A Computational Approach to Socially Distributed Cognition

In most Interactive Learning Environments (ILEs), the human learner interacts with an expert in the domain to be taught. We explored an different approach: the system does not know more than the learner, but learns by interacting with him. A human-computer collaborative learning (HCCL) system includes a micro-world, in which two learners jointly try to solve problems and learn, the human learner and a computerized colearner. This paper presents the foundations of this artificial co-learner. The collaboration between learners is modelled as 'socially distributed cognition' (SDC). The SDC model connects three ideas: i) a group is a cognitive system, ii) reflection is a dialogue with oneself, iii) social processes are internalised. The key has been to find a computational connection between those ideas. The domain chosen for illustration is the argumentation concerning how some changes to an electoral system affect the results of elections. This argumentation involves a sequence of arguments and their refutations. The basic principle is that learners 'store' the structure of this argumentation (dialogue pattern) and 'replay' it individually later on. The verbs 'store' and 'replay' do not refer to a simple 'record and retrieve' process. Storage is implemented as the incremental and parameterised evolution of a network of arguments, here called a 'dialogue pattern'. The learning outcome is a structuration of knowledge (rules) into situation-specific models, used to guide reasoning. We conducted experiments in two settings: with a human and an artificial learner or with two artificial learners. The common findings of these two experiments is that the SDC model generates learning effects provided that the discussion is intensive, i.e. that many arguments are brought into dialogue. The importance of this variable also appears in Hutchins' (1991) modelling of the evolution of the confirmation bias in groups. It is argued than computational models are heuristic tools, allowing researchers to isolate variables for designing empirical studies with human subjects. To appear in The European Journal of Psychology of Education. Dillenbourg & Self 3