Changes in educators' data literacy during a data-based decision making intervention
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The availability of support and professional development influences data use.
“Many research have espoused the influence of the availability of support and professional development (e.g.Jimerson & Wayman, 2015;van Geel et al., 2017;Villeneuve & Bouchamma, 2023).”
From Using information and communication technology (ICT)-based data systems to support teacher data-driven decision-making: Insights from the literature (2013–2023) (2024) · cites this paper as van Geel et al., 2017
Teacher data knowledge and skills, known as data literacy, are essential factors for effective data use and DDDM.
“Teacher data knowledge and skills, known as data literacy, are essential factors for effective data use and DDDM (van Geel et al., 2017).”
From Using information and communication technology (ICT)-based data systems to support teacher data-driven decision-making: Insights from the literature (2013–2023) (2024) · cites this paper as van Geel et al., 2017
Many teachers do not feel they lack time to use data to inform their practice.
“many teachers do not feel they lack time to use data to inform their practice (van Geel et al., 2017)”
From Using information and communication technology (ICT)-based data systems to support teacher data-driven decision-making: Insights from the literature (2013–2023) (2024) · cites this paper as van Geel et al., 2017
Many studies pointed out that using technology-based data systems is influential when supporting school decision-making.
“Many studies (e.g.van Geel et al., 2017) pointed out that using technology-based data systems is influential when supporting school decisionmaking.”
From Using information and communication technology (ICT)-based data systems to support teacher data-driven decision-making: Insights from the literature (2013–2023) (2024) · cites this paper as van Geel et al., 2017
Developing a culture for using data systems for DDDM sets a clear direction for teachers and the whole school.
“Developing a culture for using data systems for DDDM sets a clear direction for teachers and the whole school (Cho & Wayman, 2014;van Geel et al., 2017).”
From Using information and communication technology (ICT)-based data systems to support teacher data-driven decision-making: Insights from the literature (2013–2023) (2024) · cites this paper as van Geel et al., 2017
Personal factors that constrain teacher data use include lack of time and preparation.
“lack of time and preparation (Jimerson & Wayman, 2015;Jimerson et al., 2021;van Geel et al., 2017)”
From Using information and communication technology (ICT)-based data systems to support teacher data-driven decision-making: Insights from the literature (2013–2023) (2024) · cites this paper as van Geel et al., 2017
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References 35 · Cited by 45
- Authors
- van Geel, M., Keuning, T., Visscher, A., & Fox, J.-P.
- Affiliations
- Maastricht University (NL)
- University of Twente (NL)
- Venue
- Teaching and Teacher Education
- Publisher
- Elsevier BV
- Published
- 2017-05
- Volume
- 64
- Pages
- 187-198
- ISSN
- 0742-051X (ISSN-L)
- OpenAlex
- W2593062439
- Cited by
- 45
- References
- 35
- Citations by year
- 2026: 1 · 2025: 6 · 2024: 3 · 2023: 8 · 2022: 9 · 2021: 4 · 2020: 8 · 2019: 4 · 2017: 1 · 2016: 1
- FWCI
- 5.72
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Teaching and Teacher Education (publishedVersion)
- Research Publications (Maastricht University) (publishedVersion, other-oa)
- University of Twente Research Information (submittedVersion, other-oa)
- Research Publications (Maastricht University) (submittedVersion)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Educational Assessment and Improvement
- Physical Sciences › Computer Science › Information Systems › Digital literacy in education
- Social Sciences › Education › Teacher Education and Leadership Studies
- Keywords
- data literacy, data-driven decision making, educators, student monitoring system, school improvement, educational data, teacher professional development, school leaders, knowledge gaps
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.
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