Research Article
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Year 2023, Volume: 16 Issue: 1, 199 - 217, 31.01.2023
https://doi.org/10.30831/akukeg.1134207

Abstract

References

  • Sources marked with an asterisk (*) indicate studies included in the meta-analysis.
  • *Abrams, L. M., Varier, D., & Mehdi, T. (2021). The intersection of school context and teachers’ data use practice: Implications for an integrated approach to capacity building. Studies in Educational Evaluation, 69, 1-13. https://doi.org/10.1016/j.stueduc.2020.100868
  • Açıkel, C. (2009). Meta-analysis and its place in evidence based medicine. Bulletin of Clinical Psychopharmacology,19(2), 164-172.
  • Albrect, N., Lou, M., & Neill, S. (2014). The importance of data literacy in leadership education: Factors related to data use in instructional leadership. 03 June 2014, 10th International Academic Conference, Vienna.
  • Athanases, S., Wahleithner, J., & Bennett, L. (2012). Learning to attend to culturally and linguistically diverse learners through teacher inquiry in teacher education. Teachers College Record, 114(7), 1-50.
  • Barutçugil, İ. (2002). Bilgi yönetimi. Kariyer Publishing
  • Başol, G., & Johanson, G. (2009). Effectiveness of frequent testing over achievement: A meta-analysis study. International Journal of Human Sciences, 6(2), 99-121.
  • Bernhardt, V. L. (2009). Data use: Data-driven decision making takes a big-picture view of the needs of teachers and students. Journal of Staff Development, 30(1), 24-27.
  • *Bettesworth, L. R. (2006). Administrators' use of data to guide decision-making. [Doctoral dissertation]. University of Oregon, Eugene, OR. Borenstein, M., Hedges, L.V., Higgins, J.P.T., & Rothstein H. (2013). Comprehensive meta-analysis. Englewood, NJ: Biostat.
  • Carey, M., Grainger, P., & Christie, M. (2018). Preparing preservice teachers to be data literate: a queensland case study. Asia-Pacific Journal of Teacher Education, 46(3), 267-278. https://doi.org/10.1080/1359866X.2017.1402860
  • Childress, M. (2009). Data-driven decision making: The development and validation of an instrument to measure principals’ practices. Academic Leadership: The Online Journal, 7(1), 67-75.
  • Coburn, C. E., & Turner, E. O. (2011). Research on data use: A framework and analysis. Measurement: Interdisciplinary Research & Perspective, 9(4), 173–206. https://doi.org/10.1080/15366367.2011.626729
  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences. New York: Academic Pres.,
  • Cooper, H., Hedges, L. V., & Valentine, J. C. (2009). The handbook of research synthesis and meta-analysis. (2nd edition). New York: Russell Sage Publication.
  • Cowie, B., & Cooper, B. (2017). Exploring the challenge of developing student teacher data literacy. Assessment in Education: Principles, Policy and Practice, 24(2), 147-163. https://doi.org/10.1080/0969594X.2016.1225668
  • Cumming, G. (2012). Understanding the new statistics. New York: Routledge, Taylor and Francis Group.
  • Çarkungöz, E., & Ediz, B. (2009). Meta-analysis. Uludag University Journal of Faculty Veterinary Medicine, 28(1), 33-37.
  • Darling-Hammond, L., & Orphanos, S. (2006). Leadership development in California. Retrieved from https://cepa.stanford.edu/sites/default/files/12-Darling-Hammond%283-07%29.pdf
  • Datnow, A., Park, V., & Wohlstetter, P. (2007). Achieving with data: How highperforming school systems use data to improve instruction for elementary students. Los Angeles, CA: Center on Educational Governance, Rossier School of Education, University of Southern California.
  • Dejear, M. L. (2016). A study of how culture, collaboration, and advocacy influence data-driven decision making at community colleges. [Doctor of philosophy]. Iowa State University, Ames, Iowa.
  • DeLuca, C., & Bellara, A. (2013). The current state of assessment education aligning policy, standards, and teacher education curriculum. Journal of Teacher Education, 64(4), 356-372. https://doi.org/10.1177/0022487113488144
  • Doğan, E. (2021). Evaluating the data-driven decision making process in school management according to the views of the administrators. [Unpublished doctoral dissertation]. Gazi University, Ankara.
  • Dunn, K., Airola, D., Lo, W., & Garrison, M. (2013). What teachers think about what they can do with data: Development and validation of the data driven decision-making efficacy and anxiety inventory. Contemporary Educational Psychology, 38(1), 87-98. http://dx.doi.org/10.1016/j.cedpsych.2012.11.002
  • Earl, L., & Katz, S. (2002). Leading schools in a data rich world. In K. Leithwood and P. Hallinger (Eds.), Second international handbook of educational leadership and administration. Kluwer.
  • *Ebbeler, J., Poortman, C., Schildkamp, K., & Pieters, J. (2016). The effects of a data use intervention on educators’ satisfaction and data literacy. Educational Assessment, Evaluation and Accountability, 29(1), 83-105. https://link.springer.com/article/10.1007/s11092-016-9251-z
  • Edwards, J., Lyons, C., & Jost, M. L. (1997). Quality instruction based on data to better meet student needs. Paper presented at the Annual Meeting of the American Educational Research Association (March 24-28). ERIC Document Reproduction NO. ED440119.
  • Ellis, P. D. (2010). The essential guide to effect sizes: Statistical power, meta-analysis, and the interpretation of research results. Cambridge: Cambridge University Press.
  • Faber, J. M., & Visscher, A. J. (2014). Digitale leerlingvolgsystemen: Een review van de effecten op leerprestaties. 4W. Retrieved from http://www.kennisnet.nl/onder zoek/alle-onderzoeken/digitale-leerlingvolgsystemen-een-review-van-deeffecten-op-leerprestaties/
  • Farley-Ripple, E., & Buttram, J. (2014). Developing collaborative data use through professional learning communities: Early lessons from Delaware. Studies in Educational Evaluation, 42, 41–53. http://dx.doi.org/10.1016%2Fj.stueduc.2013.09.006
  • Feldman, J. & Tung, R. (2001). Whole school reform: how schools use the data-based inquiry and decision-making process. Seattle, WA: American Educational Research Association.
  • Gambell, T. (2004). Teachers working around large-scale assessment: Reconstructing professionalism and professional development. English Teaching: Practice and Critique, 3(2), 48-73.
  • Gesel, S. A., LeJeune, L. M., Chow, J. C., Sinclair, A. C., & Lemons, C. J. (2020). A meta-analysis of the impact of professional development on teachers’ knowledge, skill, and self-efficacy in data-based decision-making. Journal of Learning Disabilities, 1–15. https://doi.org/10.1177/0022219420970196
  • *Green, J. L., Schmitt-Wilson, S., Versland, T., Kelting-Gibson, L., & Nollmeyer, G. E. (2016). Teachers and data literacy: A blueprint for professional development to foster data driven decision making. Journal of Continuing Education and Professional Development, 3(1), 14-32. http://dx.doi.org/10.7726/jcepd.2016.1002
  • Gummer, E., & Mandinach, E. (2015). Building a conceptual framework for data literacy. Teachers College Record, 117(4), 1-22.
  • Harris, N. (2011). The impact of professional development in data-based decision making on the teaching practices of educators. [Doctoral dissertation]. Walden University College of Education, Walden.
  • Hunter, J. E., & Schmidt, F. L. (1990). Methods of meta-analysis: Correcting error and bias in research findings. Thousand Oaks, CA, US: Sage Publications, Inc.
  • Jacobs, J., Gregory, A., Hoppey, D., & Yendol-Hoppey, D. (2009). Data literacy: Understanding teachers' data use in a context of accountability and response to intervention. Action in Teacher Education, 31(3), 41-55. https://doi.org/10.1080/01626620.2009.10463527
  • *Jimenez, B. A., Mims, P. J., & Browder, D. M. (2012). Data-based decisions guidelines for teachers of students with severe ıntellectual and developmental disabilities. Education and Training in Autism and Developmental Disabilities, 47(4), 407–413.
  • Johnson, T. (2015). Professional development effects on teachers’ perceptions in analyzing and using student data. [Doctoral dissertation]. Walden University College of Education, USA.
  • Jung, P. G., McMaster, K. L., Kunkel, A., Shin, J., & Stecker, P. M. (2018). Effects of data-based individualization for students with intensive learning needs: A meta-analysis. Learning Disabilities Research & Practice, 33(3), 144–155. https://doi.org/gd4g8m
  • Kerr, K. A., Marsh, J. A., Ikemoto, G. S., Darilek, H., & Barney, H. (2006). Strategies to promote data use for instructional improvement: actions, outcomes, and lessons from three urban districts. American Journal of Education, 112(4), 496-520.
  • Keuning, T., Van Gell, M., & Visscher, A. (2017). Why a Data-Based Decision-Making Intervention Works in Some Schools and Not in Others. Learning Disabilities Research & Practice, 32(1), 1–14. http://dx.doi.org/10.1111/ldrp.12124
  • Killion, J., & Bellamy, G. T. (2000). On the job: Data analysts focus school improvement efforts. Journal of Staff Development, 21(1), 27-31. Khan, H. R., Kim, J., & Chang, C. H. (2018). Toward an understanding of data literacy. http://hdl.handle.net/2142/100243
  • *Kippers, W. B., Poortman, C. L., Schildkamp, K., & Visscher, A. J. (2018). Data literacy: What do educators learn and struggle with during a data use intervention? Studies in Educational Evaluation, 56, 31-21. https://doi.org/10.1016/j.stueduc.2017.11.001
  • LaPointe-McEwan, D., DeLuca, C., & Klinger, D. (2017). Supporting evidence use in networked professional learning: the role of the middle leader. Educational Research, 59(2), 136-153. https://doi.org/10.1080/00131881.2017.1304346
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A Meta-Analysis Study on Data Literacy Education For School Administrators and Teachers

Year 2023, Volume: 16 Issue: 1, 199 - 217, 31.01.2023
https://doi.org/10.30831/akukeg.1134207

Abstract

This meta-analysis study aimed to examine the effect of data literacy education, which affects data-based decision processes, on data use knowledge and skills of school administrators and teachers. Therefore, theses of data literacy education for school administrators and teachers and relevant studies in peer-reviewed journals were examined through several databases. The study was conducted using the Comprehensive Meta-Analysis (CMA) software, using a total of eight studies published between 2006-2021. The results revealed that the selected studies were heterogeneous. Therefore, a random effects model was applied in the study. The overall effect size value of data literacy education was calculated as 2.16, suggesting that a data literacy education makes a positive high contribution to data use knowledge and skills of school administrators and teachers. The subgroup analyzes conducted to determine the source of heterogeneity in results have shown that data literacy education did not differ by type and country of publications, but varied by type of participants, where studies conducted with mixed participants had high effect values. Based on these results, it can be suggested that data literacy education given to school administrators and teachers should be expanded in all countries and education levels, and a meta-analysis of studies conducted in correlation between data literacy and different variables.

References

  • Sources marked with an asterisk (*) indicate studies included in the meta-analysis.
  • *Abrams, L. M., Varier, D., & Mehdi, T. (2021). The intersection of school context and teachers’ data use practice: Implications for an integrated approach to capacity building. Studies in Educational Evaluation, 69, 1-13. https://doi.org/10.1016/j.stueduc.2020.100868
  • Açıkel, C. (2009). Meta-analysis and its place in evidence based medicine. Bulletin of Clinical Psychopharmacology,19(2), 164-172.
  • Albrect, N., Lou, M., & Neill, S. (2014). The importance of data literacy in leadership education: Factors related to data use in instructional leadership. 03 June 2014, 10th International Academic Conference, Vienna.
  • Athanases, S., Wahleithner, J., & Bennett, L. (2012). Learning to attend to culturally and linguistically diverse learners through teacher inquiry in teacher education. Teachers College Record, 114(7), 1-50.
  • Barutçugil, İ. (2002). Bilgi yönetimi. Kariyer Publishing
  • Başol, G., & Johanson, G. (2009). Effectiveness of frequent testing over achievement: A meta-analysis study. International Journal of Human Sciences, 6(2), 99-121.
  • Bernhardt, V. L. (2009). Data use: Data-driven decision making takes a big-picture view of the needs of teachers and students. Journal of Staff Development, 30(1), 24-27.
  • *Bettesworth, L. R. (2006). Administrators' use of data to guide decision-making. [Doctoral dissertation]. University of Oregon, Eugene, OR. Borenstein, M., Hedges, L.V., Higgins, J.P.T., & Rothstein H. (2013). Comprehensive meta-analysis. Englewood, NJ: Biostat.
  • Carey, M., Grainger, P., & Christie, M. (2018). Preparing preservice teachers to be data literate: a queensland case study. Asia-Pacific Journal of Teacher Education, 46(3), 267-278. https://doi.org/10.1080/1359866X.2017.1402860
  • Childress, M. (2009). Data-driven decision making: The development and validation of an instrument to measure principals’ practices. Academic Leadership: The Online Journal, 7(1), 67-75.
  • Coburn, C. E., & Turner, E. O. (2011). Research on data use: A framework and analysis. Measurement: Interdisciplinary Research & Perspective, 9(4), 173–206. https://doi.org/10.1080/15366367.2011.626729
  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences. New York: Academic Pres.,
  • Cooper, H., Hedges, L. V., & Valentine, J. C. (2009). The handbook of research synthesis and meta-analysis. (2nd edition). New York: Russell Sage Publication.
  • Cowie, B., & Cooper, B. (2017). Exploring the challenge of developing student teacher data literacy. Assessment in Education: Principles, Policy and Practice, 24(2), 147-163. https://doi.org/10.1080/0969594X.2016.1225668
  • Cumming, G. (2012). Understanding the new statistics. New York: Routledge, Taylor and Francis Group.
  • Çarkungöz, E., & Ediz, B. (2009). Meta-analysis. Uludag University Journal of Faculty Veterinary Medicine, 28(1), 33-37.
  • Darling-Hammond, L., & Orphanos, S. (2006). Leadership development in California. Retrieved from https://cepa.stanford.edu/sites/default/files/12-Darling-Hammond%283-07%29.pdf
  • Datnow, A., Park, V., & Wohlstetter, P. (2007). Achieving with data: How highperforming school systems use data to improve instruction for elementary students. Los Angeles, CA: Center on Educational Governance, Rossier School of Education, University of Southern California.
  • Dejear, M. L. (2016). A study of how culture, collaboration, and advocacy influence data-driven decision making at community colleges. [Doctor of philosophy]. Iowa State University, Ames, Iowa.
  • DeLuca, C., & Bellara, A. (2013). The current state of assessment education aligning policy, standards, and teacher education curriculum. Journal of Teacher Education, 64(4), 356-372. https://doi.org/10.1177/0022487113488144
  • Doğan, E. (2021). Evaluating the data-driven decision making process in school management according to the views of the administrators. [Unpublished doctoral dissertation]. Gazi University, Ankara.
  • Dunn, K., Airola, D., Lo, W., & Garrison, M. (2013). What teachers think about what they can do with data: Development and validation of the data driven decision-making efficacy and anxiety inventory. Contemporary Educational Psychology, 38(1), 87-98. http://dx.doi.org/10.1016/j.cedpsych.2012.11.002
  • Earl, L., & Katz, S. (2002). Leading schools in a data rich world. In K. Leithwood and P. Hallinger (Eds.), Second international handbook of educational leadership and administration. Kluwer.
  • *Ebbeler, J., Poortman, C., Schildkamp, K., & Pieters, J. (2016). The effects of a data use intervention on educators’ satisfaction and data literacy. Educational Assessment, Evaluation and Accountability, 29(1), 83-105. https://link.springer.com/article/10.1007/s11092-016-9251-z
  • Edwards, J., Lyons, C., & Jost, M. L. (1997). Quality instruction based on data to better meet student needs. Paper presented at the Annual Meeting of the American Educational Research Association (March 24-28). ERIC Document Reproduction NO. ED440119.
  • Ellis, P. D. (2010). The essential guide to effect sizes: Statistical power, meta-analysis, and the interpretation of research results. Cambridge: Cambridge University Press.
  • Faber, J. M., & Visscher, A. J. (2014). Digitale leerlingvolgsystemen: Een review van de effecten op leerprestaties. 4W. Retrieved from http://www.kennisnet.nl/onder zoek/alle-onderzoeken/digitale-leerlingvolgsystemen-een-review-van-deeffecten-op-leerprestaties/
  • Farley-Ripple, E., & Buttram, J. (2014). Developing collaborative data use through professional learning communities: Early lessons from Delaware. Studies in Educational Evaluation, 42, 41–53. http://dx.doi.org/10.1016%2Fj.stueduc.2013.09.006
  • Feldman, J. & Tung, R. (2001). Whole school reform: how schools use the data-based inquiry and decision-making process. Seattle, WA: American Educational Research Association.
  • Gambell, T. (2004). Teachers working around large-scale assessment: Reconstructing professionalism and professional development. English Teaching: Practice and Critique, 3(2), 48-73.
  • Gesel, S. A., LeJeune, L. M., Chow, J. C., Sinclair, A. C., & Lemons, C. J. (2020). A meta-analysis of the impact of professional development on teachers’ knowledge, skill, and self-efficacy in data-based decision-making. Journal of Learning Disabilities, 1–15. https://doi.org/10.1177/0022219420970196
  • *Green, J. L., Schmitt-Wilson, S., Versland, T., Kelting-Gibson, L., & Nollmeyer, G. E. (2016). Teachers and data literacy: A blueprint for professional development to foster data driven decision making. Journal of Continuing Education and Professional Development, 3(1), 14-32. http://dx.doi.org/10.7726/jcepd.2016.1002
  • Gummer, E., & Mandinach, E. (2015). Building a conceptual framework for data literacy. Teachers College Record, 117(4), 1-22.
  • Harris, N. (2011). The impact of professional development in data-based decision making on the teaching practices of educators. [Doctoral dissertation]. Walden University College of Education, Walden.
  • Hunter, J. E., & Schmidt, F. L. (1990). Methods of meta-analysis: Correcting error and bias in research findings. Thousand Oaks, CA, US: Sage Publications, Inc.
  • Jacobs, J., Gregory, A., Hoppey, D., & Yendol-Hoppey, D. (2009). Data literacy: Understanding teachers' data use in a context of accountability and response to intervention. Action in Teacher Education, 31(3), 41-55. https://doi.org/10.1080/01626620.2009.10463527
  • *Jimenez, B. A., Mims, P. J., & Browder, D. M. (2012). Data-based decisions guidelines for teachers of students with severe ıntellectual and developmental disabilities. Education and Training in Autism and Developmental Disabilities, 47(4), 407–413.
  • Johnson, T. (2015). Professional development effects on teachers’ perceptions in analyzing and using student data. [Doctoral dissertation]. Walden University College of Education, USA.
  • Jung, P. G., McMaster, K. L., Kunkel, A., Shin, J., & Stecker, P. M. (2018). Effects of data-based individualization for students with intensive learning needs: A meta-analysis. Learning Disabilities Research & Practice, 33(3), 144–155. https://doi.org/gd4g8m
  • Kerr, K. A., Marsh, J. A., Ikemoto, G. S., Darilek, H., & Barney, H. (2006). Strategies to promote data use for instructional improvement: actions, outcomes, and lessons from three urban districts. American Journal of Education, 112(4), 496-520.
  • Keuning, T., Van Gell, M., & Visscher, A. (2017). Why a Data-Based Decision-Making Intervention Works in Some Schools and Not in Others. Learning Disabilities Research & Practice, 32(1), 1–14. http://dx.doi.org/10.1111/ldrp.12124
  • Killion, J., & Bellamy, G. T. (2000). On the job: Data analysts focus school improvement efforts. Journal of Staff Development, 21(1), 27-31. Khan, H. R., Kim, J., & Chang, C. H. (2018). Toward an understanding of data literacy. http://hdl.handle.net/2142/100243
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There are 80 citations in total.

Details

Primary Language English
Subjects Other Fields of Education
Journal Section Articles
Authors

Emine Doğan 0000-0002-1333-3096

Publication Date January 31, 2023
Submission Date June 22, 2022
Published in Issue Year 2023 Volume: 16 Issue: 1

Cite

APA Doğan, E. (2023). A Meta-Analysis Study on Data Literacy Education For School Administrators and Teachers. Journal of Theoretical Educational Science, 16(1), 199-217. https://doi.org/10.30831/akukeg.1134207