Handling Continuity in Seamless Learning via Opportunistic Recognition and Evaluation of Activity Cohesion

Abstract

Handling the continuity of learning experience across different activities and contexts is a key challenge for seamless learning. Current context and activity recognition techniques work well in fixed environments where sensors deployment and data are known but are not adaptable to dynamic and changing situations when, for instance, a learner moves from dense to rare sensor environments. Moreover, even if we are able to recognize with more or less precision activities, it still remains the issue of understanding if there are useful and interesting educational concepts related to the activities. In this short paper we discuss our ideas and preliminary results on the definition of an opportunistic approach to recognize activities and contents that leverages on the characterization of the environments in terms of sensor richness and knowledge expressiveness. The basic idea is to recognize the kind of environment in which a learner is involved and then to adapt the most suitable techniques taking advantage of the specific features of the environment. Next, we discuss two measures allowing us to understand i) the cohesion degree of the set of (informal, not formal, formal) activities a learner is involved in, and ii) if the the learner is able and in a proper disposition to acquire new knowledge or develop a new competence from the execution of activities. We propose the adoption of the first measure in the fitness function of a swarm intelligence algorithm to optimise the search of cohesive activities.