It is common to think of a “learner model” as a global description of a student’s understanding of domain content. We propose a notion of learner model where the emphasis is on the modelling process rather than the global description. In this re-formulation there is no one single learner model in the traditional sense, but a virtual infinity of potential models, computed “just in time” about one or more individuals by a particular computational agent to the breadth and depth needed for a specific purpose. Learner models are thus fragmented, relativized, local, and often shallow. Moreover, social aspects of the learner are perhaps as important as content knowledge. We explore the implications of fragmented learner models, drawing examples from two collaborative learning systems. The main argument is that in distributed support environments that will be characteristic of tomorrow’s ITSs, it will be literally impossible to speak of a learner model as a single distinct entity. Rather “learner model” will be considered in its verb sense to be an action that is computed as needed during learning.