Data-Intensive Improvement: The Intersection of Data Science and Improvement Science

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Title: Data-Intensive Improvement: The Intersection of Data Science and Improvement Science
Alternate name: Chapter 20
Abstract/Description: While new possibilities to examine educational processes have evolved around ever-increasing volumes of data, our goal in this chapter is to describe the role of data-intensive approaches within the specific context of improvement research in education. We use the label data-intensive as a stand-in for the previously mentioned fields of educational research that make regular use of data from digital platforms and environments (National Science Foundation, 2015). The specific combinations of improvement and data-intensive research that we highlight in this chapter share a common structure of using large, complex data sets to generate insights and interventions that are employed in efforts to improve learning environments.; [Quoted from p. 465]
Author/creator: Krumm, Andrew E.; Bowers, Alex J.
Date: 2022
In publication: The Foundational Handbook on Improvement Research in Education
Editor: Peurach, Donald J.; Russell, Jennifer Lin; Cohen-Vogel, Lora; Penuel, William R.
Pages: 465-483
Publisher: Rowman & Littlefield Publishers
Resource type: en Research/Scholarly Media
Resource status/form: en Published Text
Scholarship genre: en Synthesis/Overview; en Methodological
IRE Approach/Concept: Data-Intensive Research; Data-Intensive Improvement; Improvement Science; Data Use; Continuous Improvement; Assessment; Research Practice Partnership (RPP); Collaborative Data-Intensive Improvement (CDI); Collaborative Data-Intensive Research (CDIR); Data Visualization
Featured case/project: Carnegie Math Pathways (CMP); Central Valley Networked Improvement Community (CVNIC); Youth Data Archive (YDA); DataShop; Chicago Public Schools (CPS); Educational Opportunity Project at Stanford University; Society for Improving Medical Professional Learning (SIMPL)
Primary national context: United States
Open access/full-text available: en No
ISBN: 978-1-5381-5234-8
URL: Publication Webpage
Other related resources/entities: Bill & Melinda Gates Foundation; Carnegie Foundation for the Advancement of Teaching; WestEd
Citation: Krumm, A. E., & Bowers, A. J. (2022). Data-Intensive Improvement: The Intersection of Data Science and Improvement Science. In D. J. Peurach, J. L. Russell, L. Cohen-Vogel, & W. R. Penuel (Eds.), The Foundational Handbook on Improvement Research in Education (pp. 465-483). Rowman & Littlefield Publishers. https://rowman.com/ISBN/9781538152348/The-Foundational-Handbook-on-Improvement-Research-in-Education