VERİ OKURYAZARLIĞI ÖLÇEĞİ’NİN TÜRKÇEYE UYARLAMASI: GEÇERLİK VE GÜVENİRLİK ÇALIŞMASI

Öz

Bu araştırmanın amacı okul yöneticilerinin ve öğretmenlerin verileri kullanma ve anlamlandırma becerilerini anlamak için geliştirilen Veri Okuryazarlığı Ölçeği’ni (VOY-Ö) Türkçe’ye uyarlamaktır. Araştırmanın çalışma gruplarını 2021-2022 öğretim yılında Kahramanmaraş ili Onikişubat merkez ilçesinde ilkokul, ortaokul ve lisede görev yapan okul yöneticisi ve öğretmenler oluşturmuştur. Araştırmanın analizleri, amaçlı örnekleme yöntemlerinden maksimum çeşitlilik ve kolay ulaşılabilir uygun örnekleme yöntemi esas alınarak belirlenen çalışma grubu-1’de 207, çalışma grubu-2’de 280 katılımcı ile gerçekleştirilmiştir. Ölçeğin yapı geçerliği için Açımlayıcı Faktör Analizi (AFA) ve Doğrulayıcı Faktör Analizi (DFA) kullanılmıştır. Ölçümlerin güvenirliğinin belirlenmesinde ise Cronbach alpha iç tutarlık katsayıları incelenmiştir. AFA ve DFA sonuçları Veri Okuryazarlığı Ölçeğini (VOY-Ö) Türk kültüründe ve Mandinach and Gummer’in teorik çerçevesiyle uyumlu dört faktörde 14 madde ile doğrulamıştır. Sonuç olarak araştırmanın bulguları bu ölçeğin eğitimcilerin veri okuryazarlığı düzeyini ölçmek için büyük ölçüde kabul edilebilir özelikler gösterdiğini ortaya koymuştur.

Anahtar Kelimeler

• veri
• veri kullanımı
• veri okuryazarlığı
• öğretmen
• okul yöneticisi

ADAPTATION OF DATA LITERACY SCALE INTO TURKISH: A VALIDITY AND RELIABILITY STUDY

Öz

The aim of this research is to adapt the Data Literacy Scale (DLS), which was developed to understand the skills of school principals and teachers in using and interpreting data, into Turkish. The study groups of the research consisted of school principals and teachers working in primary, secondary, and high schools in the central district of Kahramanmaraş province Onikisubat in the 2021-2022 academic year. The analysis of the research was carried out with 207 participants in study group-1 and 280 participants in study group-2, which was determined on the basis of maximum diversity and easily accessible sampling method, which is one of the purposive sampling methods. Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were used for the construct validity of the scale. Cronbach alpha internal consistency coefficients were examined to determine the reliability of the measurements. EFA and CFA results confirmed the Data Literacy Scale (DLS) with 14 items in four factors in Turkish culture and compatible with the theoretical framework of Mandinach and Gummer. As a result, the findings of the study revealed that this scale showed largely acceptable features to measure the data literacy level of educators.

Anahtar Kelimeler

• data
• data usage
• data literacy
• teacher
• school principal

Kaynakça

1. 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
2. Armstrong, J., & Anthes, K. (2001). How data can help: Putting information to work to raise student achievement. American School Boards Journal, 188(11), 38-41.
3. 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.
4. Barutçugil, İ. (2002). Bilgi yönetimi. İstanbul: Kariyer Yayıncılık
5. Brown, T. A. (2006). Confirmatory factor analysis for applied research. New York: Guilford.
6. Büyüköztürk, Ş. (2010). Sosyal bilimler için veri analizi el kitabı (12. Baskı). Ankara: Pegem.
7. 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.
8. Çokluk, Ö., Şekercioğlu, G., & Büyüköztürk, Ş. (2012). Sosyal bilimler için çok değişkenli istatistik: SPSS ve LISREL uygulamaları. Ankara: Pegem.

9. Cronbach, L.J. (1990). Essentials of psychological testing (5th ed.). New York: Harper Collins Publishers.
10. Datnow, A. & Hubbard, L. (2015). Teachers’ use of assessment data to inform instruction: lessons from the past and prospects for the future. Teachers College Record, 117, 1-26.
11. Datnow, A., Park, V., & Wohlstetter, P. (2007). Achieving with data: How high performing 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.
12. Doğan, E. (2021). Okul yönetiminde veriye dayalı karar verme sürecinin yönetici görüşlerine göre değerlendirilmesi. (Yayımlanmamış doktora tezi). Gazi Üniversitesi Eğitim Bilimleri Enstisüsü, Ankara.
13. Doğan, E., & Ottekin Demirbolat, A. (2021). Data-driven decision-making in schools scale: a study of validity and reliability. International Journal of Curriculum and Instruction, 13(1), 507-523.
14. Dunn, K, Airola, D., & Lo, W. (2013). Becoming data driven: the influence of teachers’sense of efficacy on concerns related to data-driven decision-making. The Journal of Experimental Education, 81(2), 222-241. http://dx.doi.org/10.1080/00220973.2012.699899
15. Earl, L., & Fullan, M. (2003). Using data in leadership for learning. Cambridge Journal of Education, 33(3), 383-94.
16. 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. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.78.1885&rep=rep1&type=pdf
17. Field, A. (2009). Discovering statistics using SPSS (2nd edition). London: Sage.
18. Fielding, J., & Gilbert, N. (2006). Understanding social statistics. London: Sage.
19. Fullan, M. (2008). The six secrets of change: What the best leaders do to help their organizations survive and thrive (1st ed.). San Francisco: Jossey-Bass . Gagné, P., & Hancock, G. R. (2006). Measurement model quality, sample size, and solution propriety in confirmatory factor analysis. Multivariate Behavioral Research, 41(1), 65-83. doi:10.1207/s15327906mbr4101_5
20. Gambell, T. (2004). Teachers working around large-scale assessment: Reconstructing professionalism and professional development. English Teaching: Practice and Critique, 3(2), 48-73.
21. Gummer, E., & Mandinach, E. (2015). Building a conceptual framework for data literacy. Teachers College Record, 117(4), 1-22. https://www-tcrecord-org.ezp.waldenulibrary.org/library
22. Hamilton, L., Halverson, R., Jackson, S., Mandinach, E., Supovitz, J., & Wayman, J. (2009). Using student achievement data to support instructional decision making (NCEE 2009-4067). Washington, DC: National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences, US Department of Education
23. Hooper, D., Coughlan, J., & Mullen, M. (2008, September). Evaluating model fit: a synthesis of the structural equation modelling literature. In 7th European Conference on research methodology for business and management studies (pp. 195–200).
24. Ikemoto, G. S., & Marsh, J. A. (2007). Different conceptions of data-driven decision making. Yearbook of the National Society for the Study of Education, 106(1), 105-132.
25. Jöreskog, K. G., & Sörbom, D. (1993). LISREL 8: Structural equation modeling with the SIMPLIS command language. Chicago: Scientific Software International.
26. Kaufman, T. E., Graham, C. R., Picciano, A. G., Popham, J. A., & Wiley, D. (2014). Data-driven decision making in the K-12 classroom. In J. M. Spector, M. D. Merrill, J. Elen, & M. J. Bishop (Eds.), Handbook of research on educational communications and technology (pp. 337–346). Springer. http://dx.doi.org/10.1007/978-1-4614-3185-5_27
27. 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(3), 496-520.
28. Killion, J., & Bellamy, G. T. (2000). On the job: Data analysts focus school improvement efforts. Journal of Staff Development, 21(1), 27-31.
29. Kline, R. B. (2013). Exploratory and confirmatory factor analysis. In Y. Petscher & C. Schatsschneider (Eds.), Applied quantitative analysis in the social sciences (pp. 171-207). Routledge.
30. Love, N., Stiles, K. E., Mundry, S., & DiRanna, K. (2008). A data coach's guide to improving learning for all students: Unleashing the power of collaborative inquiry. Corwin Press.
31. MacCallum, R. C., Widaman, K. F., Zhang, S., & Hong, S. (1999). Sample size in factor analysis. Psychological Methods, 4(1), 84-99. https://doi.org/10.1037/1082-989X.4.1.84
32. Mandinach, E. B. (2012). A perfect time for data use: Using data-driven decision making to inform practice. Educational Psychologist, 47(2), 71–85. http://dx.doi.org/10.1080/00461520.2012.667064
33. Mandinach, E., & Gummer, E. (2013). A systemic view of implementing data literacy in educator preparation. Educational Researcher, 42(1), 30-37. http://dx.doi.org/10.3102/0013189X12459803
34. Mandinach, E. B., & Gummer, E. S. (2016). What does it mean for teachers to be data literate: Laying out the skills, knowledge, and dispositions. Teaching and Teacher Education, 60, 452-457. http://dx.doi.org/10.1016/j.tate.2016.07.011
35. Mandinach, E. B., Kahl, S., Parton, B. M., & Carson, R. M. (2014, July). What's the difference between assessment literacy and data literacy? Washington, DC: In Webinar for the data quality Campaign,
36. Marsh, J. A., Pane, J. F., & Hamilton, L. S. (2006). Making sense of data-driven decision making in education: Evidence from recent RAND research. https://www.rand.org/pubs/occasional_papers/OP170/
37. Means, B. (2005). Evaluating the impact of implementing student information and instructional management systems. Menlo Park, CA: U.S. Department of Education, SRI International Policy and Program Studies Service.
38. Means, B., Chen, E., DeBarger, A., & Padilla, C. (2011). Teachers’ ability to use data to inform instruction: Challenges and supports. U.S. Department of Education, Office of Planning, Evaluation, and Policy Development.
39. Means, B., Padilla, C., DeBarger, A., & Bakia, M. (2009). Implementing data-informed decision-making in schools: Teacher access, supports, and use. SRI International, Menlo Park, CA.; Department of Education, Washington, DC. Office of Planning, Evaluation.
40. Millî Eğitim Bakanlığı, (2018). 2023 Vizyon Belgesi. http://2023vizyonu.meb.gov.tr/doc/2023_EGITIM_VIZYONU.pdf
41. Myers, N. D., Ahn, S., & Jin, Y. (2011). Sample size and power estimates for a confirmatory factor analytic model in exercise and sport: A Monte Carlo approach. Research Quarterly for Exercise and Sport, 82(3), 412-423.
42. Ross, E. (2017). State teacher policy yearbook: National summary, national council on teacher quality. Washington, DC. https://www.nctq.org/publications/2017-State-Teacher-Policy-Yearbook.
43. Schildkamp, K., & Kuiper, W. (2010). Data-informed curriculum reform: Which data, what purposes, and promoting and hindering factors. Teaching and Teacher Education, 26(3), 482-496. https://doi.org/10.1016/j.tate.2009.06.007
44. Schildkamp, K., & Lai, M. K. (2013). Data-based decision making: Conclusions and a data use framework. In K. Schildkamp, M. K. Lai, & L. Earl (Eds.), Data-based decision making in education: Challenges and opportunities (pp. 177–191). Springer. http://dx.doi.org/10.1007/978-94-007-4816-3_10
45. Sümer, N. (2000). Yapısal eşitlik modelleri: Temel kavramlar ve örnek uygulamalar. Türk Psikoloji Yazıları, 3, 49–73.
46. Stiggins, R. J. (2004). New assessment beliefs for a new school mission. Phi Delta Kappan, 86(1), 22-27.
47. Symonds, K. W. (2003). After the test: How schools are using data to close the achievement gap. Bay Area School Reform Collaborative.
48. Tabachnick, B. G., & Fidell, L. S. (2012). Using multivariate statistics (6th ed.). Allyn & Bacon.
49. Tosun, Ü., & Karadağ, E. (2008). Yapılandırmacı düşünme envanterinin Türkçe’ye uyarlanması dil geçerliği ve psikometrik incelemesi. Kuram ve Uygulamada Eğitim Bilimleri, 8(1), 225–264.
50. Van Geel, M., Keuning, T., Visscher, A. J., & Fox, J. P. (2016). Assessing the effects of a school-wide databased decision-making intervention on student achievement growth in primary schools. American Educational Research Journal, 53(2), 360–394. https://doi.org/10.3102/0002831216637346
51. Van Kuijk, M. F., Deunk, M. I., Bosker, R. J., & Ritzema, E. S. (2016). Goals, data use, and instruction: The effect of a teacher professional development program on reading achievement. School Effectiveness and School Improvement, 27(2), 1-22. https://doi.org/10.1080/09243453.2015.1026268
52. Williams, D., & Coles, L. (2007). Teachers’ approaches to finding and using research evidence: an information literacy perspective. Educational Research, 49(2), 185-206. https://doi.org/10.1080/00131880701369719