1. Complex semantics. To use an LTS, an instructional designer must learn its syntax and semantics and must adapt his or her modeling practices accordingly. For example, IMS Learning Design (IMS-LD) provides a language based on a theatrical metaphor, within which it is possible to describe the structure of activities and tasks, the assignment of roles and the workflow of a unit of learning as a “learning design” [ 19]. Each learning scenario modeled with IMS-LD must be defined according to the theatrical metaphor (play, act, role-part, etc.).
2. Generic concern. In order to cover a wide range of needs, standards such as IMS-LD and SCORM are generic. In consequence, they are shallower and allow only a superficial modeling of needs. They are characterized by a lack of pedagogic and contextual expressiveness:
- Pedagogic expressiveness is fundamental for instructional designers because it expresses how adequately the domain represents and how comfortably instructional designers can express their ideas.
- Contextual expressiveness is also fundamental because it allows outputting the adequate TEL system models with regard to context.
3. LTS-compliance required. A major inconvenience of standards is related to the fact that designers cannot choose freely since they must use an LMS compliant with the standard used for modeling. For example, SCORM content can be interpreted only by a SCORM-compliant LMS; i.e., an LMS which implements the SCORM Run Time Environment.
2.2.1 MDA Basis According to the Object Management Group (OMG) [ 28], Model Driven Engineering (MDE) [ 21], [ 22] is a specific approach to software engineering that defines a theoretical framework for generating a code from models using successive model transformations.
The main goal of this approach is to separate the business side of a system from its implementation. The business model of a system can therefore drive its implementations on different platforms. In this way, we can expect to obtain better coherence between implementation and interoperability.
The best-known MDE initiative is the Model Driven Architecture proposed by the OMG [ 22].
MDA states that it models the environment and the requirements for a system in a Computational Independent Model (CIM).
A CIM does not show the details of system structure. Thus, a CIM can be used to build a Platform Independent Model (PIM). A PIM focuses on the operation of the system while hiding details related to the use of a particular platform. A PIM maintains platform independence in order to be suitable for use with different platforms. The transformation of a PIM into a Platform Specific Model (PSM) is based on the associated Platform Model (PM).
A PSM is a system model for a specific platform. It combines PIM specifications with the details that specify how that system uses a particular platform.
Fig. 1 shows the main concepts and techniques used in MDA.
2.2.2 MDA as a TEL Design Process Following MDA practices, a typical design scenario based on MDA is presented in Fig. 2 and can be described as follows [ 33].
First, the instructional designer informally defines the learning scenario to be created and the resources needed (CIM).
To formalize intent in a pedagogic model, a modeling language is used. This language allows defining the pedagogic method and contextual constraints. In MDA terms, the pedagogic model is a PIM.
Next, to obtain a system that can be executed by a LMS, the pedagogic model is transformed into a technical model (PSM). This corresponds to the LMS metamodel. The common way to transform one model into another (and the one we used) is to use a model transformation engine (like ATL [ 20]) and a set of model transformation rules dedicated to this type of mapping. Transformation rules express refinements from the pedagogic metamodel to the LMS metamodel.
We mention that the difference between a pedagogic concept (related to PIM) and a technical one (related to PSM) consists in the fact that a pedagogic concept is abstract and related to the pedagogic intents of the instructional designer, but a technical concept is concrete and represents an alternative tool allowing the execution of the corresponding pedagogic concept within an LMS. Therefore, pedagogic concepts remain abstract until they are contextualized by LMS concepts at the transformation step.
It is important to note that the semantic signification of each element in the pedagogic metamodel and in the LMS metamodel is as follows:
- Each concept (of the LMS or pedagogic model) represents an activity, a resource, or an actor.
- Each attribute of a concept represents a property of that concept.
- Each association expresses a relationship between two concepts: two activities (e.g., complementary use), two actors (e.g., collaboration rules), two resources (e.g., simultaneous use), a resource and an activity (e.g., associating a resource to an activity), a resource and an actor (e.g., affecting an actor to a resource) and, finally, an activity and an actor (e.g., affecting an actor to an activity).
2.2.3 MDA Contribution to TEL Design In the literature, a number of recent papers show an interest in TEL system design based on MDA [ 9], [ 10], [ 13], [ 32], [ 33], [ 37].
As explained in Section 2.1, MDA as an instructional design process addresses the limits of LTS by empowering instructional designers to:
1. Create specific modeling languages that ensure both pedagogic and contextual expressiveness. These languages are specific and are thus more targeted and focused. They allow accurate descriptions with a semantic precision not achievable with generic models.
3. Reuse learning scenario models through standards such as MOF (Meta Object Facility [ 28]).
4. Address any LMS, which is an important benefit offered by MDA since the design based on an LTS requires adoption of a compliant LMS.
5. Automatically deploy TEL systems on the chosen LMS. Therefore, designers are spared the technical difficulties related to deployment.
Nevertheless, the main limit of MDA as an instructional design process is the high level of technical expertise it requires.
Our current research focuses on proposing automated instructional design tools to assist instructional designers in performing different tasks related to the MDA process. In this paper, we focus on the task of transforming pedagogy with regard to LMS tooling. In the next section, we present and analyze work related to this issue.
1. Assistance message: a message explaining a best practice and where it is pertinent.
2. Configuration: timed display of an assistance message when a best practice is activated.
3. LMS element(s) concerned by a best practice.
R. Drira and M. Laroussi are with the RIADI Laboratory, ENSI Manouba Campus 2010, Tunisia.
E-mail: email@example.com, firstname.lastname@example.org.
X. Le Pallec is with the LIFL Laboratory, USTL, F-59650 Villeneuve d'Ascq, France. E-mail: email@example.com.
B. Warin is with ULCO, LISIC, Calais F-62100, France.
Manuscript received 16 Mar. 2011; revised 27 Oct. 2011; accepted 18 Nov. 2011; published online 6 Feb. 2012.
For information on obtaining reprints of this article, please send e-mail to: firstname.lastname@example.org, and reference IEEECS Log Number TLT-2011-03-0030.
Digital Object Identifier no. 10.1109/TLT.2011.35.
Rim Drira received the PhD degree in computer science in 2010 from the University of Manouba (Tunisia) and the University of Sciences and Technologies of Lille I (France). She is currently associated with the Riadi Laboratory ( http://www.riadi.rnu.tn) and the NOCE Team of the LIFL Laboratory ( http://www.lifl.fr) and is a teaching assistant in the University of Tunis. Since 2005, she has been interested in TEL system design, in particular, based on technology learning standards and a model-driven approach. She has coauthored around 10 publications.
Mona Laroussi received the PhD degree in computer science from the University of Tunis in Tunisia in 2001. She is currently an assistant professor in the Department of Computer Science at the National Institute of Applied Sciences and Technology, Tunisia, and an associated researcher in the NOCE group of the LIFL Laboratory, Lille, France ( http://noce. univ-lille1.fr/cms). Her current research interests include mobile learning, user models, adaptability, and context-awareness. She is a coauthor of more than 40 publications related to her research areas.
Xavier Le Pallec received the PhD degree in computer science in 2002. He is an associate professor in the Computer Science Research Lab of the University of Lille 1 (LIFL). His research interests include model-driven engineering, visual notations, and model components. After studies on e-learning as an application domain, he has worked on multimodal interactions, and in particular on intelligent environments, since 2007.
Bruno Warin received the PhD degree in 1986 with a thesis in theoretical computer science on “rationality and recognizability on graphs.” From 1992 to 1998, while holding a position as lecturer at the Université du Littoral Côte d'Opale, he created a company to develop an expert system in insurance pricing. This system is the French market leader. Since 1999, his research interests have centered on the construction of technology-enhanced learning. In parallel to his research, he works in higher education, in particular on project-based learning and software engineering applied to learning management systems.