Fen Okuryazarlığı Bağlamında Öğretmen Adaylarının Yapay Zekâ Konusuna Yönelik Sosyo-Bilimsel Modelleri
Özet
Bu nitel araştırmada, öğretmen adaylarının fen okuryazarlığı bağlamında yapay zekâ konusuna yönelik sosyo-bilimsel modelleri ve zihinsel anlayışları incelenmiştir. Çalışma, yedi farklı lisans programında öğrenim gören 28 öğretmen adayıyla yürütülmüş; veriler katılımcıların hazırladıkları çizimler ve nitel açıklamalardan oluşan sosyo-bilimsel modeller üzerinden içerik analizi tekniğiyle incelenmiştir. Araştırma bulgularına göre öğretmen adayları yapay zekâ teknolojisini eğitim, sağlık, iletişim, ekonomi, teknoloji, toplum ve politika boyutları çerçevesinde hem olumlu hem de olumsuz yönleriyle değerlendirmiştir. Olumlu görüşlerde en yüksek frekans sağlık (hastalıkların erken teşhisi, ilaç ve tedavi imkânları) ve eğitim (öğrenci ile öğretmen ihtiyaçlarının karşılanması, bilgiye erişim kolaylığı) temalarında elde edilirken; ekonomik verimlilik, insan gücü ve zaman tasarrufu ile günlük yaşamı kolaylaştıran otonom araçlar öne çıkmıştır. Olumsuz görüşlerde ise toplum boyutunda işsizlik artışı ve insani tembellik riski, teknoloji boyutunda siber güvenlik sorunları, sağlık alanındaki olası yanılma payı, iletişimde yüz yüze etkileşimin zayıflaması ve eğitimde fırsat eşitsizliği vurgulanmıştır. Ayrıca kodlayıcılar arası güvenirlik %89 olarak hesaplanmış ve verilerin doğruluğu katılımcı ifadelerinden yapılan doğrudan alıntılarla desteklenmiştir. Sonuç olarak, sosyo-bilimsel modelleme uygulamalarının öğretmen adaylarının karmaşık, tartışmalı ve disiplinlerarası fen konularına çoklu bakış açısıyla yaklaşmalarını sağladığı, karar verme süreçlerini desteklediği, bilim ve toplum arasındaki bağı somutlaştırarak fen okuryazarlığı becerilerine önemli katkılar sunduğu tespit edilmiştir.
In this qualitative research, prospective teachers' socio-scientific models and mental understandings regarding artificial intelligence within the context of science literacy were examined. The study was conducted with 28 prospective teachers studying in seven different undergraduate programs; the data were analyzed using content analysis through socio-scientific models consisting of drawings and qualitative explanations prepared by the participants. According to the research findings, prospective teachers evaluated artificial intelligence technology with both positive and negative aspects within the framework of education, health, communication, economy, technology, society, and policy dimensions. While the highest frequency in positive views was obtained in health (early diagnosis of diseases, medicine, and treatment opportunities) and education (meeting student and teacher needs, ease of access to information) themes; economic efficiency, human power and time savings, and autonomous vehicles that make daily life easier came to the fore. In negative views, the increase in unemployment and the risk of human laziness in the society dimension, cybersecurity issues in the technology dimension, the potential margin of error in health, the weakening of face-to-face interaction in communication, and inequality of opportunity in education were emphasized. Additionally, inter-coder reliability was calculated as 89%, and data validity was supported by direct quotes from participant statements. In conclusion, it was determined that socio-scientific modeling practices enable prospective teachers to approach complex, controversial, and interdisciplinary science topics from multiple perspectives, support their decision-making processes, and make significant contributions to their science literacy skills by contextualizing the link between science and society.
Referanslar
Büyüköztürk, Ş., Kılıç Çakmak E., Akgün, Ö. E., Karadeniz, Ş. & Demirel, F. (2011). Bilimsel araştırma yöntemleri (10. baskı). Ankara: Pegem A Yayıncılık.
Carpenter, T. P. & Lehrer, R. (1999). Teaching and learning mathematics with understanding. Mathematics classrooms that promote understanding, 19-32.
Dawson, V. M. (2011). A case study of the impact of introducing socio-scientific issues into a reproduction unit in a Catholic girls’ school. In T. Sadler, (Ed.). Socioscientific issues in the classroom: Teaching, learning and research (pp.313-345). Dordrecht: Springer.
Dawson, V. M. & Venville, G. (2010). Teaching strategies for developing students’ argumentation skills about socioscientific issues in high school genetics. Research in Science Education, 40(2), 133-148.
Durant, J. R. (1993). What is scientific literacy. In J. R. Durant & J. Gregory (Eds.), Science and Culture in Europe (pp. 129-137).
Eastwood, J., Sadler, T., Zeidler, D., Lewis, A., Amiri, L. & Applebaum, S. (2012). Contextualizing nature of science instruction in socioscientific issues. International journal of Science Education, 34(15), 2289-2315.
Feinstein, N. (2011). Salvaging science literacy. Science education, 95(1), 168-185.
Fleming, R. (1986). Adolescent reasoning in socio-scientific issues. Part I: Social cognition. Journal of Research in Science Teaching, 23, 677-687.
Gabel, L. L. (1976). The development of a model to determine perceptions of scientific literacy. The Ohio State University.
Gilbert, J. K. (2006). On the nature of “context” in chemical education. International Journal of Science Education, 28(9), 957-976.
Gilbert, J. K. & Osborne, R. J. (1980). The use of models in science and science teaching. European Journal of Science Education, 2(1), 3-13.
Gobert, J. D. & Buckley, B. C. (2000). Introduction to Model-based Teaching and Learning, International Journal of Science Education, 22(9): 891-894.
Grosslight, L., Unger, C., Jay, E. & Smith, L. C. (1991). Understanding models and their use in science: conceptions of hiddle and high school students and experts, Journal of Research in Science Teaching, 28: 799-822.
Gülçiçek, Ç. & Güneş, B. (2004). Fen öğretiminde kavramların somutlaştırılması: modelleme stratejisi, bilgisayar simülasyonları ve analojiler. Eğitim ve Bilim, 29(134), 36-48.
Harrison, A.G. & Treagust, D.F. (2000). A typology of school science models. International Journal of Science Education, 22(9), 1011-1026.
Hazen, R. M. & Trefil, J. (1991). Science matters. Achieving scientific literacy. New York: AnchorBooks Doubleday.
Hurd, P. D. (1958). Science literacy: Its meaning for American schools. Educational leadership, 16(1), 13-16.
Jenkins, E. W. (1994). Public understanding of science and science education for action. Journal of curriculum studies, 26(6), 601-611.
Justi, S. R. & Gilbert, K. J. (2002). Modelling teachers’ view on the nature of modelling and implications for the education of modellers. International Journal of Science Education, 24(4), 369-387.
Kee, L., Sadler, T., Zangori, L. & Friedrichsen, P. J. (2021). Developing and using multiple models to promote scientific literacy in the context of socio‑scientific issues. Science & Education, 30 (2021), 589-607.
Kee, L., Zangori, L., Sadler, T. & Friedrichsen, P. (2020). Integrating scientific modeling and socio-scientific reasoning to promote scientific literacy. In W. Powell (Ed.), Socio-scientific issue-based instruction for scientific literacy development. IGI Global.
Kolstø, S. D. (2001). Scientific literacy for citizenship: Tools for dealing with the science dimension of controversial socioscientific issues. Science Education, 85, 291-310.
Laugksch, R. C. (2000). Scientific literacy: A conceptual overview. Science education, 84(1), 71-94.
Lehrer, R., Schauble, L. & Petrosino, M. (2000). Modeling in mathematics and science (pp. 101-159). Nueva Jersey: Lawrence Erlbaum.
Merriam, S. B. & Tisdell, E. J. (2015). Qualitative research: A guide to design and implementation. John Wiley & Sons.
Miles, M. B. & Huberman, A. M. (1994). Qualitative data analysis: An expanded sourcebook. Thousand Oaks, CA: Sage Publications.
Miller, J. D. (1983). Scientific literacy: A conceptual and empirical review. Daedalus, 112(2), 29-48.
National Research Council [NRC] (2011). A framework for K-12 science education: practices, crosscutting concepts, and core ideas. National Academies Press.
Nielsen, J. A. (2012). Science in discussions: An analysis of the use of science content in socioscientific discussions. Science Education, 96(3), 428-456.
Patronis, T., Potari, D. & Spiliotopoulou, V. (1999). Students’ argumentation in decision-making on a socio-scientific issue: Implications for teaching. International Journal of Science Education, 21(7), 745-754.
Pella, M. O., O'hearn, G. T. & Gale, C. W. (1966). Referents to scientific literacy. Journal of Research in Science Teaching, 4(3), 199-208.
Presley, M. L., Sickel, A. J., Muslu, N., Merle-Johnson, D., Witzig, S. B., Izci, K. & Sadler, T. D. (2013). A Framework for socio-scientific issues based education. Science Educator, 22(1), 26-32.
Roberts, D. A. (1983). Scientific literacy: towards balance in setting goals for school science programs. A Discussion Paper. Publications Office, Science Council of Canada, 100 Metcalfe St., Ottawa, Ontario.
Roberts, D. A. (2007). Scientific literacy/science literacy. In S. K. Abell & N. G. Lederman (Eds.), Handbook of research in science education (pp. 729-780). Mahwa, New Jersey: Lawrence Erlbaum Associates.
Roberts, D. A. (2013). Scientific literacy/science literacy. In Handbook of research on science education (pp. 743-794). Routledge.
Rubba, P. & Anderson, H. (1978) Development of an instrument to assess secondary students' understanding of the nature of scientific knowledge. Science Education 62(4), 449-458.
Sadler, T. D. (2004). Informal reasoning regarding socio-scientific issues: A critical review of literature. Journal of Research in Science Teaching, 4, 513-536.
Sadler, T. D. & Zeidler, D. L. (2005). Patterns of informal reasoning in the context of socioscientific decision making. Journal of Research in Science Teaching, 42(1), 112-138.
Sadler, T. D., Barab, S. & Scott, B. (2007). What do students gain by engaging in socioscientific inquiry? Research in Science Education, 37(4), 371-391.
Schauble, L., Glaser, R., Duschl, R. A., Schulze, S. & John, J. (1995). Students' understanding ofthe objectives and procedures of experimentation in the science classroom. The Journal of the Learning Sciences, 4,131-166.
Schwarz, C. V., Reiser, B. J., Davis, E. A., Kenyon, L., Achér, A., Fortus, D., Shwartz, Y., Hug, B. & Krajcik, J. (2009). Developing a learning progression for scientific modeling: Making scientific modeling accessible and meaningful for learners. Journal of Research in Science Teaching, 46(6), 632-654.
Sharon, A. J. & Baram‐Tsabari, A. (2020). Can science literacy help individuals identify misinformation in everyday life? Science Education, 104(5), 873-894.
Shen, B. S. (1975). Views: Science Literacy: Public understanding of science is becoming vitally needed in developing and industrialized countries alike. American scientist, 63(3), 265-268.
Treagust, D. F.,Chittleborough, G. & Mamila, T. L. (2002). Students' Understanding of the Rol of Scientific Models in Learning Science, International Journal of Science Education, 24(4), 357-368.
Türköz, G. & Öztürk, N. (2020). Fen bilgisi öğretmen adaylarının bazı sosyo-bilimsel konularla ilgili kararlarının çok boyutlu bakış açısı ile incelenmesi. Cumhuriyet Uluslararası Eğitim Dergisi, 9(1), 175-197.
Van Driel, H. J. & Verloop, N. (2002). Experienced teachers’ knowledge of teaching and learning of models and modeling in science education. International Journal of Science Education, 24(12), 1255-1272.
Windschitl, M., Thompson, J. & Braaten, M. (2008). Beyond the scientific method: Model-based inquiry as a new paradigm of preference for school science investigations. Science Education, 92(5), 941– 967.
Yacoubian, H. A. & Khishfe, R. (2018). Argumentation, critical thinking, nature of science and socioscientific issues: A dialogue between two researchers. International Journal of Science Education, 40(7), 796807.
Yıldırım, A. & Şimşek, H. (2021). Sosyal bilimlerde nitel araştırma yöntemleri (12. Baskı). Ankara: Seçkin Yayıncılık.
Zeidler, D. (2015). Socioscientific issues. In: Gunstone R. (eds) Encyclopedia of Science Education. Springer, Dordrecht.
Zeidler, D. L., Herman, B. C. & Sadler, T. D. (2019). New directions in socioscientific issues research. Disciplinary and Interdisciplinary Science Education Research, 1(1), 1-9.
Zeidler, D. L., Walker, K. A., Ackett, W. A. & Simmons, M. L. (2002). Tangled up in views: Beliefs in the nature of science and responses to socioscientific dilemmas. Science E
