A PARADIGM FOR THE APPLICATION OF CLOUD
COMPUTING IN MOBILE INTELLIGENT TUTORING
Hossein Movafegh Ghadirli1 and Maryam Rastgarpour2
1 Graduate student in Computer Engineering, Young Researchers Club, Islamshahr
Branch, Islamic Azad University, Islamshahr, Iran
2 Faculty of Computer Engineering, Department of Computer, Saveh
Branch, Islamic Azad University, Saveh, Iran
Nowadays, with the rapid growth of cloud computing, many industries are going to move their computing
activities to clouds. Researchers of virtual learning are also looking for the ways to use clouds through
mobile platforms. This paper offers a model to accompany the benefits of “Mobile Intelligent Learning”
technology and “Cloud Computing”. The architecture of purposed system is based on multi-layer
architecture of Mobile Cloud Computing. Despite the existing challenges, the system has increased the
life of mobile device battery. It will raise working memory capacity and processing capacity of the
educational system in addition to the greater advantage of the educational system. The proposed system
allows the users to enjoy an intelligent learning every-time and every-where, reduces training costs and
hardware dependency, and increases consistency, efficiency, and data reliability.
Mobile Services, Cloud Computing, Mobile Intelligent Learning, Expert System
Nowadays growth of technology is fast and unpredictable in the economy, industry and personal
issues . One of the aspects of social life is the process of learning in universities, schools and
other educational institutions. Extensive researches and huge investments have been carried out
to develop technological learning in recent years. Now the word “Learning” is accompanied
with the concepts such as Electronic, Cognitive, Intelligent, Distance and Web based. Since one
of the attractive, efficient and widely used technologies is the use of mobile devices to do the
tasks, researchers have tried to replace the previous notions with mobile learning. They develop
educational softwares that can be implemented on mobile devices.
Mobile learning means the use of learning applications on mobile devices such as smart phones,
PDA and tablets (unlike mobile devices which are small, portable, compact and pocket sized,
Laptops are not considered as mobile systems, since they are expensive and heavy and they
consume much energy) . Recent researches indicate that the variety of learners, the training
and learning process and infrastructure changes to subscribers, in addition to significant impact
on learning quality, is more motivating learners. It causes wider interest of investors toward
At the present, mobile devices are increasing rapidly, since they are the easiest and the most
effective communication tools. In addition, their crucial role in human life, when and where to
use them are not restricted (called ETEW1) , , . Mobile users can use different
applications on their devices or receive even different kinds of services through wireless
With increasing propagation of mobile devices technology, the popularity of this device has also
increased. Some features such as mobility, optimized and easy to use are of the benefits of
mobile devices. Nevertheless, the challenges of the resources of mobile devices (such as short
battery life, small memory capacity and low bandwidth) and also of communication (such as
mobility and data security) are the reasons for the decrease of service quality.
Cloud computing has been known as the Infrastructure of the next generation . Cloud
computing provides users with a way to share distributed resources and services of
organizations in a cloud, and a platform and software is provided as a service in that
infrastructure . Cloud computing can present benefits for the users in the use of the
infrastructures (such as servers, networks and storages), platform (such as firm-wares and
operating systems), and softwares (such as applications) with a little cost. In addition, cloud
computing providers (such as Google, Amazon, IBM, Sun Microsystems, Microsoft, IBM, and
Sales-force) can use their resources flexibly, depending on the demands of the users .
Many educational institutions such as universities and schools would like to use software that
can be hosted on the cloud; since it allows the final user (such as the softwares on his/her PC)
needs no License, installation and maintenance of the softwares , . In this regard, some
cloud providers like Amazon, Google, Yahoo, Microsoft, etc. also support free hosting of e-
learning systems . Thus, this paper tries to present a paradigm for the application of cloud
computing in mobile intelligent tutoring systems.
1.1. Related Works
In 2009 a system was introduced that provided private and virtual education for learners with
regard to pedagogical rules . But researchers were to transfer the complicate educational
systems from PCs to mobile devices. The benefits of cloud computing and mobile learning
integration have been pointed out in , one of which is increasing the quality of
communication between the learner and the teacher. But they are mentioned in detail in section
Some mobile applications already extract and aggregate information from multiple phones.
Tweetie Atebits for the iPhone uses locations from other phones running the application to
allow users to see recent Twitter posts by nearby users . Video and photo publishing
applications such as YouTube and Flickr allow users to upload multimedia data to share online.
The Ocarina application Smule for the iPhone allows users to listen to songs played by other
users of the application, displaying the location of each user on a globe. Such smartphone
applications are “push”-based and centralized, meaning that users push their information to a
remote server where it is processed and shared .
Cornucopia is one of the implemented examples of the proposed system, designed for the
research affairs of undergraduate Genetic learners, and Plantations Pathfinder which was also
designed to provide information for them, qua farms and gardens information were shown on
mobile devices for visitors .
Another example of the system was presented in  that teaches some courses on image/video
processing; using a mobile phone, learners are able to compare a variety of algorithms such as
deblurring, denoising, face detection and image enhancement used in mobile applications.
The rest of this paper is organized as follows. Section 2 investigates mobile intelligent learning
systems and its challenges. It also explains cloud computing and its derivative namely mobile
1 Every Time and Every Where
E. Vartiainen, KV-V Mattila, “User experience of mobile photo sharing in the cloud,” in
Proceedings of the 9th International Conference on Mobile and Ubiquitous Multimedia (MUM),
Z. Shen, Q. Tong, “The Security of Cloud Computing System enabled by Trusted Computing
Technology”, In Proceedings of International Conference on Signal Processing Systems
D. Sun, et al., “Enhancing Security by System-Level Virtualization in Cloud Computing
Environments,” Intelligent Computing and Information Science, 2011.
Hossein Movafegh Ghadirli received his B.S. in Computer Engineering from Saveh
branch, Islamic Azad University (IAU), Saveh, Iran in 2009 and He is currently a
graduate student in Computer Engineering at Science and Research branch, IAU,
Saveh, Iran. His overriding interest has been bringing E-Learning, M-Learning and
Intelligent Tutoring Systems to improve their productivity for both government and
commercial organizations. He is a member of Young Researchers Club, Islamshahr
Branch, Islamic Azad University, Islamshahr, Iran.
Maryam Rastgarpour received her B.S. in Computer Engineering from Kharazmi
University, Tehran, Iran in 2003, and the M.S. in Computer Engineering from Science
and Research branch, Islamic Azad University (IAU) , Tehran, Iran in 2007.She is
currently a Ph.D. candidate in AI there. She is also a lecturer at Computer
Department, Faculty of Engineering, Saveh branch, IAU for graduate and
undergraduate students. Her research interests include in the areas of Machine
Learning, Pattern Recognition, Expert Systems, E-Learning, Machine Vision,
specifically in image segmentation and Intelligent Tutor System.