Educational data sciences

Abstract

In this paper, we develop a conceptual framework for organizing emerging analytic activities involving educational data that can fall under broad and often loosely defined categories, including Academic/Institutional Analytics, Learning Analytics/Educational Data Mining, Learner Analytics/Personalization, and Systemic Instructional Improvement. While our approach is substantially informed by both higher education and K-12 settings, this framework is developed to apply across all educational contexts where digital data are used to inform learners and the management of learning. Although we can identify movements that are relatively independent of each other today, we believe they will in all cases expand from their current margins to encompass larger domains and increasingly overlap. The growth in these analytic activities leads to the need to find ways to synthesize understandings, find common language, and develop frames of reference to help these movements develop into a field.