The CSCL (Computer Supported Collaborative Learning) community has been suffering from a lack of models and tools to support the effective design of collaborative learning (CL) sessions based on learning theories [3]. One reason given by [1;3] is the difficulty of understanding, comparing and using theoretical approaches in real educational applications due to the lack of a common understanding of what a learning theory is. Without such models it is difficult to describe, share and analyze CL processes while making use of learning theories to evaluate educational benefits and to design CL sessions.

Usual approaches provide their systems with a kind of "expertise" using a set of heuristics and domain theories built in the procedures (programming languages). This means that the programmers, not the systems, have an understanding of the knowledge being used. As a result, these systems cannot share or build new knowledge, ignore the existence of theories on which the knowledge is based, and finally cannot justify their recommendations systematically and scientifically [6] . As pointed out by [6]: "neither inference techniques nor beautiful theoretical formalism can contribute to an improvement of the situation".

Our approach calls upon techniques of ontological engineering to, at first, establish a common understanding of what a learning theory is by representing it in terms of its explicitness, formalism, concepts and vocabulary. This makes theories understandable both by computers and humans. We then propose techniques of reasoning on these theories which allows dynamic guiding of instructional planning and an effective design of learning processes.

2. BACKGROUND AND RELATED WORK

In practical terms ontological engineering helps to achieve the following [2;6]:

• A common vocabulary and highly structured definition of concepts;
• semantic interoperability and high expressiveness;
• coherence and systematization of knowledge; and
• meta-models and foundations for solving different problems in a variety of contexts.

In CSCL research previous work using ontologies have been successfully applied to solve problems as: group formation [5], interaction analysis [3] and modeling of learner's development [4]. Our approach uses these previous achievements to propose a formalization of learning theory for CL, to create new techniques for effective design and analysis of group learning activities and, finally, to provide foundations for a complete ontology-aware authoring system which can really understand the theories. As part of our work we extend the Collaborative Learning Ontology [5]. Some of the concepts that we use in such ontology are shown in Figure 1.

Figure 1. Concepts of Collaborative Learnin Ontology

3. GOALS OF THE RESEACH AND CURRENT STATUS

Until now, with the achievements of using ontologies in CSCL presented in section 2, it is possible to successfully identify which kind of collaboration occurs in a CL session, understanding the essence of the group's interactions, and to estimate the expected educational benefits for each member. Nevertheless, there are some limitations:

• There is no relation among interactions models and learner’s development;
• We can not define what a learning theory is explaining the learner's development through a set of events; and
• There is no way to intervene while a session is taking place.
  For example, if a learner, who has a misunderstanding, teaches another learner, he will transfer his problem to the other learner from the beginning until the end of the CL session.

To deal with the problems presented above, we analyze seven different learning theories, frequently used to supported CSCL activities, to clarify the relationships among interaction, learning strategies and learning goals. Thus, based on previous models of interaction processes [3] and learner’s growth [4], we defined an ontological structure to represent an excerpt of the Learning Theory concept. This ontological structure can be represend as a Graph. For example, in Figure 2 we show a graphical visualization of our structure using the learning theory Cogntive Apprenticeship. Each circle in the Figure 2 represents an interaction. For each interaction we have strategies, goals and roles for learners who are interacting.

Figure 2. Graphical example of an ontological structure to describe the learning theory Cogntive Apprenticeship using the strategy Learning by Apprenticeship

The proposed structure allows us to work with theories at the macro (strategies and goals) and micro levels (interactions and roles) and to create a link between them. This link clarifies, more precisely, how interactions can affect learner's development helping designers to select interactions and roles for each learner with justifications based on the theories.

6 REFERENCES
1. Conole, G., Dyke, M., Oliver, M. and Seale, J. (2004) "Mapping pedagogy and tools for effective learning design". Computer & Education, 43, 1-2, 17-33.
2. Devedzic, V. Understanding Ontological Engineering. Communications of the ACM, 45, 4, (April 2002), 136-144.
3. Inaba, A., Ohkubo, R., Ikeda, M. and Mizoguchi, R. Models and Vocabulary to Represent Learner-to-Learner Interaction Process in Collaborative Learning. In Proceeding of the Int. Conference on Computers in Education, 1088-1096.
4. Inaba, A., Ikeda, M. and Mizoguchi, R. (2003b) What Learning Patterns are Effective for a Learner’s Growth?. In Proceeding of the Int. Conference on Artificial Intelligence in Education, 219-226.
5. Inaba, A., Supnithi, T., Ikeda, M., Mizoguchi, R. and Toyoda, (2000) J. How Can We Form Effective Collaborative Learning Groups?. In Lecture Notes in Computer Science 1839, 282-291.
6. Mizoguchi, R. & Bourdeau, J. (2000) "Using Ontological Engineering to Overcome AI-ED Problems", Int. Journal of Artificial Intelligence in Education, 11, 2, 107-121.
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