In this paper we review the vast literature on data driven decision-making (DDDM) in institutions of higher education (IHEs). Given increasing pressure for IHEs to use data to inform decision-making, it is important to understand what is known about the opportunities and challenges facing DDDM. To contextualize the literature we briefly review the history of DDDM in education settings. We then summarize how scholars have conceptualized and studied DDDM in IHEs in general, and then regarding curriculum and instruction specifically. Our review found that scholars have examined DDDM in regard to institutional functioning and structures (e.g., total quality management, knowledge management, and strategic planning), supporting institutional decision-making (e.g., decision support systems, data mining, and academic analytics), meeting institutional or programmatic accreditation, quality assurance, developing and honing methods for improving data use, analysis, and distribution, facilitating participatory models of decision-making, and curricular and/or instructional improvements. We found curriculum and instruction specific research on course management, learning analytics, curriculum planning, assisting teaching and learning centers, in-class formative assessment, and post-class data use. We discuss implications of our findings in a framework of considerations related to successful DDDM implementation and study, including review of DDDM in K-12 environments in the US and postsecondary education worldwide. Recommendations for successful DDDM include acknowledging and attending to local realities, ensuring salience of data and DDDM processes to key stakeholders, fostering and capitalizing on local data savvy and collaboration among stakeholders towards meaningful objectives, and formalizing and normalizing adequate data collection and management systems and access. Based on these results we recommend that educators, policymakers, and researchers look to the experiences of K-12 educators in the US and European and Australasian IHEs with DDDM movements, focus on linking larger data systems and policies with local needs and practices and locally derived data, engage in more descriptive research on how local actors perceive and utilize data, and focus on linking larger data systems and policies with these local needs and practices.
Keywords: Data-driven decision-making, higher education, literature review