ABSTRACT
Critical perspectives on Data-Driven Decision-Making (DDDM) and analytics have focused on issues such as students' data privacy, the surveillance state, the tendency for a technocratic approach to educational management, and the commercialization of student data. In higher education, the largest body of research on DDDM and learning analytics (LA) focuses on descriptions about innovations, interventions, and developments with LA and DDDM, a state of affairs not dissimilar from the K-12 literature. One of the most promising frameworks for studying DDDM and the use of LA was developed by Coburn and Turner, who draw upon insights from theories of situated and distributed cognition to develop a model that captures the temporal processes of data use as they unfold in specific situations. The chapter reveals some different data chains that study participants reported, each involving a diverse range of data and information types, retention structure elements, and forms of continuous improvement.
