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Data-based decision-making for school improvement

Research insights and gaps

Bibliographic Data

ID23375720
AuthorsKim Schildkamp (0000-0001-6154-6857, ELAN, University of Twente, BMS/ELAN, Enschede, The Netherlands, corresponding author)
Year2019
Volume61
Issue3
Pages257-273
Publication date2019-07-03
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueEducational Research (JOURNAL)
Journal identifiersISSN: 0013-1881 • E-ISSN: 1469-5847
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/00131881.2019.1625716
OpenAlexW2951965635
LanguageEN
Citations received72
References cited54

Background: Data-based decision-making in education often focuses on the use of summative assessment data in order to bring about improvements in student achievement. However, many other sources of evidence are available across a wide range of indicators. There is potential for school leaders, teachers and students to use these diverse sources more fully to support their work on a range of school improvement goals.Purpose and sources of evidence: To explore data-based decision-making for school improvement, this theoretical paper discusses recent research and literature from different areas of data use in education. These areas include the use of formative assessment data, educational research study findings and ‘big data’. In particular, the discussion focuses on how school leaders and teachers can use different sources of data to improve the quality of education.Main argument: Based on the literature reviewed, an iterative model of data use for school improvement is described, consisting of defining goals for data use, collecting different types of data or evidence (e.g. formal data, informal data, research evidence and ‘big data’), sense-making, taking improvement actions and evaluation. Drawing on the literature, research insights are discussed for each of these components, as well as identification of the research gaps that still exist. It is noted that the process of data use does not happen in isolation: data use is influenced by system, organisation and team/individual level factors.Conclusions: When it comes to using data to improve the quality of teaching and learning, it is evident that some of the most important enablers and barriers include data literacy and leadership. However, what is less well understood is how we can promote the enablers and remove the barriers to unlock, more fully, the potential of data use. Only then can data use lead to sustainable school improvement.

Argument (complex analysis) · Data collection · Data quality · Educational research · Formative assessment · Identification (biology) · Mathematics education · Process (computing) · Quality (philosophy) · Sociology · Summative assessment · Computer Science · Educational Assessment and Improvement · Engineering · Evaluation and Performance Assessment · Psychology

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Unique citing works72
Citations per year12
Citation span2020 - 2026 (7)
Citation velocitycurrent
Highly citedNo
Citation typesNeutral: 71

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