The Use of Multiple Student Modeling to Parameterize Group Learning

Abstract

Recent criticism of ITS research for neglecting social aspects of learning is constructively answered by a practical attempt to extrapolate student modeling and intelligent learning support to group situations. The central focus is on using individually assessed student models to anticipate and parameterize group learning situations. This requires the integration of knowledge from individual models. Aspects of system architecture and dialogue design are discussed based on an example of a replicated interactive learning environment. Perspectives for the formal elaboration of multiple student modeling and its relevance for empirical testing are outlined.