Educational big data: extracting meaning from data for smart education

摘要

Contemporary education, at all levels, is unbounded by time and space and learning can occur both in physical and virtual environments and with the assistance of countless technologies and pedagogical instruments. Data are a constant in all learning transactions regardless of where they take place, resulting in an overwhelming volume of data sources and formats that are behind the concept of Educational Big Data (EBD). With the pervasiveness of technology and online learning, big data, in the context of education, has experienced exponential growth and includes data deriving from students' interaction with technology and their personal and academic profile (Ferguson, 2012). Furthermore, EBD is a central concern of educational institutions, as its value becomes increasingly visible and as a new body of evidence of its benefits becomes gradually published and disclosed among researchers and practitioners. Governments too will be able to use EBD to make educational improvements. As it is true for big data in other sectors, in education, one of the dominant conundrums of its existence is how to extract meaning from the data that is collected. While some agreement occurs in terms of the best instruments and techniques, governments, institutions and teachers are still left with the question of how to implement them and what questions to pose.