What do the Data Say? A Case Study with Pre-Service Teachers
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Published
Apr 28, 2024
Abstract
Data literacy, which is considered among 21st century skills, is becoming increasingly important. In particular, incentives for data-driven decision-making draw attention to data literacy. Data literacy is also critical for teachers who need to use data in educational settings. Therefore, there is a need for data on whether teachers are equipped with data literacy competencies before their service. In this study, pre-service teachers’ data literacy competencies were examined. The case study approach was used in the study, which was conducted as qualitative research. The study group consisted of 61 pre-service teachers studying in three different undergraduate programs. Data were collected through the “What do the data say?” activity instrument. Rubrics were used to analyze the data. The findings of the study showed that while there was no difference in terms of using data and data communication, there was a statistically significant difference between the programs in terms of data recognition, comparing data and establishing relationships between data competencies, as well as total data literacy. In addition, it was found that the majority of pre-service teachers were partially inadequate or inadequate in terms of using data, data communication and total data literacy. Nearly half of them were partially inadequate or inadequate in comparing data and establishing relationships between data. The results indicated that pre-service teachers have certain deficiencies regarding data literacy.
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Keywords
Data, Data Literacy Competencies, Pre-Service Teachers
References
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Ertaş-Kılıç, H. (2022b). Data analysis. In S. Temel Aslan (Ed.), Developing data literacy in education with sample activities. Nobel Publishing. ISBN: 9786254178528. pp. 221-248.
Erwin, R. W. (2015). Data literacy: Real-world learning through problem-solving with data sets. American Secondary Education, 43(2):18-26. Available at: https://www.jstor.org/stable/43694208
Floridi, L., Taddeo, M. (2016). What is data ethics? Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2083):20160360. DOI: https://doi.org/10.1098/rsta.2016.0360
Fontichiaro, K., Lennex, A., Hoff, T., Hovinga, K., Oehrli, J. A. (Ed.) (2017). Data literacy in the real world: Conversations & case studies.
Michigan Publishing, University of Michigan Library. Available at: https://quod.lib.umich.edu/m/maize/mpub9970368/1:1/--data-literacy-in-the-real-world-conversations-case-studies?rgn=div1;view=toc
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Howell, D. C. (1997). Statistical methods for psychology. Wadsworth Publishing. ISBN: 9781111835484.
Hunter-Thomson, K. (2019). Data literacy 101: What do really mean by “data”? Science Scope, 43(2):84-95. Available at: https://www.proquest.com/scholarly-journals/data-literacy-101-what-do-we-really-mean/docview/2277983743/se-2?accountid=38938
Hunter-Thomson, K. (2020). Data literacy 101: What can we actually claim from our data? Science Scope, 43(6):20-26. Available at: https://www.proquest.com/scholarly-journals/data-literacy-101-what-can-we-actually-claim-our/docview/2376676255/se-2?accountid=38938
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Jesilevska, S. (2017). Data quality dimensions to ensure optimal data quality. The Romanian Economic Journal, 63:89-103. Available at: http://www.rejournal.eu/sites/rejournal.versatech.ro/files/articole/2017-04-02/3443/6jesilevska.pdf
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Kandilli Observatory, and the Earthquake Research Institute (2021). Earthquake maps, graphics and tables in 2020. Available at: http://www.koeri.boun.edu.tr/sismo/2/deprem-verileri/yillik-deprem-haritalari/2020-yili-deprem-harita-grafik-ve-tablolari/
Keray-Dinçel, B. (2022). Data communication. In S. Temel-Aslan (Ed.), Developing data literacy in education with sample activities. Nobel Publishing. ISBN: 9786254178528. pp. 297-338.
Kjelvik, M. K., Schultheis, E. H. (2019). Getting messy with authentic data: Exploring the potential of using data from scientific research to support student data literacy. CBE-Life Sciences Education, 18(2):1-8. DOI: https://doi.org/10.1187/cbe.18-02-0023
Kutlu, Ö., Doğan, C. H., Karakaya, İ. (2010). Determining student success. Pegem Academy. ISBN: 9786055885014.
Mandinach E. B., Gummer E. S., Muller R. (2011). The complexities of integrating data-driven decision making into professional preparation in schools of education: It’s harder than you think. CNA Education/Education Northwest/WestEd. Available at: https://educationnorthwest.org/sites/default/files/gummer-mandinach-report-summary.pdf
Mandinach, E. B., Gummer, E. S. (2013). A systemic view of implementing data literacy in educator preparation. Educational Researcher, 42(1):30-37. DOI: https://doi.org/10.3102/0013189X12459803
Mandinach, E. B., Gummer, E. S. (2016). Every teacher should succeed with data literacy. Phi Delta Kappan, 97(8):43-46. DOI: https://doi.org/10.1177/0031721716647018
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How to Cite
Temel-Aslan, S., Ertaş-Kılıç, H., Unlu, M., & Keray-Dinçel, B. (2024). What do the Data Say? A Case Study with Pre-Service Teachers. Science Insights Education Frontiers, 21(2), 3435–3460. https://doi.org/10.15354/sief.24.or567
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