Organizational considerations in establishing the Data-Informed District
Abstract
In the United States, effective data use is proving to be a vexing problem. In response, scholars have recently begun viewing this as a systemic problem, believing there are actions a school district may take to make data use more efficient and tenable throughout the organization. In this article, we add to the knowledge of how school organizations can more effectively use data for educational improvement. Through the lens of the Data-Informed District, we leverage the research on organizational improvement and data use to discuss 3 specific organizational areas in which these districts may improve: establishing common understandings, professional learning for using data, and computer data systems.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
ICT skills are an important condition, but they are not sufficient to ensure actual data use in schools on their own.
“Moreover, ICT skills are an important condition, but they are not sufficient to ensure actual data use in schools on their own (Cho, Allwarden, & Wayman, 2016;Hamilton et al., 2009;Wayman, Jimerson et al., 2012).”
From Formative assessment: A systematic review of critical teacher prerequisites for classroom practice (2020) · cites this paper as Wayman, Jimerson et al., 2012
Data must be perceived as relevant, reliable, and valid by teachers.
“However, we want to stress here that although data availability and access may be facilitated by some sort of data or assessment system, it is crucial that these data are perceived as relevant, reliable, and valid by teachers (Wayman, Jimerson et al., 2012).”
From Formative assessment: A systematic review of critical teacher prerequisites for classroom practice (2020) · cites this paper as Wayman, Jimerson et al., 2012
The availability of data does not ensure the actual use of data to make changes in instructional practices in the classroom.
“Studies (e.g. Gelderblom et al. 2016; Wayman, Jimerson, and Cho 2012) have shown that the availability of data does not ensure the actual use of data to make changes in the instructional practices that happen in the classroom.”
From Data-based decision-making for school improvement: Research insights and gaps (2019) · cites this paper as Wayman, Jimerson, and Cho 2012
Cites
Lave, J., & Wenger, E. (1991) · Cambridge University Press eBooks · doi:10.1017/cbo9780511815355 · Cited by 33,219 · see citing works
Situated Cognition and the Culture of Learning
Brown, J. S., Collins, A., & Duguid, P. (1989) · Educational Researcher · doi:10.3102/0013189x018001032 · Cited by 13,645 · cited 1× · see citing works
“Fullan ( 2007 ) asserted that the majority of teacher learning must happen in the context of everyday work, and others have adopted a similar approach (eg, Borko, 2004 ; Brown, Collins, & Duguid, 1989 ; Lave & Wenger, 1991 ; Spillane & Louis, 2002 ).”
Data-informed curriculum reform: Which data, what purposes, and promoting and hindering factors
Schildkamp, K., & Kuiper, W. (2010) · Teaching and Teacher Education · doi:10.1016/j.tate.2009.06.007 · Cited by 406 · cited 3× · see citing works
“While the positive effects of collaboration are likewise well established in the data-use literature (e.g., Kerr et al., 2006; Lachat & Smith, 2005; Schildkamp & Kuiper, 2010; Wayman & Stringfield, 2006), the link between collaboration and learning how to use data is just beginning to emerge.”
Kerr, K. A., Marsh, J. A., Ikemoto, G. S., Darilek, H., & Barney, H. (2006) · American Journal of Education · doi:10.1086/505057 · Cited by 341 · see citing works
Teachers’ Use of Data: Loose Coupling, Agenda Setting, and Team Norms
Young, V. M. (2006) · American Journal of Education · doi:10.1086/505058 · Cited by 281 · see citing works
Park, V., & Datnow, A. (2009) · School Leadership & Management · doi:10.1080/13632430903162541 · Cited by 199 · cited 5× · see citing works
“Thus, what actually becomes of policy can be seen as flexible, multidirectional, and co-constructed (Datnow, 2006; Park & Datnow, 2009).”
Influential
Flagged first, then by citations
Cited by
Sorted by citations
Data-based decision-making for school improvement: Research insights and gaps
Schildkamp, K. (2019) · Educational Research · doi:10.1080/00131881.2019.1625716 · Cited by 347 · see citing works
Formative assessment: A systematic review of critical teacher prerequisites for classroom practice
Schildkamp, K., van der Kleij, F. M., Heitink, M. C., Kippers, W. B., & Veldkamp, B. P. (2020) · International Journal of Educational Research · doi:10.1016/j.ijer.2020.101602 · Cited by 330 · see citing works
Misconceptions about data-based decision making in education: An exploration of the literature
Mandinach, E. B., & Schildkamp, K. (2021) · Studies in Educational Evaluation · doi:10.1016/j.stueduc.2020.100842 · Cited by 209 · see citing works
Factors Influencing the Functioning of Data Teams
Schildkamp, K., & Poortman, C. (2015) · Teachers College Record: The Voice of Scholarship in Education · doi:10.1177/016146811511700403 · Cited by 185 · see citing works
Data teams for school improvement
Schildkamp, K., Poortman, C. L., & Handelzalts, A. (2016) · School Effectiveness and School Improvement · doi:10.1080/09243453.2015.1056192 · Cited by 171 · see citing works
Van der Kleij, F. M., Vermeulen, J. A., Schildkamp, K., & Eggen, T. J. H. M. (2015) · Assessment in Education: Principles, Policy & Practice · doi:10.1080/0969594x.2014.999024 · Cited by 135 · see citing works
Kippers, W. B., Wolterinck, C. H. D., Schildkamp, K., Poortman, C. L., & Visscher, A. J. (2018) · Teaching and Teacher Education · doi:10.1016/j.tate.2018.06.015 · Cited by 119 · see citing works
Prenger, R., & Schildkamp, K. (2018) · Educational Psychology · doi:10.1080/01443410.2018.1426834 · Cited by 101 · see citing works
Jimerson, J. B. (2014) · Studies in Educational Evaluation · doi:10.1016/j.stueduc.2013.10.010 · Cited by 97 · see citing works
Self-citationSolving student achievement problems with a data use intervention for teachers
Poortman, C. L., & Schildkamp, K. (2016) · Teaching and Teacher Education · doi:10.1016/j.tate.2016.06.010 · Cited by 95 · see citing works
Farley-Ripple, E. N., & Buttram, J. L. (2014) · Studies in Educational Evaluation · doi:10.1016/j.stueduc.2013.09.006 · Cited by 79 · see citing works
Data-based decision making for instructional improvement in primary education
Gelderblom, G., Schildkamp, K., Pieters, J., & Ehren, M. (2016) · International Journal of Educational Research · doi:10.1016/j.ijer.2016.07.004 · Cited by 74 · see citing works
Redefining public values: data use and value dilemmas in education
Mausethagen, S., Prøitz, T. S., & Skedsmo, G. (2021) · Education Inquiry · doi:10.1080/20004508.2020.1733744 · Cited by 19 · see citing works
- Authors
- Wayman, J. C., Jimerson, J. B., & Cho, V.
- Affiliations
- The University of Texas at Austin (US)
- Texas Christian University (US)
- Boston College (US)
- Corresponding author
- Wayman, Jeffrey C.
- Venue
- School Effectiveness and School Improvement
- Publisher
- Informa UK Limited
- Published
- 2012-06
- Volume
- 23
- Issue
- 2
- Pages
- 159-178
- ISSN
- 0924-3453 (ISSN-L)
- 1744-5124
- OpenAlex
- W2133855281
- Cited by
- 122
- References
- 6
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 7 · 2023: 4 · 2022: 10 · 2021: 8 · 2020: 6 · 2019: 5 · 2018: 7 · 2017: 15 · 2016: 14 · 2015: 17 · 2014: 7 · 2013: 8 · 2012: 2
- FWCI
- 22.21
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- School Effectiveness and School Improvement (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Educational Assessment and Improvement
- Social Sciences › Education › Educational Leadership and Practices
- Social Sciences › Education › Teacher Education and Leadership Studies
- Keywords
- data use in education, school districts, organizational improvement, common understanding, professional learning, data systems, educational improvement, organizational culture
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
