Using MDD to Extend the IMS LD Standard with

Adaptability

Valérie Monfort

1,2

, Slimane Hammoudi

and Maha Khemaja

Université de Sfax, MIRACL, Sfax, Tunisie

Université de Paris 1 Panthéon Sorbonne, Sorbonne, France

ESEO 4, Rue Merlet de la Boulaye B.P. 9249 009 Angers Cedex 01, Angers, France

PRINCE Research Group, ISITC Hammam Sousse, University of Sousse, Sousse, Tunisia

Abstract. A few e-Learning platforms propose a solution for ubiquity and

context aware adaptability. Current standards, as Learning Design (LD), require

an extension to propose context awareness. Based on previous related works,

we define a fully interoperable and learner (ambient) context adaptable

platform, by using meta modeling based approach mixing MDD, parameterized

transformations, and models composition. The scope of this paper is to extend

LD meta model as a first step. We use a concrete software engineering

industrial product that was promoted by French Government.

1 Introduction

E Learning aims the delivery of a learning, training or education program by

electronic and it involves the use of a computer or electronic device (e.g. a mobile

phone), in some way, to provide training, educational or learning material.

Concerning the architecture point of view, e Learning platforms gather two separated

and distributed parts as: authoring tools (for pedagogical contents definition) and

execution platforms. So, e Learning may: i) use several media and devices, ii)

promote specific training according to learner skills, iii) send specific events to

increase complexity of lessons and to assess learner reactions, …

Previous works allowed us to use Web services to get interoperability and

flexibility to changes. But, we noticed the lack of adaptability, so, we extended Web

services to introduce adaptability with aspects [12]. We noticed this very efficient and

pragmatic solution was very technical. Recently, we have investigated a model driven

approach and context awareness to provide developers mechanisms that allow them

representing an application in abstract way (in a model) and, then, automatically

generating the corresponding code [7], [8]. We aimed to explore adaptability and

flexibility on a service platform using context with the benefits of an MDD (Model

Driven Development) [9] development strategy. These benefits are related to

productivity, quality, adaptability and maintenance.

Moreover, e Learning standards tend to extend their semantic to Web services

standards [4]. We studied e-Learning standards metamodels, but, we noticed no

semantics concerning context aware adaptability.

Monfort V., Hammoudi S. and Khemaja M.

Using MDD to Extend the IMS LD Standard with Adaptability.

DOI: 10.5220/0003025600800086

In Proceedings of the 2nd International Workshop on Future Trends of Model-Driven Development (ICEIS 2010), page

ISBN: 978-989-8425-10-2

We aim to propose a fully interoperable and learner (ambient) context adaptable

platform. This paper studies e-Learning standard semantics to introduce context

awareness metamodels. We focus here on Learning Design (LD) that helps to define a

pedagogical scenario with its components (roles, activities, environment, and

outcomes). LD proposes an entity for context in its metamodel but, according to us,

this approach is too semantically poor.

We shall process as followed. Second section presents context and context

awareness, aspect based services models and context aware metamodel. Third section

discusses an extension of LD metamodel with models composition and a concrete

industrial software engineering project. Fourth section presents some related works.

Let us define now context awareness.

2 Context Aware Modelling

2.1 Context and Context Awareness

Context awareness is a quite new discipline in e Learning domain. For instance, in [6]

[11], the authors noticed the context acts like a set of constraints that influence the

behavior of a system (a user or a computer) embedded in a given task. They discussed

the nature and structure of context but they notice the lack of representation of context

in e Learning domain. The emergence of new technologies, in particular wireless

communications and the increasing use of portable devices (smart phones, Personal

Digital Assistants(PDA), laptops…), has stimulated the emergence of a new

computing paradigm called: pervasive computing. In fact we have moved from the

desktop computing paradigm to the mobile and ubiquitous computing paradigm.

Pervasive computing refers to the seamless integration of devices into the user’s

everyday life. “Appliances should disappear into the background to make the user

and his tasks the central focus rather than computing devices and technical

issues.”[13]. Computing applications now operate in a variety of new settings; for

example, embedded in cars or wearable devices. They use information about their

context to respond and adapt to changes in the computing environment. They are, in

short, increasingly context aware. The context awareness of such applications is the

subject of a recent field of studies in pervasive computing called: context-aware

systems. This terminology was discussed in [10] and presented as “software that

adapts according to its location of use, the collection of nearby people and objects, as

well as changes to those objects over time”. Since then, there have been numerous

attempts to define context-aware computing. In [10], [11], they define context-

awareness as the ability of a program or device to sense or capture various states of its

environment and itself. Referring to these latter definitions a context-aware

application must have the ability to capture the necessary contextual entities from its

environment, use them to adapt its behavior (run time environment) and finally

present available services to the user. In this sense and to describe context-awareness

independently from application, function, or interface, [11] proposes four features of

context-aware application : (1) Contextual sensing which refers to the detection of

environmental states and their presentation to the user; (2) Contextual adaptation

refers to the adaptation of application behavior to the current context; (3) Contextual

resource discovery is the use of context data to discover other resources within the

same context; (4) Contextual augmentation in which the environment is augmented

with digital data associated to a particular context. In [2], [3], the authors introduce

another definition in which they insist on the use of context and the relevance of

context information. The authors consider that: “a system is context-aware if it uses

context to provide relevant information and/or services to the user, where relevance

depends on the user’s task”. They explain how to use context and propose a

classification of the features of context-aware applications that combine the ideas of

[10].

2.2 Model Driven Development (MDD)

At the beginning of this century, software engineering needs to handle software

systems that are becoming larger and more complex than before. Object-oriented and

component technology seem insufficient to provide satisfactory solutions to support

the development and maintenance of these systems. To adapt to this new context,

software engineering has applied an old paradigm, i.e. models, but with a new

approach, i.e. Model Driven Development (MDD). In this new global trend, Model

Driven Architecture (MDA) is a particular variant. MDA is based on standards from

the Object Management Group (OMG) [9]; it proposes an architecture with four

layers: meta metamodel, metamodel, model and information (i.e. an implementation

of its model). MOF (Meta Object Facility) is a standard from OMG for metamodels

specification. The development is based on the separation of concerns (e.g. business

and technical concerns), which are afterwards transformed between them. So,

business concerns are represented using Platform-Independent Model (PIM), and

technical concerns are represented using Platform-Specific Model (PSM). Finally, it

is well recognized nowadays that model transformation is one of the most important

operations in MDA. In the context of the basic four levels Metamodeling architecture

of MDA, various scenarios of model-to-model transformation have been identified.

The most common scenario of these transformations, which is compatible with the

MOF2.0/QVT standard includes the following elements. Transformation rules specify

how to generate a target model (i.e. PSM) from a source model (i.e. PIM). To

transform a given model into another model, the transformation rules map the source

into the target metamodel. The transformation rules are based on a transformation

language, such as the standard QVT. The transformation engine takes the source

model, executes the transformation rules, and produces the target model as output.

Adaptable Service platforms have been proposed for the development of mobile

context-aware applications. The development of such platforms involves a number of

challenges from which we consider two main issues in the context of our approach of

model driven development:

• The definition of a metamodel to describe the contextual domain in which a given

application or service is defined.

• A mechanism to integrate the context into the business application using a model

driven approach.

In [7], [8] we have discussed these two main issues. We have defined a context

metamodel which identifies and adds the most relevant and generic contextual entities

that will be held in account in modelling any mobile and context aware application.

We have then proposed a parameterized transformation technique which allows

merging context information with business logic at model level. We have investigated

this type of transformation which is not explored and there is not a standard

transformation language implementing it.

3 Extension of LD to Context Awareness

3.1 LD Model

A Learning Design (LD) is a description of a method enabling learners to achieve

intended learning objectives and outcomes by performing predefined learning

activities. More specifically, a learning design is a means allowing the Instructional

designer to describe a learning scenario in terms of a set of activities that learners

should perform according to the different roles that they may play within

environments (i.e Run-time environment). Environments are described in terms of

Learning Objects and Services that should assist learners during the Learning process.

IMS-LD (Instructional Management Systems-Learning Design) [1], [5] specification

provides for previously described concepts a meta-model (Figure 1) that was and is

still used by LD authoring tools developers. According to IMS-LD specification, LD

concepts must meet height requirements. We name the third one because it deals with

personalization that is relevant to our work. The LD specification states that: “The

content and activities within a unit of learning can be adapted based on the

preferences, portfolio, pre-knowledge, educational needs, and situational

circumstances of users. In addition, the control over the adaptation process must be

given, as desired, to the student, a staff member, the computer, and/or the designer”.

Fig. 1. The LD Specification.

However, IMS-LD provides neither means nor modeling solutions to take into

account contextual data of mobile users for instance. We should stress that the

contextual data, unlike those already defined for personalization, are dynamic and

may depend on the user’s external environment. We propose to extend this model

with our context metamodel with composition mechanisms according to [14], [15] .

Fig. 2. Generic BPMN Process of the training.

3.2 Illustrative example and Model Composition

We worked for French government to implement a navigation and fishing e learning

application. We proposed following metamodels coming from our research works.

The application aims to train different kind of learners as: young students coming

from fishery schools and adults working for fishery companies. In fine, the aim of the

system is to train learners to be: fishery captain, fishing boat mechanic, sailor, port

manager,…

During course, learner according to his skills receives a navigation and/or fishing

scenario as “ go to 100 miles from Saint Jean de Luz and fish tuna”. So, the learner

has to do obligatory tasks as: to check weather, to define the road, to check the fitted

nets, to check mechanic,… The generic BPMN (Business Process Modeling) [16]

process shows (Figure 2) the different tasks to do by the learner and the teacher. The

teacher programs a course that will be received by the learner anywhere he is, via any

media, … after identifying himself. The learner is assessed in real time and the

teacher may send him events. At the end of the module, the diploma is delivered or

not. The learner is in front of his laptop and receives the training. All the navigation

tools (radar, sounder, GPS,…) are simulated. According to his skills, the teacher

(human or system) can send to the learner desktop specific events as mist, rain,… and

the learner has to react properly. Moreover, the system provides an estimation of

learner skills in real time. The resulting composed model is formed by: i) training

metamodel (that could be later formatted to e-learning standards), ii) a contextual

model that was already composed to component class from LD. Another composition

may also be done with fishery business metamodel. For each training module, a link

may be done with specific business data. For instance, a training module about tuna

fishery involves the choice of the fitted net. A mark is put on the required classes.

4 Conclusions

Other approaches aims to use metamodeling: i) to define e Learning interoperable and

platforms independent system ii) and to extend standards as [1], [4], [5]. Some

researchers introduce adaptability with Multi Agent System but we choose an hybrid

approach based on software engineering and Artificial Intelligence. Previous works as

[7], [8], propose solutions to model context. We use these approaches to extend them

to eLearning according to our choices. We did not find any concrete and relevant

related works concerning such an approach in e-Learning domain, but we are

convinced our approach is pertinent because we got good results with fishing

simulators and in other Web based application domains.

This paper proposes a metamodel approach to introduce (ambient) context

awareness in LD model. It is based on our previous works about adaptability and

models composition based MDD. We propose examples coming from a concrete

industrial project. We aim: i) to define an independent platform model based on

services, ii) to implement models transformations to link these models to

implementation platform, iii)to promote automatic code generation… We propose

now transformation rules via a technical platform based on services and supporting

context awareness.

References

1. Boticario, J. Olga. C Santos: An open IMS-based user modeling approach for developing

adaptive Learning managements systems. Retrieved from

www-jime.open.ac.uk/2007/02/.

2. Chen, G., and Kotz, D. (2000). A survey of context-aware mobile computing research.

Tech. rep., Dept. of Computer Science, Dartmouth College.

3. Dey, A. K. (2001). Understanding and Using Context. Personal and Ubiquitous Computing

5, 1, 4-7.

4. Dietze Stefan, Alessio Gugliotta, and John Marko Rosić Vlado Glavinić Branko Žitko:

Intelligent authoring shell based on Web services :

http://www.pmfst.hr/~bzitko/radovi/files/INES2004.pdf

5. Davinia Hernandez Leo, Juan I. Asensio Perez, Yannis A. Dimitriadis, "IMS Learning

Design Support for the Formalization of Collaborative Learning Patterns" Advanced

Learning Technologies, IEEE International Conference on, pp. 350-354, Fourth IEEE

International Conference on Advanced Learning Technologies (ICALT'04), 2004.

6. Mary, B., and Patrick, B. (2005). Understanding context before to use it. In 5th

International and Interdisciplinary Conference on Modeling and Using Context vol. 3554 of

Lectures Notes in Artificial Intelligence, Springer-Verlag, pp. 29-40.

7. V. Monfort, S. Hammoudi, When Parameterized MDD Supports Aspect Based SOA ,

IJEBR 2010, International Journal of E-Business Research (To appear).

8. V. Monfort, S. Hammoudi, ICSOC, Towards Adaptable SOA: Model Driven Development,

Context and Aspect The 7th International Conference on Service Oriented Computing,

November 23-27 2009, Stockholm, Sweden.

9. OMG (Object Management Group). (2001). Model Driven Architecture (MDA), OMG

document number ormsc/2001-07-01.

10. Schilit, B. N., and Theimer, M. (1994). Disseminating active map information to mobile

hosts. IEEE Network 8 22-32.

11. Strang, T., and Linnhoff-Popien, C. (2004). A Context Modeling Survey. In First

International Workshop on Advanced Context Modelling, Reasoning and Management,

UbiComp.

12. Tomaz, R. F., Hmida, M. B., and Monfort, V. (2006). Concrete solutions for web services

adaptability using policies and aspects. The International Journal of Cooperative

Information Systems (IJCIS), 15(3):415– 438.

13. Weiser, M. (1991). The computer for the 21st century. Scientific American. PP 94-104.

14. J. Klein, L. Hélouet, and J. M. Jézéquel. -- Semantic-based weaving of scenarios. -- In

Proceedings of the 5th International Conference on Aspect-Oriented Software Development

(AOSD'06), Bonn, Germany, March 2006. ACM

15. Sten Lundesgaard, Arnor Solberg, Jon Oldevik, Robert France, Jan Oyvind Aagedal, Frank

Eliassen, Construction and Execution of Adaptable Applications Using an Aspect- Oriented

and Model Driven Approach, IFIP DAIS 2007, LNCS 4531, 76-89, 2007.

16. BPMN specification web site retrieved from http://www.bpmn.org/