Tracing and Enhancing Serendipitous Learning with ViewpointS

Abstract

This is a position paper describing the author’s views on a potential new research direction for assessing, constructing and exploiting brain-founded models of learning of individual as well as collective humans. The recent approach – called ViewpointS – aiming to unify the Semantic and the Social Web, data mining included, by means of a simple “subjective” primitive – the viewpoint - denoting proximity among elements of the world, seems to offer a promising context of innovative empirical research in modeling human learning less constrained with respect to the previous three other ones. Within this context, a few phenomena of serendipitous learning have been simulated, showing that the process of collective construction of knowledge during free navigation may offer interesting side effects of informal, serendipitous knowledge acquisition and learning. We envision therefore an extension of the modeling functions within ViewpointS by adding measures of the emotions and mental states as acquired during experimental sessions. These brain-related components may in a first phase allow to describe and classify models in order to understand the relations among knowledge structures and mental states. Subsequently, more predictive experiments may be envisaged. These may allow to forecast the acquisition of knowledge as well as sentiment from previous events during interactions. We are convinced that useful applications may range, for instance, from Tutoring, to Health, to consensus formation in Politics at very low investment costs as the experimental set up consists of minimal extensions of the Web.