Participatory Learning: A Constructivist Model

Abstract

This chapter discusses a formal model of human and machine learning called participatory learning. This model allows the representation of machine learning in a constructivist framework. In this model, the learner's previous beliefs play an important role in the assimilation of further information. A central aspect of the theory is the degree of compatibility between observations and belief. In a constructivist theory, learning is a bootstrap process. The name participatory learning highlights the fact that the learner's current knowledge of the subject participates intimately in the learning process. Central to participatory learning is the idea that an exogenous observation has the greatest impact on learning when the observation is largely compatible with the present belief system. In particular, observations in conflict with current core constructs or strongly held beliefs are discounted. The role of arousal or anxiety can be thought of as salient and/or massed negative feedback.