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Data-informed decision-making (DIDM) gives reference to the collection and analysis of data to guide decisions that improve success. DIDM is used in education communities (where data is used with the goal of helping students and improving curricula) but is also applicable to (and thus also used in) other fields in which data is used to inform decisions. While data based decision making is a more common term, data-informed decision-making is a preferable term since decisions should not be based solely on quantitative data. Most educators have access to a data system for the purpose of analyzing student data. These data systems present data to educators in an over-the-counter data format (embedding labels, supplemental documentation, and a help system, making key package/display and content decisions) to improve the success of educators’ data-informed decision-making. In Business, fostering and actively supporting DIDM in their firm and among their colleagues could be the main rôle of CIOs (Chief Information Officers) or CDOs (Chief Data Officers).
Assessment in higher education is a form of DIDM aimed at using evidence of what students learn to improve curriculum, student learning, and teaching. Standardized tests, grades, and student work scored by rubrics are forms of student learning outcomes assessment. There are numerous organizations aimed at promoting the assessment of student learning through DIDM including the National Institute for Learning Outcomes Assessment, the Association for the Assessment of Student Learning in Higher Education, and, to an extent, the Association of American Colleges and Universities.
- U.S. Department of Education Office of Planning, Evaluation and Policy Development (2009). Implementing data-informed decision making in schools: Teacher access, supports and use. United States Department of Education (ERIC Document Reproduction Service No. ED504191)
- Knapp, M. S., Swinnerton, J. A., Copland, M. A., & Monpas-Hubar, J. (2006). Data-informed leadership in education. Seattle, WA: Center for the Study of Teaching and Policy.
- Aarons, D. (2009). Report finds states on course to build pupil-data systems. Education Week, 29(13), 6.
- Rankin, J. (2013, March 28). How data Systems & reports can either fight or propagate the data analysis error epidemic, and how educator leaders can help. Presentation conducted from Technology Information Center for Administrative Leadership (TICAL) School Leadership Summit.
- Delort P. 2012. ICCP Technology Foresight Forum - "Harnessing data as a new source of growth: Big data analytics and policies" . OECD, 2012
- Flaherty, Colleen. "Large-Scale Assessment Without Standardized Tests". Inside HigherEd. Retrieved 24 February 2017.
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