Designing computational systems for serendipity in learning

Abstract

Serendipity, the process of making fortunate discoveries for which someone was not looking for, can play a crucial role in leveraging creativity in learning [1]. Serendipity enables creative connections to develop while it can have a role in revealing hidden connections or "hidden analogies", especially in a social context such as in most learning processes [2]. The results of a chance encounter can result in new ideas relevant to the learner's previous knowledge [3]. In previous work, we have reviewed the related literature and identified five enabling factors for serendipity [4]: diversity, unexpectedness or novelty, personalization, visualization, and social interaction. The aim of this paper is to construct an architectural framework based on the aforementioned factors that can be used as a guide in the development of information seeking systems aiming to leverage serendipity in learning. © 2013 Springer-Verlag.