Social networks serendipity for educational learning by surprise from big and small data analysis

Abstract

This paper follows our stream of research on Serendipity Engineering for series events connected in time and space. Occurring by rather hidden and unexpected connections Serendipity Engineering is also related to the bipolar decision making process: hesitate before taking the risk, accept and engage in serendipity or deny it. Although research exists within the Global Systems Science field for serendipity identification and engineering, the results are not encouraging as such research now fails in predictions. Massive global research and apparent impact is evident and reachable in social media such as Twitter, Facebook, Instagram etc. Furthermore, utilising a range of tools, methodologies and techniques, it may be possible to identify activities and hidden connections in both big and small data and predict, aid or even interfere in the decision making process. Such interference based on prediction can actually alter the future decision making users’ domino of events and items selected and orchestrate series of events that may have possible utility in psychology, marketing, serendipity systems design and engineering design or other influential disciplines. Our research main aim is the identification, collection and analysis of both big and small data in order to shed light in serendipity connections in chrono-spatial intelligence and even engineer it, called Chrono-Spatial Intelligence for Serendipity Engineering (CSISE). A second aim is to identify tools, methodologies and evaluation techniques to facilitate such deep understanding via Chrono-Spatial Intelligence Analytics. Here, we propose a serendipity engineering model for learning insights anchored in big and small data methods, tools and learning analytics.