eLSaaS: A FRAMEWORK FOR MOBILE LEARNING CONTENT

ADAPTATION

Ivan Madjarov and Omar Boucelma

Aix-Marseille Univ, LSIS, Avenue Escadrille Normandie-Niemen, 13397 Marseille, France

Keywords:

Cloud-based Content Adaptation, m-Learning, Web Services, Software as a Service.

Abstract:

As a typical Internet application, an m-learning system is an innovative approach for delivering well-designed,

learner-centred, interactive and facilitated learning environments to anyone, anywhere at any time. Cloud

computing is a technology and a business model that allows access to an extensible set of storage and com-

puting facilities with a provision and pay as you go model. This position paper discusses the problem of

content adaptation for mobile devices in a cloud context. A device independent model is presented in order

to achieve automatic adaptation of the content based on its semantics and the capabilities of the target device.

An m-learning software as a service cloud framework is presented for adapting, displaying and manipulating

learning documents on smart devices. Some aspects of services integration in the cloud for m-learning are

also discussed.

1 INTRODUCTION

Cloud computing is a technology and business model

that follows a provision/pay-as-you go model for

the delivery of computing resources. With the vast

amount of available resources (data and services), the

cloud paradigm together with mobile computing en-

ables services that are scalable on demand and im-

plemented on virtualized resources over the Inter-

net (Keng, 2011). Software-as-a-Service (SaaS) is

cloud service layer which delivers a single applica-

tion through the Web browser to thousands of desktop

or mobile learners. Mobile learning (m-learning) is

a time constrained activity performed usually on-the-

ﬂy. Mobile technologies tent to restrict signiﬁcantly

presentation features.

Most learning resources already in use in desktop-

based learning course management systems (LCMS)

cannot be simply ported to mobile devices. Hence,

this necessary to develop context-aware learning tools

for mobile environments. In this paper, we advocate

an e-learning-software-as-a-service (eLSaaS) model

to handle scalability over distributed learning content.

The objective is to provide accessibility over a wide

variety of mobile devices.

We believe that a new cloud-based e-learning sys-

tem should comprise a set of independent but coop-

erating non-monolithic Web services-based applica-

tions that integrate pedagogical data between com-

mon LCMS. For instance, in opposition to the re-

stricted LMS learner’s access, a content adaptation

method can be developed for cloud users. Our claim

is to make a device-independent m-learning SaaS

gateway between different mobile devices and the

plethora of learning objects (LOs) (LOM, 2002) that

are available on various LCMS.

The rest of this paper is organized as follows. Sec-

tion 2 surveys some related work, and in Section 3

the cloud-based approach for m-learning systems is

discussed. Section 4 details our SaaS solution for m-

learning as well as design issues and implementation

details. Conclusion and future work are presented in

Section 5.

2 RELATED WORK

Currently there are on-going projects that propose the

usage of cloud computing as a basis for modern e-

learning applications and systems. As an example, the

CloudIA project (Doelitzscher, 2011) demonstrates

the feasibility of a private cloud infrastructure for e-

learning. This project addresses functionalities for

enabling an e-learning system in the cloud, such as

authentication and integration with existing IT infras-

tructures, and the creation of customized on-demand

virtual machines. The authors make a choice for pri-

vate resources. However, their choice is not compared

276

Madjarov I. and Boucelma O..

eLSaaS: A FRAMEWORK FOR MOBILE LEARNING CONTENT ADAPTATION.

DOI: 10.5220/0003958102760281

In Proceedings of the 4th International Conference on Computer Supported Education (CSEDU-2012), pages 276-281

ISBN: 978-989-8565-06-8

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

with the features and capabilities of those available in

the public domain.

A similar architectural system has been adopted

in BlueSky (Dong, 2009). This cloud framework en-

ables physical machines to be virtualized and allo-

cated on demand for e-learning systems. However,

the security layer for this cloud-based framework is

not addressed.

In Casquero (2008) and Al-Zoube (2009) simi-

lar cloud frameworks, based on iGoogle with gadgets

related to Google Apps infrastructure for the devel-

opment of a corporative e-learning network, are pre-

sented. The authors discuss the integration of insti-

tutional and external services in order to provide a

personalized support. Google App Engine provides a

Java Web framework (Jetty), a servlet container, and

BigTable for data storage. However, the process of

data and application integration with Google Apps is

not covered by the authors.

In Madjarov (2010) features of a personalized m-

learning approach are presented including context-

based adaptation and portability of LOs on several

mobile Web browsers. Also discussed are the hierar-

chical display of multimedia units with index extrac-

tion and content summarization.

Finally, in Madjarov (2011), a service-based so-

lution is described that overcomes the limitations of

mobile devices in combining textual content adapta-

tion with alternate audio transcoding to better fulﬁl

student needs.

3 CLOUD-BASED e-LEARNING

3.1 Cloud Computing Architecture

The cloud can be seen as a unique access point for

all the requests coming from the world wide spread

clients. Cloud computing provides dynamically scal-

able infrastructure supplying computation, storage

and communication capabilities as services (Hossain,

2011). In this infrastructure the coupling between re-

sources and applications is facilitated. Cloud com-

puting is the promising infrastructure which can pro-

vide information and application interoperability to

e-learning systems. Figure 1 shows how to build e-

learning systems through cloud services in a simple

way.

Cloud computing comprises three layers as pre-

sented in Figure 2 (Creeger, 2009):

1. Infrastructure as a Service (IaaS);

2. Platform as a Service (PaaS);

3. Software as a Service (SaaS).

Figure 1: Cloud computing and e-learning cloud-based ar-

chitecture.

Figure 2: e-Learning cloud architecture layers.

3.1.1 Infrastructure as a Service (IaaS)

IT infrastructure is deployed in a provider’s data cen-

tre as virtual machines, i.e., the virtualized hardware

resources are deployed as a service. IaaS comprises

the layer of storage, hardware, servers and network-

ing components. Architecture scalability is achieved

through virtualization, such that multiple systems or

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operating systems can be run at the same time on a

virtual machine or across multiple machines. A user

can access the server, network and storage equipment,

only through Internet. Also, he or she can install one’s

own application system.

3.1.2 Platform as a Service (PaaS)

The concept of PaaS remains invisible to the user as it

provides the necessary operating platforms for the vir-

tually provided applications (hardware and software

as service). It offers an application platform for the

Internet programming interface and the operating en-

vironment, in which users can structure and deploy

the domain speciﬁc applications. PaaS allows soft-

ware and service development without downloading

tools and software to client machines. Using the PaaS

concept, large and complicated software packages can

be developed, tested and disseminated. Thus, the

PaaS concept supports virtualization and scalability.

3.1.3 Software as a Service (SaaS)

This is a software delivery model in which software

and its associated data are hosted centrally and are ac-

cessed by users using a Web browserover the Internet.

Web 2.0 is the main technology behind the realization

of SaaS (Cortez, 2010). SaaS has become a common

deliverymodel for most business applications, includ-

ing accounting, customer relationship management

(CRM), enterprise resource planning (ERP), and re-

cently e-learning LCMS. Clients access software ser-

vices such as email, word processing, spreadsheets,

quizzes, exercises, simulations, etc. from the cloud

instead of running these applications directly on their

client computers.

3.2 Cloud e-Learning

There is a growing interest in cloud computing for e-

learning practitioners. Greater connectivity between

centralized cloud-side applications, in combination

with low cost, and low processor capacity of mo-

bile devices could provide better access, more con-

trol, and greater freedom for mobile learners. At the

same time, mobile devices signiﬁcantly differ from

each other in their characteristics. An ultimate chal-

lenge facing m-learning is the creation of pedagogical

learning models to handle the speciﬁcity of mobile

pedagogical processes and the inherent constraints of

mobile devices (Keng, 2011). To overcome the limita-

tions of lightweight devices, distributed client-server

architecture using Web services can be employed. To

handle scalability over large-sized learning content

sources, and also to provide accessibility over wide

variety of mobile devices, a SaaS m-learning model

can be deployed. In a cloud infrastructure, Web pages

are generally designed for desktop screens making it

difﬁcult to visualize on mobile phones. To overcome

this constraint a scalable adaptation process for a wide

variety of mobile units is needed.

The SaaS approach is perfect for e-learning and

m-learning because it can be implemented quickly

and it is easy to maintain. Thus, clients can receive

the latest updates and features without any extra ﬁnan-

cial obligation. Another advantage with SaaS is that

it helps authors to share pedagogical resources with a

simple ”click”, using Web 2.0 technology. All aspects

of an e-learning or m-learning solution can be deliv-

ered using the SaaS model, including LMS, LCMS,

courseware content, authoring tools, and synchronous

collaboration tools like webcasting and white board-

ing (Basal, 2010). To visualize this relationship, refer

to the service layer in Figure 2.

4 e-LEARNING SOFTWARE AS A

SERVICE (eLSaaS)

This section presents a solution for building a virtual

and personalized learning environment which utilizes

a cloud-based technology to create a service-oriented

model for m-learning application service providers

and learners. The concept of eLSaaS has introduced

as a software distribution model in which applications

are hosted by a service provider and distributed via the

Web. Our contribution is as follows:

• demonstration of a Web service-based architec-

ture to an integrated Web-based learning and m-

learning environment;

• design of a service-based framework, as part of

an e-learning SaaS cloud, that uses hierarchical

displaying multimedia units with index extraction

and content summarization;

• description of a SaaS-based e-learning system ar-

chitecture to provide a ﬂexible integration model

in which all the learning components and applica-

tions are well deﬁned and loosely connected;

• deployment of multimedia services and especially

the presentation of multimedia content on mobile

environments.

One major drawback of existing e-learning sys-

tems is that they are content-centric. Many course au-

thors simply move all their learning materials to the

LMS. The pedagogical materials are presented uni-

formly to all learners regardless of their background,

learning styles and preferences. In the same time,

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some efforts of content providers and course instruc-

tors in pedagogical content organization in function

of LMS’s branching capabilities helps to overcome

partially these constraints. Despite all this nowadays,

we need a suitable context-aware pedagogical content

adapted to learning preferences, proﬁles and require-

ments, i.e., a learner-centric content.

In the proposed eLSaaS environment we can eas-

ily combine semi-structured data, stored in a Native

XML Database (NXDB), with structured data stored

in a Relational Database (RDB) through Web ser-

vices (WS). The objective is to provide direct data

and application integration, located at distributed sites

in order to improve the achievement of learning out-

comes. This approach promotes a device-independent

m-learning gateway between different mobile units

and the huge number of learning resources available

on a plethora of LMSs. It becomes possible by com-

bining our Web-based Open Semantic Editor Suite

(WOSES) (Madjarov, 2010) with a set of additional

services to allow different mobile units a direct ac-

cess to LOs customarily designed for desktop Web

browsers. A semantic content adaptation service is

plugged for content standardization. This tool uses

templates to automatically and efﬁciently adapt con-

tent for mobile Web browsers. An alternative service

is available for a speech solution, which allows learn-

ers to turn any written text into natural speech ﬁles,

when using standard voices. This approach allows the

generation and the progressive downloading of text

and audio based learning material dynamically for m-

learning and ubiquitous access.

4.1 m-Learning Content Model

4.1.1 m-Learning Advantages and Challenges

M-Learning focuses on the mobility of the learner,

interacting with portable technologies, and presents

several advantages:

• Ability to access learning everywhere and any-

time e.g. down time can be leveraged for learning;

• Ability to access learning at the point of need;

• Flexibility for mobile development as video, pre-

sentations, podcasts, and quizzes are all potential

outputs to mobile devices;

• Creativity with a huge potential for location-based

and context-based learning.

At the same time, we can list several challenges

(disadvantages) of m-learning delivery and develop-

ment:

• The choice of a mobile device which can techno-

logically meet learners needs best is often difﬁcult

and expensive;

• The course interface size and richness seems criti-

cal to the engagementfactor of a learner in a learn-

ing;

• Mobile device potentially is never disconnected

and ensures permanent access to an abundance

of information that requires time for incubation,

critical thinking, and reﬂection for learning. This

can be critical for the learner’s success due to the

”fast” (mobile) learning;

• Challenges of initial cost threshold of mobile ap-

plications development.

As SaaS is delivered over the Internet through

Web browsers, in our approach to overcome some

m-learning disadvantages and to produce device-

independent Web-accessible information that can be

browsed in a readable and effective way on different

smart devices and software platforms we use mobile

Web browser and methods for effective mobile device

recognition, and mobile Web browsers functionalities

identiﬁcation.

4.1.2 m-Learning Multimodal Portability

For an effective Mobile Device Recognition Method

(MDRM) we use the header ﬁeld in the HTTP proto-

col. To prove the multimodal portability on mobile

browsers we conducted a series of tests that repre-

sent some of the common design types that are in use,

and like most real Web pages, not all of them are de-

signed to work with small screens. We tested several

mobile Web browsers on different models of PDAs,

smartphones and cell phones in order to identify their

compatibility with desktop Web browsers. Analysis

of the test results (Madjarov 2010) showed that multi-

media pedagogical content is suitable for mobile Web

browsers such as Opera Mobile, Safari, and Firefox

Mobile. The main problem to address is to how to tai-

lor the multimedia presentation for the small-screen

of a mobile devices. Thus, the main problem is not

focused on the complexity of pedagogical hyperme-

dia content.

4.1.3 m-Learning Mash-up

Another way to mitigate the previously cited disad-

vantages consists of a ﬂexible architecture enabling

learners to mash-up heterogeneous set of services that

support different learning activities such as produc-

tion, distribution, reﬂection, and discussion. A mash-

up is an application that uses, combines and aggre-

gates data or functionality from more than one source

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to create a new service. The idea is that combining

various applications into one will help to present data

in a more useful way to meet the m-learning spe-

ciﬁc needs. These applications are usually hosted

in the cloud and diffused as Web services. Mash-

ups can be a very effective approach to overcome

challenges of distributed services and to solve prob-

lems related to devices heterogeneity. Using mash-

ups in an m-learning environment can help to con-

nect resources and applications in one environment

customized to the needs of suitable Web service-

based content-adaptationtechnique that we developed

(Madjarov, 2011). On the other hand, application

mash-ups can be achieved by implementing the appli-

cations as widgets (Al-Zoube, 2009). This portable

application is typically implemented using HTML

5.0, JavaScript and CSS3, and can be run on a wide

range of platforms and recent versions of mobile Web

browsers as showed by our tests. A widget performs

a speciﬁc function, usually obtaining content from

one website and displays it on another website (W3C,

2011). In this case, we suggest the use of Web ser-

vices technologies that can be involved through the

Ajax technique to manage the client-server commu-

nication asynchronously. In our concept, we recom-

mend the use of a thin mobile client instead of heavy

clients installed in smart devices for client-to-cloud

communication.

4.1.4 m-Learning Content Presentation

The device context presents a decisive factor for ap-

propriate presentation of multimedia learning content

in connection with m-learning. Besides the choice of

learning content and appropriate methods of interac-

tion, the input-output modality plays a central role for

optimal use of m-learning in different context, scenar-

ios and situations. M-learning content can be given

in the form of a visual presentation as text, pictures,

and tables or can be given as sound data in the form

of an acoustic presentation. The speech synthesis or

text-to-speech (TTS) combines previously-recorded

words, or produced synthetically by linking the small-

est linguistic units. In order to create a naturally-

sounding result, one must consider the length and tone

of the individual components. To build the speech-

production service in our eLSaaS framework, we in-

voked the proper SOAP methods that will enable the

Web service to send text and generate speech ﬁles on

the Cloud server.

4.2 Implementation Scenario

Figure 3 highlights the WOSES cloud-based applica-

tion integration with a Web-based LCMS. The inter-

connection is carried out by a Web Services Manage-

ment System (WSMS). As illustrated, the learning-

centric data and the management-centric data are

clearly separated. Pedagogical documents are devel-

oped in the WOSES section of the eLSaaS-based Xe-

sop system and thereafter are stored in a NXDB. The

information relevant to learner personal data, learner

proﬁles, course maps, LOs sequencing, data presen-

tation and general user data is stored in the RDB of

LCMS. The publication process of learning content

is carried out by WSMS. This allows integration into

existing LCMS systems as a cloud-based service. In

the discussed case, Web service-based content mod-

ules make the bridge from e-learning to m-learning

system in a simple and effective way through Apache

Libcloud (Apache, 2011), an open source library that

provides a system-neutral interface to cloud provider

APIs. The Java version supports Amazon EC2.

Figure 3: e-Learning software as a service solution.

For system deployment, we used eCUME

(Moodle-based) e-Learning system deployed at Aix-

Marseille University (eCUME, 2012). Our system is

based on the Apache containers suite for data stor-

age and service management. We integrated the PHP-

based LCMS interface via Web services. For ser-

vices deployment we used Apache Axis. For stor-

ing and managing LOs, we used eXist (Native XML

database) running in the Apache Tomcat Servlet en-

gine as a Web application and invoked via REST-

style Web services API. To integrate with other e-

learning and/or m-learning systems we implemented

an Apache jUDDI registry.

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5 CONCLUSIONS

In this paper, we advocate and illustrate a cloud-based

m-learning solution that may leverage both existing e-

learning services, cloud computing provision/pay-as-

you-go models and mobile devices. This solution is

clearly needed because:

• Many mobile applications are based on cloud ser-

vices such as location service and messaging ser-

vice;

• Cloud services promote the use of mobile Web

browsers instead of mobile applications since the

cell phones are not powerful or fast enough;

• SaaS enables a speech-productionservice where a

method sends a text and generates speech ﬁles on

the speech cloud server.

The solution we propose shows clearly how to

combine existing individual systems into a virtual

one, availableas a SaaS unit. We receivedpositiveini-

tial feedback from users who tested our system. Our

approach is consistent with cloud-based solutions that

are being proposed for LMS such as Moodle.

For future work, we plan to use mash-ups in a

context-awarecontent adapted interface to connect re-

sources and applications that are customized to the

needs of individual users. We also plan to work on

different scenarios where it will be possible to ex-

change both data and applications among different

systems.

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