Data-Intensive Improvement: The Intersection of Data Science and Improvement Science
Item
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
Date
In publication
Pages
465-483
Publisher
Rowman & Littlefield Publishers
Resource type
Research/Scholarly Media
Resource status/form
Published Text
Scholarship genre
Synthesis/Overview
Methodological
IRE Approach/Concept
Featured case/project
Primary national context
Open access/full-text available
No
ISBN
978-1-5381-5234-8
Is part of
Other related resources/entities
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
Item sets
Linked resources
Filter by property
| Title | Alternate label | Class |
|---|---|---|
 Section IV: Designs, Tools, and Methods of Improvement Research in Education [Foundational Handbook] |
Book Section |
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