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VIEWS OF PRE-SERVICE TEACHERS’ ABOUT DATA

Year 2023, Volume: 13 Issue: 4, 2008 - 2025, 25.12.2023

Melihan Ünlü Betül Keray Dinçel Hülya Ertaş Kılıç Safiye Temel Aslan

https://doi.org/10.30783/nevsosbilen.1270115

Abstract

The aim of the study is to examine the views of pre-service teachers about how they define, make sense of, and where they use the data. The study group of the research consists of 18 pre-service teachers, 6 of whom are Turkish, 6 of whom are mathematics, and 6 of whom are studying science teaching. In order to examine the views of the pre-service teachers, semi-structured interview form was preferred as a data collection tool. Descriptive analysis method was used to analyze the data. The answers given by the pre-service teachers regarding the definition of data show that they think of data as information/preliminary information and expressions/ values. It was concluded that they found the data most useful in "processing information and obtaining/giving information". While the majority of the participants stated that they could understand the variables in the data set and the relationships between the variables, they had difficulty in identifying the pattern of the data and estimating the context of the data. Pre-service teachers stated that, they had difficulty in interpreting the tables containing quantitative/qualitative data, determining the data ranges in the graph and when there was quantitative data in the graph. In addition, it is seen that all of the participants are aware of their data extraction areas and they mostly use the data to base their decisions in their daily lives.

Keywords

data data literacy pre-service teachers

References

  • Ackoff, R. L. (1989). From data to wisdom. Journal of Applied Systems Analysis, 16, 3-9. https://softwarezen.me/wp-content/uploads/2018/01/datawisdom.pdf
  • Baker, L. (2020). Data types getting started with statistics. https://books.google.com.tr/books?id=AlMMEAAAQBAJ&printsec=frontcover&d=types+of+data+qualitative+data+quantitative+data&hl=tr&sa=X&ved=2ahUKEwj2g569yv_uAhWoxIsKHRa8AsEQ6AEwB3oECAUQAg#v=onepage&q=types%20of%20data%20qualitative%20data%20quantitative%20data&f=false.
  • Başkale, H. (2016). Nitel araştırmalarda geçerlik, güvenirlik ve örneklem büyüklüğünün belirlenmesi. Dokuz Eylül Üniversitesi Hemşirelik Fakültesi Elektronik Dergisi (DEUHFED), 9(1), 23-28. https://dergipark.org.tr/tr/download/article-file/753041.
  • Büyüköztürk, Ş., Kılıç-Çakmak, E., Akgün, Ö., Karadeniz, Ş., & Demirel, F. (2011). Bilimsel araştırma yöntemleri. Pegem Akademi Yayıncılık.
  • Calzada-Prado, J., & Marzal, M.Á. (2013). Incorporating data literacy into information literacy programs: Core competencies and contents, Libri, 63(2), 123-134. https://doi.org/10.1515/libri-2013-0010.
  • Capraro, M. M., Kulm, G., & Capraro, R. M. (2005). Middle grades: Misconceptions in statistical thinking. School Science and Mathematics, 105(4), 165-174. https://doi.org/10.1111/j.1949-8594.2005.tb18156.x
  • Çelik, S. & Akdamar, E. (2018). Büyük veri ve veri görselleştirme. Akademik Bakış Uluslararası Hakemli Sosyal Bilimler Dergisi, (65), 253-264. https://dergipark.org.tr/en/pub/abuhsbd/issue/36059/404871
  • Dunlap, K., & Piro, J. S. (2016). Diving into data: Developing the capacity for data literacy in teacher education. Cogent Education, 3(1), 1-13. https://doi.org/10.1080/2331186X.2015.1132526
  • Ebbeler, J., Poortman, C.L., Schildkamp, K., & Pieters, J. M. (2017). The effects of a data use intervention on educators’ satisfaction and data literacy. Educational Assessment, Evaluation and Accountability (29), 83–105. https://doi.org/10.1007/s11092-016-9251-z
  • Frank, M., Walker, J., Attard, J., & Tygel, A. (2016). Data Literacy: what is it and how can we make it happen? Editorial. The Journal of Community Informatics, 12(3), 4-8. https://doi.org/10.15353/joci.v12i3.3274
  • Gallup, S.D., Dattero, R., & Hicks, R.C. (2002). Knowledge management systems: an architecture for active and passive knowledge. Information Resource Management Journal, 15(1), 22-7. https://doi.org/ 10.4018/irmj.2002010103
  • Gardner, H. E. (2000). Intelligence reframed: Multiple intelligences for the 21st century. Hachette.
  • Gibson, J. P., & Mourad, T. (2018). The growing importance of data literacy in life science education. American Journal of Botany, 105(12), 1953-1956. https://doi.org/10.1002/ajb2.1195
  • Gummer, E. S., & Mandinach, E. B. (2015). Building a conceptual framework for data literacy. Teachers College Record, 117(4), 1-22. https://doi.org/10.1177/016146811511700401 Henderson, J., & Corry, M. (2020). Data literacy training and use for educational professionals. Journal of Research in Innovative Teaching & Learning. 14(2), 232–244. https://doi.org/ 10.1108/JRIT-11-2019-0074
  • Hoelter, L. (2017). “But it’s a number, so it has to be true!”: An introduction to data literacy, part I. K. Fontichiaro, A. Lennex, T. Hoff, K. Hovinga ve J. A. Oehrli (Ed.), Data literacy in the real world: Conversations & Case studies (s. 9-12) içinde. Michigan Publishing, University of Michigan Library.
  • Hunter-Thomson, K. (2020). Data literacy 101: What can we actually claim from our data? Science Scope, 43(6), 20-26. https://www.proquest.com/openview/7c4c7fc2ebe84e743a2da009a6e9a57e/1?pq-origsite=gscholar&cbl=36017
  • In, J. & Lee, S. (2017). Statistical data presentation. Korean Journal of Anesthesiology, 70(3), 267-276. https://doi.org/10.4097/kjae.2017.70.3.267
  • Kelleher, J. D. & Tierney, B. (2020). Veri bilimi (O. Öztürk, Çev.). Tellekt.
  • Loshin, D. (2010). Master data management. Morgan Kaufmann.
  • Liew, A. (2007). Understanding data, information, knowledge and their inter-relationships. Jour Journal of Knowledge Management Practice, 8(2). http://www.tlainc.com/articl134.htm
  • Mandinach, E. B., & Gummer, E. S. (2013). A systemic view of implementing data literacy in educator preparation. Educational Researcher, 42(1), 30-37. https://doi.org/10.3102/0013189X12459803
  • Mandinach, E. B., & Schildkamp, K. (2021). Misconceptions about data-based decision making in education: An exploration of the literature. Studies in Educational Evaluation, 69, 1-10. https://doi.org/10.1016/j.stueduc.2020.100842
  • Marr, B. (2017). Veri stratejisi. MediaCart.
  • Marsh, J. A. (2012). Interventions promoting educators’use of data: research insights and gaps. Teachers College Record, 114(11), 1–48. https://doi.org/10.1177/016146811211401106
  • MEB (2019). PISA 2018 Türkiye ön raporu. http://pisa.meb.gov.tr/eski%20dosyalar/wp-content/uploads/2020/01/PISA_2018_Turkiye_On_Raporu.pdf
  • Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis: An expanded source book. SAGE.
  • Özbey, İ. B. (2021). Koronavirüs salgınının toplumsal yapı üzerindeki etkileri: Erzurum örneği. Afyon Kocatepe Üniversitesi Sosyal Bilimler Dergisi, 23(3), 821-839. https://doi.org/10.32709/akusosbil.874853
  • Patton, M. Q. (2014). Nitel araştırma ve değerlendirme yöntemleri (M. Bütün ve S. B. Demir, Çev. Ed.). Pegem Akademi Yayıncılık.
  • Rezba, R. J., Sprague, C. R., McDonnough, J. T. & Matkins, J. J. (2007). Learning and assessing science process skills. Kendall/Hunt Publishing Company.
  • Robson, C. (2015). Gerçek dünya araştırması (Ş. Çınkır & N. Demirkasımoğlu, Çev.). Anı Yayıncılık.
  • Türk Dil Kurumu Sözlükleri (2019). Genel Türkçe sözlük. https://sozluk.gov.tr. Vahey, P., Yarnall, L., Patton, C., Zalles, D., & Swan, K. (2006). Mathematizing middle school: Results from across-disciplinary study of data literacy. San Francisco: Paper presented at the Annual Conference of the American Educational Research Association.
  • Vahey, P., Rafanan, K., Patton, C., Swan K., van’t Hooft, M., Kratcoski, A. & Stanford, T. (2012). A cross-disciplinary approach to teaching data literacy and proportionality. Educational Studies in Mathematics (81), 179–205. https://doi.org/10.1007/s10649-012-9392-z.
  • Wang, R. Y. & Strong, D. M. (1996). Beyond accuracy: What data quality means to data consumers. Journal of Management Information Systems, 12(4), 5-33. https://doi.org/10.1080/07421222.1996.11518099
  • Weber, R. P. (1990). Basic content analysis. SAGE. https://doi.org/10.4135/9781412983488
  • Wolff, A., Gooch, D., Cavero Montaner, J.J, Rashid, U. & Kortuem, G. (2016). Creating an understanding of data literacy for a data-driven society. The Journal of Community Informatics, 12(3), 9-26. https://doi.org/10.15353/joci.v12i3.3275
  • Yıldırım, A. & Şimşek, H. (2008). Sosyal bilimlerde nitel araştırma yöntemleri. Seçkin Yayıncılık.
  • Yüksel-Şahin, F. (2004). Ortaöğretim öğrencilerinin ve üniversite öğrencilerinin matematik korku düzeyleri. Eğitim Bilimleri ve Uygulama, 3(5), 57- 74. https://www.idealonline.com.tr/IdealOnline/lookAtPublications/paperDetail.xhtml?uId=26965

ÖĞRETMEN ADAYLARININ VERİLERE YÖNELİK GÖRÜŞLERİ

Year 2023, Volume: 13 Issue: 4, 2008 - 2025, 25.12.2023

Melihan Ünlü Betül Keray Dinçel Hülya Ertaş Kılıç Safiye Temel Aslan

https://doi.org/10.30783/nevsosbilen.1270115

Abstract

Bu çalışmanın amacı öğretmen adaylarının verilere ve verileri anlayabilme durumlarına ilişkin değerlendirmelerini belirlemektir. Araştırmanın çalışma grubunu 6’sı Türkçe öğretmenliği, 6’sı ilköğretim matematik öğretmenliği ve 6’sı fen bilgisi öğretmenliğinde öğrenim gören 18 öğretmen adayı oluşturmaktadır. Öğretmen adaylarının düşüncelerini derinlemesine inceleyebilmek amacıyla veri toplama aracı olarak görüşme yöntemi tercih edilmiştir. Verilerin analizinde içerik analizi yöntemi kullanılmıştır. Öğretmen adaylarının veri tanımına ilişkin verdikleri yanıtlar verileri daha çok bilgi ve nicel değer olarak düşündüklerini göstermektedir. Verileri en fazla “bilgiyi işlemede ve bilgi edinme/vermede” işe yarar gördükleri sonucuna ulaşılmıştır. Katılımcıların çoğunluğu veri setindeki değişkenleri ve değişkenler arasındaki ilişkileri anlayabildiklerini belirtirken bir konuya ait verilerde, verilerdeki örüntüyü belirlemede ve verilerin bağlamını tahmin etmede zorlandıklarını ifade etmişlerdir. Öğretmen adaylarının büyük bir çoğunluğu veri görselleştirmelerini anlayabildiklerini belirtmişlerdir. Bununla birlikte öğretmen adayları en fazla nicel/nitel veri içeren tabloları yorumlamada, grafikte nicel veriler yer aldığında ve grafikteki veri aralıklarını belirlemede zorlandıklarını belirtmişlerdir. Ayrıca katılımcıların tamamının veri çıkarım alanlarının farkında oldukları ve günlük yaşamlarında verilerden en çok bilgiyi anlamak/aktarmak için yararlandıkları görülmektedir.

Keywords

veri veri okuryazarlığı öğretmen adayları

References

  • Ackoff, R. L. (1989). From data to wisdom. Journal of Applied Systems Analysis, 16, 3-9. https://softwarezen.me/wp-content/uploads/2018/01/datawisdom.pdf
  • Baker, L. (2020). Data types getting started with statistics. https://books.google.com.tr/books?id=AlMMEAAAQBAJ&printsec=frontcover&d=types+of+data+qualitative+data+quantitative+data&hl=tr&sa=X&ved=2ahUKEwj2g569yv_uAhWoxIsKHRa8AsEQ6AEwB3oECAUQAg#v=onepage&q=types%20of%20data%20qualitative%20data%20quantitative%20data&f=false.
  • Başkale, H. (2016). Nitel araştırmalarda geçerlik, güvenirlik ve örneklem büyüklüğünün belirlenmesi. Dokuz Eylül Üniversitesi Hemşirelik Fakültesi Elektronik Dergisi (DEUHFED), 9(1), 23-28. https://dergipark.org.tr/tr/download/article-file/753041.
  • Büyüköztürk, Ş., Kılıç-Çakmak, E., Akgün, Ö., Karadeniz, Ş., & Demirel, F. (2011). Bilimsel araştırma yöntemleri. Pegem Akademi Yayıncılık.
  • Calzada-Prado, J., & Marzal, M.Á. (2013). Incorporating data literacy into information literacy programs: Core competencies and contents, Libri, 63(2), 123-134. https://doi.org/10.1515/libri-2013-0010.
  • Capraro, M. M., Kulm, G., & Capraro, R. M. (2005). Middle grades: Misconceptions in statistical thinking. School Science and Mathematics, 105(4), 165-174. https://doi.org/10.1111/j.1949-8594.2005.tb18156.x
  • Çelik, S. & Akdamar, E. (2018). Büyük veri ve veri görselleştirme. Akademik Bakış Uluslararası Hakemli Sosyal Bilimler Dergisi, (65), 253-264. https://dergipark.org.tr/en/pub/abuhsbd/issue/36059/404871
  • Dunlap, K., & Piro, J. S. (2016). Diving into data: Developing the capacity for data literacy in teacher education. Cogent Education, 3(1), 1-13. https://doi.org/10.1080/2331186X.2015.1132526
  • Ebbeler, J., Poortman, C.L., Schildkamp, K., & Pieters, J. M. (2017). The effects of a data use intervention on educators’ satisfaction and data literacy. Educational Assessment, Evaluation and Accountability (29), 83–105. https://doi.org/10.1007/s11092-016-9251-z
  • Frank, M., Walker, J., Attard, J., & Tygel, A. (2016). Data Literacy: what is it and how can we make it happen? Editorial. The Journal of Community Informatics, 12(3), 4-8. https://doi.org/10.15353/joci.v12i3.3274
  • Gallup, S.D., Dattero, R., & Hicks, R.C. (2002). Knowledge management systems: an architecture for active and passive knowledge. Information Resource Management Journal, 15(1), 22-7. https://doi.org/ 10.4018/irmj.2002010103
  • Gardner, H. E. (2000). Intelligence reframed: Multiple intelligences for the 21st century. Hachette.
  • Gibson, J. P., & Mourad, T. (2018). The growing importance of data literacy in life science education. American Journal of Botany, 105(12), 1953-1956. https://doi.org/10.1002/ajb2.1195
  • Gummer, E. S., & Mandinach, E. B. (2015). Building a conceptual framework for data literacy. Teachers College Record, 117(4), 1-22. https://doi.org/10.1177/016146811511700401 Henderson, J., & Corry, M. (2020). Data literacy training and use for educational professionals. Journal of Research in Innovative Teaching & Learning. 14(2), 232–244. https://doi.org/ 10.1108/JRIT-11-2019-0074
  • Hoelter, L. (2017). “But it’s a number, so it has to be true!”: An introduction to data literacy, part I. K. Fontichiaro, A. Lennex, T. Hoff, K. Hovinga ve J. A. Oehrli (Ed.), Data literacy in the real world: Conversations & Case studies (s. 9-12) içinde. Michigan Publishing, University of Michigan Library.
  • Hunter-Thomson, K. (2020). Data literacy 101: What can we actually claim from our data? Science Scope, 43(6), 20-26. https://www.proquest.com/openview/7c4c7fc2ebe84e743a2da009a6e9a57e/1?pq-origsite=gscholar&cbl=36017
  • In, J. & Lee, S. (2017). Statistical data presentation. Korean Journal of Anesthesiology, 70(3), 267-276. https://doi.org/10.4097/kjae.2017.70.3.267
  • Kelleher, J. D. & Tierney, B. (2020). Veri bilimi (O. Öztürk, Çev.). Tellekt.
  • Loshin, D. (2010). Master data management. Morgan Kaufmann.
  • Liew, A. (2007). Understanding data, information, knowledge and their inter-relationships. Jour Journal of Knowledge Management Practice, 8(2). http://www.tlainc.com/articl134.htm
  • Mandinach, E. B., & Gummer, E. S. (2013). A systemic view of implementing data literacy in educator preparation. Educational Researcher, 42(1), 30-37. https://doi.org/10.3102/0013189X12459803
  • Mandinach, E. B., & Schildkamp, K. (2021). Misconceptions about data-based decision making in education: An exploration of the literature. Studies in Educational Evaluation, 69, 1-10. https://doi.org/10.1016/j.stueduc.2020.100842
  • Marr, B. (2017). Veri stratejisi. MediaCart.
  • Marsh, J. A. (2012). Interventions promoting educators’use of data: research insights and gaps. Teachers College Record, 114(11), 1–48. https://doi.org/10.1177/016146811211401106
  • MEB (2019). PISA 2018 Türkiye ön raporu. http://pisa.meb.gov.tr/eski%20dosyalar/wp-content/uploads/2020/01/PISA_2018_Turkiye_On_Raporu.pdf
  • Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis: An expanded source book. SAGE.
  • Özbey, İ. B. (2021). Koronavirüs salgınının toplumsal yapı üzerindeki etkileri: Erzurum örneği. Afyon Kocatepe Üniversitesi Sosyal Bilimler Dergisi, 23(3), 821-839. https://doi.org/10.32709/akusosbil.874853
  • Patton, M. Q. (2014). Nitel araştırma ve değerlendirme yöntemleri (M. Bütün ve S. B. Demir, Çev. Ed.). Pegem Akademi Yayıncılık.
  • Rezba, R. J., Sprague, C. R., McDonnough, J. T. & Matkins, J. J. (2007). Learning and assessing science process skills. Kendall/Hunt Publishing Company.
  • Robson, C. (2015). Gerçek dünya araştırması (Ş. Çınkır & N. Demirkasımoğlu, Çev.). Anı Yayıncılık.
  • Türk Dil Kurumu Sözlükleri (2019). Genel Türkçe sözlük. https://sozluk.gov.tr. Vahey, P., Yarnall, L., Patton, C., Zalles, D., & Swan, K. (2006). Mathematizing middle school: Results from across-disciplinary study of data literacy. San Francisco: Paper presented at the Annual Conference of the American Educational Research Association.
  • Vahey, P., Rafanan, K., Patton, C., Swan K., van’t Hooft, M., Kratcoski, A. & Stanford, T. (2012). A cross-disciplinary approach to teaching data literacy and proportionality. Educational Studies in Mathematics (81), 179–205. https://doi.org/10.1007/s10649-012-9392-z.
  • Wang, R. Y. & Strong, D. M. (1996). Beyond accuracy: What data quality means to data consumers. Journal of Management Information Systems, 12(4), 5-33. https://doi.org/10.1080/07421222.1996.11518099
  • Weber, R. P. (1990). Basic content analysis. SAGE. https://doi.org/10.4135/9781412983488
  • Wolff, A., Gooch, D., Cavero Montaner, J.J, Rashid, U. & Kortuem, G. (2016). Creating an understanding of data literacy for a data-driven society. The Journal of Community Informatics, 12(3), 9-26. https://doi.org/10.15353/joci.v12i3.3275
  • Yıldırım, A. & Şimşek, H. (2008). Sosyal bilimlerde nitel araştırma yöntemleri. Seçkin Yayıncılık.
  • Yüksel-Şahin, F. (2004). Ortaöğretim öğrencilerinin ve üniversite öğrencilerinin matematik korku düzeyleri. Eğitim Bilimleri ve Uygulama, 3(5), 57- 74. https://www.idealonline.com.tr/IdealOnline/lookAtPublications/paperDetail.xhtml?uId=26965
There are 37 citations in total.

Details

Primary Language Turkish
Subjects Educational Psychology
Journal Section Educational Sciences
Authors

Melihan Ünlü 0000-0003-3337-8758

Betül Keray Dinçel 0000-0002-2184-7361

Hülya Ertaş Kılıç 0000-0001-9683-3186

Safiye Temel Aslan 0000-0001-6969-8871

Early Pub Date December 20, 2023
Publication Date December 25, 2023
Published in Issue Year 2023 Volume: 13 Issue: 4

Cite

APA Ünlü, M., Keray Dinçel, B., Ertaş Kılıç, H., Temel Aslan, S. (2023). ÖĞRETMEN ADAYLARININ VERİLERE YÖNELİK GÖRÜŞLERİ. Nevşehir Hacı Bektaş Veli Üniversitesi SBE Dergisi, 13(4), 2008-2025. https://doi.org/10.30783/nevsosbilen.1270115
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