Framework for group learning: FITS/CSCL

Computer Supported Collaborative Learning(CSCL) has recently been gathering much attention of many researchers. It is based on the idea that knowledge should not be simply transferred from a teacher to a learner but be built in a learner's head while interacting with each other in group activities. Although collaborative learning is not a new concept which has been carried out in classrooms for long time, it is expected to be a new promising paradigm in AI in ED community.

The advantages of CSCL are summarized as follows:

1. Getting learners motivated
2. Learning is stimulated more through communication done between each other.
   • Learning by teaching which facilitates learning by externalization of one's understanding
   • Learning by diagnosing which deepens understanding by diagnosing other learners.
   • Learning by open discussion which facilitates thinking capability through interaction.
3. Learning of how to discuss and how to negotiate

Although all of them are equally important, computers have nothing new to do with the first one. The last issue is interesting, but it is very ambitious, since it requires almost complete natural language understanding capability of computers in order to build an operational system. According to this observation, we take the second one as a target to realize in our research. Our research is mainly concerned with the following three major goals:

1. To identify objectives of communication and to build its decision model
2. To identify modes of communication and to build its decision model
3. To identify roles of learner models in CSCL.

In order to achieve these goals, we do need a sophisticated vocabulary in terms of which we can describe objectives and modes of communication, decision models, knowledge for decision making, etc. This implies we first design ontology for CSCL. Needless to say, AI techniques based on symbolism need primitives or a set of basic vocabulary for representing knowledge and objects. They reflect conceptualization of systems under consideration. One might think that ontological issues must be far into domain and hence it is domain-specific and loses generality. By ontology, however, we mean a system of basic vocabulary usable across various domain knowledge, that is, "generic task". Working hypothesis of this research is that we can find a good ontology for CSCL task by looking at the task carefully from generic task point of view. What should be notice is to design a good ontology to represent the domain knowledge, the communication model, and the learning process model from the educational point of view. We are currently engaged in developing the intelligent support system for collaborative learning of physics domain.

Major publications

1. Learning Goal Ontology Supported by Learning Theories for Opportunistic Group Formation, Proc. of AIED'99, (to appear)
2. Opportunistic Group Formation- A Theory for Intelligent Support in Collaborative Learning -, Proc. of AIED-97, pp.167-174, (1997)
3. Ontological Issues of CSCL Systems Design, Proc. of AIED-95, Washington, DC, pp.242-249 (1995)