 | 1 | |||||||||
| 52 | COED | 1 | 1 | |||||||||
| 53 | ELEC LIB | 1 | 1 | |||||||||
| 54 | TNER | 1 | 1 | |||||||||
| Total | – | 1 | 7 | 7 | 17 | 13 | 8 | 11 | 7 | 1 | 72 |
Number of papers published country wise
| S.N. | Country | No. of papers | % of papers published |
|---|---|---|---|
| 1 | Australia | 1 | 1.39 |
| 2 | Bangladesh | 1 | 1.39 |
| 3 | Canada | 1 | 1.39 |
| 4 | Chile | 1 | 1.39 |
| 5 | China | 13 | 18.06 |
| 6 | Germany | 1 | 1.39 |
| 7 | Greece | 1 | 1.39 |
| 8 | India | 9 | 12.5 |
| 9 | Iran | 2 | 2.78 |
| 10 | Ireland | 1 | 1.39 |
| 11 | Italy | 2 | 2.78 |
| 12 | Kuwait | 1 | 1.39 |
| 13 | Malaysia | 5 | 6.95 |
| 14 | Oman | 3 | 4.17 |
| 15 | Romania | 1 | 1.39 |
| 16 | Saudi Arabia | 2 | 2.78 |
| 17 | Serbia | 1 | 1.39 |
| 18 | Spain | 2 | 2.78 |
| 19 | Sudan | 1 | 1.39 |
| 20 | Sweden | 1 | 1.39 |
| 21 | Switzerland | 1 | 1.39 |
| 22 | Taiwan | 2 | 2.78 |
| 23 | Tunisia | 1 | 1.39 |
| 24 | UK | 1 | 1.39 |
| 25 | Ukraine | 1 | 1.39 |
| 26 | USA | 16 | 22.23 |
| Total | 72 | 100 | |
Annual frequency distribution of papers published in each country
| S.N | Year/Country | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | Total |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Australia | 1 | 1 | |||||||||
| 2 | Bangladesh | 1 | 1 | |||||||||
| 3 | Canada | 1 | 1 | |||||||||
| 4 | Chile | 1 | 1 | |||||||||
| 5 | China | 1 | 4 | 3 | 2 | 1 | 2 | 13 | ||||
| 6 | Germany | 1 | 1 | |||||||||
| 7 | Greece | 1 | 1 | |||||||||
| 8 | India | 1 | 2 | 1 | 1 | 4 | 9 | |||||
| 9 | Iran | 1 | 1 | 2 | ||||||||
| 10 | Ireland | 1 | 1 | |||||||||
| 11 | Italy | 1 | 1 | 2 | ||||||||
| 12 | Kuwait | 1 | 1 | |||||||||
| 13 | Malaysia | 1 | 2 | 1 | 1 | 5 | ||||||
| 14 | Oman | 1 | 2 | 3 | ||||||||
| 15 | Romania | 1 | 1 | |||||||||
| 16 | Saudi Arabia | 1 | 1 | 2 | ||||||||
| 17 | Serbia | 1 | 1 | |||||||||
| 18 | Spain | 1 | 1 | 2 | ||||||||
| 19 | Sudan | 1 | 1 | |||||||||
| 20 | Sweden | 1 | 1 | |||||||||
| 21 | Switzerland | 1 | 1 | |||||||||
| 22 | Taiwan | 2 | 2 | |||||||||
| 23 | Tunisia | 1 | 1 | |||||||||
| 24 | UK | 1 | 1 | |||||||||
| 25 | Ukraine | 1 | 1 | |||||||||
| 26 | USA | 1 | 2 | 2 | 3 | 6 | 1 | 1 | 16 | |||
| Total | 0 | 1 | 7 | 7 | 17 | 13 | 8 | 11 | 7 | 1 | 72 |
Notes:The maximum number of papers were published from the USA (22.23%), followed by China (18.06%). Nine articles (12.5%) were published from India, indicating an excellent scope for research in this area. In addition, the year-wise frequency distribution of papers shows that China and the USA published most of the papers in the years 2015 and 2016, respectively
List of factors affecting CCE
| Sr. No. | Factors | References |
|---|---|---|
| 1 | Adaptability | (Liao et al., (2016); Celdrán et al., (2019); Dron, (2018); Bouyer and Arasteh, (2014); Elgelany and Alghabban, (2017); Liu et al., (2018)) |
| 2 | Availability of platform | (Baldassarre et al., (2018); Gros, (2016); Islam et al., (2017); Liao et al., (2016)) |
| 3 | Cognitive learning ability | (Ding and Cao, (2017); Ahad et al., (2018)) |
| 4 | Compatibility | (Naveed et al., (2019); Tarhini et al., (2018)) |
| 5 | Computational cost | (Kim et al., (2016)) |
| 6 | Confidence | (Al-Samarraie and Saeed, (2018); Amor et al., (2020); Sarrab et al., (2015); Yaghmaei and Binesh, (2015)) |
| 7 | Connectivity | (Al-Samarraie and Saeed, (2018); Stein, (2013); Liao et al., (2016)) |
| 8 | Control | (Liao et al., (2016); Liu et al., (2018); Mościcki and Mascetti, (2018); Islam et al., (2017); Sarrab et al., (2015); Wu et al., (2013a), (2013b)) |
| 9 | Cost of hardware | (Banait et al., (2015); Qasem et al., (2019)) |
| 10 | Cost of infrastructure | (Ahad et al., (2018); Bouyer and Arasteh, (2014); Ramachandran et al., (2014); Liu et al., (2018)) |
| 11 | Cost of maintenance | (Saleh et al., (2018); Daim et al., (2016); Boja et al., (2013); Ramachandran et al., (2014); Liu et al., (2018)) |
| 12 | Cost of software | (Stein, (2013); Qasem et al., (2019); Elgelany and Alghabban, (2017); González-Martínez et al., (2015)) |
| 13 | Dynamic data | (Kim et al., (2016); Liao et al., (2016)) |
| 14 | Ease of use | (Caglar et al., (2015); Liao et al., (2016); Ding and Cao, (2017); Jose and Christopher, (2019); Naveed et al., (2019); Sarrab et al., (2015); Sabi et al., (2016); Wu et al., (2013a), (2013b)) |
| 15 | Educational ideology | (Zhu et al., (2016)) |
| 16 | Extended machine life | (Stein, (2013)) |
| 17 | Interoperability | Mościcki and Mascetti, (2018); Islam et al., (2017); Yaghmaei and Binesh, (2015) |
| 18 | Links and resources | (Ding and Cao, (2017); Saleh et al., (2018); Liu et al., (2018); Al-Harthi et al., (2018); Elgelany and Alghabban, (2017)) |
| 19 | Machine type priority | (Cornetta et al., (2019)) |
| 20 | Perceived ease of use | (Bhatiasevi and Naglis, (2016); Wu et al., (2013a), (2013b)) |
| 21 | Performance | (Caglar et al., (2015); Baldassarre et al., (2018); Chao et al., (2015); Islam et al., (2017)) |
| 22 | Personalisation | (Dron, (2018); Peng et al., (2019); Al-Shammari, (2014)) |
| 23 | Privacy | (Kim et al., (2016); Ahad et al., (2018); Daim et al., (2016); Li et al., (2017); Qasem et al., (2019); Elgelany and Alghabban, (2017); González-Martínez et al., (2015); Sarrab et al., (2015); Wu et al., (2013a), (2013b)) |
| 24 | QoS | (Zhang et al., (2015); Celdrán et al., (2019); Qasem et al., (2019); Bouyer and Arasteh, (2014)) |
| 25 | Quicker results | (Zhang et al., (2015); Mościcki and Mascetti, (2018)) |
| 26 | Reduced energy consumption | (Saleh et al., (2018); Liu et al., (2018)) |
| 27 | Relative advantage | (Tarhini et al., (2018); Sabi et al., (2016)) |
| 28 | Reliability | (Liu et al., (2018); Morze et al., (2015); Chao et al., (2015); Naveed et al., (2019); Qasem et al., (2019); Al-Harthi et al., (2018); Islam et al., (2017); Karim and Rampersad, (2017); Yaghmaei and Binesh, (2015); Wu et al., (2013a), (2013b)) |
| 29 | Resource pooling | (Liao et al., (2016)) |
| 30 | Scalability | (Caglar et al., (2015); Celdrán et al., (2019); Jose and Christopher, (2019); Cornetta et al., (2019); Elgelany and Alghabban, (2017); González-Martínez et al., (2015); Islam et al., (2017); Sarrab et al., (2015); Wang, (2013); Yaghmaei and Binesh, (2015); Ramachandran et al., (2014)) |
| 31 | Security | (Neuhaus et al., (2014); Stein, (2013); Sotsenko et al., (2016); Liao et al., (2016); Jose and Christopher, (2019); Saleh et al., (2018); Ahad et al., (2018); Baldassarre et al., (2018); Chao et al., (2015); Cornetta et al., (2019); Daim et al., (2016); Naveed et al., (2019); Qasem et al., (2019); Zurita, (2014); Boja et al., (2013); Bouyer and Arasteh, (2014); Elgelany and Alghabban, (2017); González-Martínez et al., (2015); Islam et al., (2017); Karim and Rampersad, (2017); Liu et al., (2018); Saini and Kaur, (2017); Wang, (2013); Yaghmaei and Binesh, (2015); Ramachandran et al., (2014); Sabi et al., (2016); Wu et al., (2013a), (2013b)) |
| 32 | Skills | (Sabi et al., (2016)) |
| 33 | Storage | (Zurita, (2014); Elgelany and Alghabban, (2017)) |
| 34 | Task type priority | (Cornetta et al., (2019)) |
| 35 | Teachers’ learning ability | (Zhu et al., (2016); Bouyer and Arasteh, (2014)) |
| 36 | Timely availability | (Ahad et al., (2018)) |
| 37 | Tool reuse | (Celdrán et al., (2019); Barve et al., (2017)) |
| 38 | Trust in provider | (Banait et al., (2015)) |
| 39 | Trust in website | (Hew and Kadir, (2016); Bhatiasevi and Naglis, (2016)) |
| 40 | User type priority | (Cornetta et al., (2019)) |
| 41 | Users’/learners’ behaviour | (Gros, (2016); Hu, (2016); Morze et al., (2015); Jou and Wang, (2013)) |
| 42 | Vendor lock | (Qasem et al., (2019); Tarhini et al., (2018); González-Martínez et al., (2015); Wu et al., (2013a), (2013b)) |
| 43 | Visual flow | (Al-Harthi et al., (2018)) |
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