Instantiation in Partial Learning

Abstract

The adaptive learning systems are changing the learning process as we know it. One of the advantages they have over the traditional ways of learning is the attempt to adapt to the learner's capabilities in order to deliver the knowledge as optimizing as possible. Even in such sophisticated implementations there are differences in the treatment of the adaptive learning.

During the past years spent in research of different aspects of the adaptive learning, we made a distinction of our latest development phase as an advanced adaptive learning, having more specific approach to the problem from the phase where the problem of adaptive learning is treated as a general case. Considering the conditions of advanced adaptive learning rather than basic adaptive learning, the process of learning is different and closely related to the human learner. In order to demonstrate this key improvement, we presented a general learner model through its learning mechanism and its behavior in the adaptive learning environment together with the instantiation process. In this paper we present a new way of learning with learning environment instances, constructed by choosing different ways to obtain the knowledge for a target unit.