Big Data and Learning Analytics: Heuristics and Interpretive Frameworks

Abstract

As a result of the capability to directly access information on all types of digitally mediated social practices and the corresponding massive accumulation of data, evaluation of social phenomena has taken a new direction that challenges conventional analytical models. Education is a suitable field for reflecting about these approaches, for analyzing the epistemic relevance of the new methods of data-driven assessment and for exploring the changes that arise from the new technological capabilities. This paper studies the impact of the new scenario in the field of learning analytics from big data, reflecting upon the change in the structure of the categories used in the evaluation of learning, as well as developing a detailed explanation of a new approach to learning analytics based on heuristics.