People Power: A human-computer collaborative learning system

Abstract

This paper reports our research work in the new field of human-computer collaborative learning (HCCL). The general architecture of an HCCL is defined. An HCCL system, called People Power, has been implemented in CLOS. It contains a micro-world in which the learner can create an electoral system and simulate elections. The learner's task is to infer relations between the features of the electoral system and the distribution of seats. The human learner collaborates with a computational learner. The collaboration between learners is modelled as ‘socially distributed cognition’ (SDC). We view a pair of learners as a single cognitive agent whose components are distributed over two brains. This model maps inter-people and intra-people communication processes and thereby proposes an explanation of how the former generates the latter: the pattern of arguments that emerge from dialogue is reused by the artificial learner when it reasons alone. Reasoning is implemented as a dialogue with oneself. We report some results of the first experiments we have conducted.