A multiagent approach to obtain open and flexible user models in adaptive learning communities

Abstract

Nowadays, many user-modeling systems are applied to web-based adaptive systems. The large number of very different users using these systems make user model construction difficult. The solution is to use machine learning techniques that dynamically update the models by monitoring user behavior. However, the design of machine learning tasks for user modeling is static. This poses a problem in adaptive learning environments based on virtual communities. Each virtual community has its own administrators, and each administrator may prefer to include some more information on the user model. Another problem in the application of machine learning techniques for user model construction is the need to retrain the machine learning algorithms when new user interaction data become available. To face these problems, in this paper we present a multiagent adaptive module set in an adaptive learning collaborative environment. Our goal is two fold: (i) we want each administrator to be able to define new machine learning attributes in the user model (ii) we want to provide a mechanism to dynamically retrain the algorithms.