Cited by 21Educational Assessment and ImprovementTeacher Education and Leadership StudiesOrganizational Learning and Leadership

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

School-level organizational routines for learning: supporting data use

M. Kallemeyn, Leanne·2014·Journal of Educational Administration

Abstract

Purpose – The purpose of this paper is to use an extreme case to identify and describe the nature of routines that might support processes and outcomes of data use, drawing from a framework developed by Coburn and Turner (2012a). Design/methodology/approach – The author conducted a four-month case study (Stake, 1995) of an elementary school in a large urban school district that had implemented balanced score cards. The author identified a school that had strong qualities to support data use, including leadership and information systems. Findings – Two school-level organizational routines facilitated teachers’ data use: collaborative teams and processes of inquiry. These routines stored knowledge about the types of data teachers ought to notice, and to a lesser extent, how they ought to interpret data and construct implications for practice. These routines also provided opportunities for single and double-loop learning (Argyris and Schön, 1996) and might contribute to improvements in student learning. This case provides an example of how a school negotiated external performance management pressures, and maintained their professional autonomy, focussing on internally initiated assessments. Originality/value – Relatively little research has described what organizational routines support data use among practitioners. In addition to describing two routines, this case also demonstrated the need to frame these routines as organizational routines for learning. To further develop thes…

Overview

Integrity signals

Registry

Crossref · 10.1108/jea-02-2013-0025

Cites

3 of 3 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

  • Classroom teachers' tasks and responsibilities go beyond teaching itself and include advising/counselling, organising excursions, and administrative work.

    “However, we argue that classroom teachers’ tasks and responsibilities go beyond teaching itself and include many other tasks such as advising/counselling, organising excursions, and administrative work (e.g., Albiladi et al., 2020; Kallemeyn, 2014).”
  • Data-literate teachers place value on collaboration among colleagues and actively seek institutional support for effective data use.

    “They would also place value on collaboration among colleagues and actively seek institutional support for effective data use (Kallemeyn, 2014; Marsh & Farrell, 2015; Nicholson et al., 2017; Poortman & Schildkamp, 2016).”
  • Teachers use data to develop school-based intervention programs, make decisions about school directions, and evaluate school performance for meeting accountability requirements.

    “In terms of guiding school improvement strategies, teachers use data to develop school-based intervention programs (Abdusyakur & Poortman, 2019; Jimerson et al., 2021; Kallemeyn, 2014; Thompson, 2012), make decisions about school directions (Huffman & Kalnin, 2003; Prenger & Schildkamp, 2018; Schildkamp, 2019), and evaluate school performance for meeting the accountability requirements (Hardy, 201”

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 37

  1. Adler, P.S. , Goldoftas, B. and Levine, D.I. (1999), “Flexibility versus efficiency? A case study of model changeovers in the Toyota production system”, Organizational Science, Vol. 10 No. 1, pp. 43-68. · DOI
  2. Argyris, C. and Schön, D.A. (1996), Organizational Learning II: Theory, Method, and Practice, Addison-Wesley Publishing Company, Reading, MA.
  3. Boudett, K.P. and Steele, J.L. (Eds) (2007), Data Wise in Action: Stories of Schools Using Data to Improve Teaching and Learning, Harvard Education Press, Cambridge, MA.
  4. Boudett, K.P. , City, E.A. and Murnane, R.J. (Eds) (2005), Data Wise: A Step-by-Step Guide to Using Assessment Results to Improve Teaching and Learning, Harvard Education Press, Cambridge, MA.
  5. Coburn, C.E. and Turner, E.O. (2012a), “Research on data use: a framework and analysis”, Measurement: Interdisciplinary Research and Perspectives, Vol. 9 No. 4, pp. 173-206. · DOI · Paper page
  6. Coburn, C.E. and Turner, E.O. (2012b), “The practice of data use: an introduction”, American Journal of Education, Vol. 118 No. 2, pp. 99-111. · DOI
  7. Darling-Hammond, L. (1989), “Accountability for professional practice”, Teachers College Record, Vol. 91 No. 1, pp. 59-80. · DOI
  8. Datnow, A. and Park, V. (2009), “School system strategies for supporting data use”, in Kowalski, T.J. and Lasley, T.J. (Eds), Handbook of Data-Based Decision Making in Education, Routledge, New York, NY, pp. 191-206.
  9. Earl, L.M. and Katz, S. (2006), Leading Schools in a Data-Rich World, Corwin Press, Thousand Oaks, CA.
  10. Earl, L.M. and Timperley, H. (2008), Professional Learning Conversations: Challenges in Using Evidence for Improvement, Springer, New York, NY. · DOI
  11. Emerson, R.M. , Fretz, R.I. and Shaw, L.L. (2011), Writing Ethnographic Fieldnotes, 2nd ed., The University of Chicago Press, Chicago, IL. · DOI
  12. Fitzgerald, T. (2008), “The continuing politics of mistrust: performance management and the erosion of professional work”, Journal of Educational Administration and History, Vol. 40 No. 2, pp. 113-128. · DOI
  13. Fitzgerald, T. , Youngs, H. and Grootenboer, P. (2003), “Bureaucratic control or professional autonomy?: performance management in New Zealand schools”, School Leadership and Management, Vol. 32 No. 1, pp. 91-105. · DOI
  14. Flyvbjerg, B. (2006), “Five misunderstandings of case-study research”, Qualitative Inquiry, Vol. 12 No. 2, pp. 219-245. · DOI · Paper page
  15. Hargreaves, D.H. (1999), “The knowledge-creating school”, British Journal of Educational Studies, Vol. 47 No. 2, pp. 122-144. · DOI
  16. Honig, M.I. and Coburn, C. (2008), “Evidence-based decision making in school district central offices: toward a policy and research agenda”, Educational Policy, Vol. 22 No. 4, pp. 578-608. · DOI
  17. Honig, M.I. and Venkateswaran, N. (2012), “School – central office relationships in evidence use: understanding evidence use as a systems problem”, American Journal of Education, Vol. 118 No. 2, pp. 199-222. · DOI
  18. Ikemoto, G.S. and Marsh, J.A. (2007), “Understanding the promise and dynamics of data-informed leadership”, in Moss, P.A. (Ed.), Evidence and Decision Making, Blackwell Publishing, Malden, MA, pp. 105-131.
  19. Ingram, D. , Louis, K.S. and Schroeder, R.G. (2004), “Accountability policies and teacher decision making: barriers to the use of data to improve practice”, Teachers College Record, Vol. 106 No. 6, pp. 1258-1287. · DOI · Paper page
  20. Kaplan, R.S. and Miyake, D.N. (2010), “The balanced scorecard”, The School Administrator, Vol. 67 No. 2, pp. 10-15 available at: www.aasa.org/SchoolAdministratorArticle.aspx?id=11784 (accessed January 10, 2012).
  21. Kirkhart, K.E. (2000), “Reconceptualizing evaluation use: an integrated theory of influence”, in Caraceli, V.J. and Preskill, H. (Eds), The Expanding Scope of Evaluation Use, Jossey-Bass, San Francisco, CA, pp. 5-24. · DOI
  22. Knapp, M.S. , Copland, M.A. and Swinnerton, J.A. (2007), “Understanding the promise and dynamics of data-informed leadership”, in Moss, P.A. (Ed.), Evidence and Decision Making, Blackwell Publishing, Malden, MA, pp. 74-104. · DOI
  23. Lindberg, E. and Wilson, T.L. (2011), “Management by objectives: the Swedish experience in upper secondary schools”, Journal of Educational Administration, Vol. 49 No. 1, pp. 62-75. · DOI
  24. Little, J.W. (2012), “Understanding data use practice among teachers: the contribution of micro-process studies”, American Journal of Education, Vol. 118 No. 2, pp. 143-166. · DOI · Paper page
  25. Miner, A.S. , Ciuchta, M.P. and Gong, Y. (2008), “Organizational routines and organizational learning”, in Becker, M.C. (Ed.), Handbook of Organizational Routines, Edward Elgar, Cheltenham, pp. 152-186. · DOI
  26. Nonaka, I. and Takeuchi, H. (1995), The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation, Oxford, New York, NY. · DOI
  27. Paavola, S. , Lipponen, L. and Hakkarainen, K. (2004), “Models of innovative knowledge communities and three metaphors of learning”, Review of Educational Research, Vol. 74 No. 4, pp. 557-576. · DOI
  28. Spillane, J.P. (2012), “Data in practice: conceptualizing the data-based decision-making phenomena”, American Journal of Education, Vol. 118 No. 2, pp. 113-141. · DOI · Paper page
  29. Storey, A. (2002), “Performance management in schools: could the balanced scorecard help?”, School Leadership and Management, Vol. 22 No. 3, pp. 321-338. · DOI
  30. Tuckman, B.W. (1965), “Development sequence in small groups”, Psychological Bulletin, Vol. 63 No. 6, pp. 384-399. · DOI · Paper page
  31. Wayman, J.C. and Chu, V. (2009), “Preparing educators to effectively use student data systems”, in Kowalski, T.J. and Lasley, T.J. (Eds), Handbook of Data-Based Decision Making in Education, Routledge, New York, NY, pp. 89-104.
  32. Wayman, J.C. and Stringfield, S. (2006), “Technology-supported involvement of entire faculties in examination of student data for instructional improvement”, American Journal of Education, Vol. 112 No. 4, pp. 549-571. · DOI
  33. Young, V. and Kim, D.H. (2010), “Using assessments for instructional improvement: a literature review”, Education Policy Analysis Archives, Vol. 18 No. 19, pp. 1-40. · DOI
  34. Halverson, R. , Grigg, J. , Prichett, R. and Thomas, C. (2007), “The new instructional leadership: creating data-driven instructional systems in schools”, Journal of School Leadership, Vol. 17 No. 2, pp. 158-193. · DOI
  35. Moss, P.A. (Ed.) (2007), Evidence and Decision Making, Blackwell Publishing, Malden, MA.
  36. Raths, J. , Kotch, S.A. and Gorowara, C.C. (2009), “Research on teachers using data to make decisions”, in Kowalski, T.J. and Lasley, T.J. (Eds), Handbook of Data-Based Decision Making in Education, Routledge, New York, NY, pp. 207-221.
  37. Sherer, J.Z. and Spillane, J. (2011), “Constancy and change in work practice in schools: the role of organizational routines”, Teachers College Record, Vol. 113 No. 3, pp. 611-657. · DOI

Cited by

Sorted by citations

Metadata

Authors
M. Kallemeyn, L.
Venue
Journal of Educational Administration
Publisher
Emerald
Published
2014-07-01
Volume
52
Issue
4
Pages
529-548
ISSN
0957-8234 (ISSN-L)
OpenAlex
W2093072904
Cited by
21
References
3
Citations by year
2026: 1 · 2025: 1 · 2024: 5 · 2023: 2 · 2022: 2 · 2021: 4 · 2020: 2 · 2018: 1 · 2017: 2 · 2015: 1
FWCI
1.87
Type
journal article
Language
en
Open access
closed
Locations
Journal of Educational Administration (publishedVersion)
Topics
  • Social Sciences › Education › Educational Assessment and Improvement
  • Social Sciences › Education › Teacher Education and Leadership Studies
  • Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Organizational Learning and Leadership
Keywords
data use in schools, balanced scorecard, school leadership, cooperative groups, inquiry process, double-loop learning, single-loop learning, professional autonomy, performance management, tacit knowledge, explicit knowledge, instructional innovation, elementary school, internal evaluation

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