INTERACTION AMONG THREE FUNDAMENTAL SKILLS: REFLECTIONS OF COGNITIVE AND AFFECTIVE STRUCTURES ON PRE-SERVICE SCIENCE TEACHERS

Abstract

In the contemporary educational landscape, science education has evolved from mere knowledge transmission into a multidimensional process encompassing higher-order thinking, cognitive readiness, and practical application. This shift necessitates that pre-service teachers are not only professionally competent but also capable of designing educational content tailored to their target audience. This study investigates the correlation between computational thinking skills, attitudes toward laboratory practices, and the readiness levels of pre-service      science teachers. The study sample included 200 pre‑service science teachers pursuing higher education at a state university in Izmir for the 2023-2024 academic year. The data were analyzed using Pearson correlation. The findings reveal positive, significant, and moderate-to-strong correlations between all three variables. Furthermore, fourth-year students demonstrated significantly higher laboratory readiness than third-year students, reflecting an increased responsibility for self-regulated learning as they approach graduation. These results suggest that cognitive tools like computational thinking are deeply intertwined with attitudes and self-directed learning, emphasizing the need for teacher education curricula to adopt a holistic approach that considers these interactions.

Keywords: attitude, computational thinking, laboratory, pre-service science teacher, readiness

Ethics and Conflict of Interest

All ethical rules were observed at each stage of the research. The authors declare that he acted in accordance with ethical rules in all processes of the research. The authors declare that they do not have any conflict of interest with other persons, institutions or organizations.

REFERENCES

Alkan, F. (2012). Kendi kendine öğrenmenin kimya laboratuvarında öğrenci başarısına, öğrenme hazırbulunuşluğuna, laboratuvar becerilerine yönelik tutumuna ve endişesine etkisi [Effect of self-directed learning on the students' success, learning readiness, attitudes towards laboratory skills and anxiety in chemistry laboratory]. [Unpublished Master’s thesis]. Hacettepe University.

Barr, D., Harrison, J., & Conery, L. (2011). Computational thinking: A digital age skill for everyone. International Society for Technology in Education, 38, 20-23. https://eric.ed.gov/?id=EJ918910 

Bransford, J. D., Brown, A. L., & Cocking, R. R. (2000). How people learn: Brain, mind, experience, and school. Washington DC: National Academy Press.

Bybee, R. W. (2013). The Case for STEM education: Challenges and opportunities. National Science Teachers Association.

Candy, P. (1991). Self-direction for lifelong learning: A comprehensive guide to theory and practice. San Francisco, CA: Jossey-Bass Publishers.

Cheng, X. (2025). Significance and challenges of scientific inquiry in science education. Science Insights Education Frontiers, 28(2), 4529–4631. https://doi.org/10.15354/sief.25.co471

Guglielmino, L. M. (1977). Development of the self-directed learning readiness scale. Athens: University of Georgia, 64.

Hofstein, A., & Lunetta, V.N. (2004), The laboratory in science education: Foundations for the twenty-first century. Sci. Ed., 88. 28-54. https://doi.org/10.1002/sce.10106

Karasar, N. (2012). Bilimsel araştırma yöntemi: Kavramlar, ilkeler, teknikler (23^rd ed.). Nobel Yayın Dağıtım.

Kurbanoglu, N., & Akin, A. (2010). The relationships between university students' chemistry laboratory anxiety, attitudes, and self-efficacy beliefs. Australian Journal of Teacher Education, 35(8), 48-59. https://doi.org/10.14221/ajte.2010v35n8.4    

Lye, S. Y., & Koh, J. H. L. (2014). Review on teaching and learning of computational thinking through programming: What is next for K-12? Computers in Human Behavior, 41, 51-61. https://doi.org/10.1016/j.chb.2014.09.012

Osborne, J. F., & Dillon, J. (2008). Science education in Europe. London: Nuffield Foundation.

Saeid, N., & Eslaminejad, T. (2017). Relationship between students’ self-directed learning readiness, academic self-efficacy, and achievement motivation. International Education Studies, 10(1), 225–232.  http://dx.doi.org/10.5539/ies.v10n1p225

Sari, U., Ulusoy, A., & Pektaş, H.M. (2025) Computational thinking in science laboratories based on the flipped classroom model: Computational thinking, Laboratory Entrepreneurial and Attitude. J Sci Educ Technol, 34, 1334–1358. https://doi.org/10.1007/s10956-024-10192-y

Şahin, F., & Şasmaz Ören, F. (2022). Laboratory as an ınstrument in improving the scientific reasoning skills of pre-service science teachers with different cognitive styles. Science Insights Education Frontiers, 13(2), 1875–1897. https://doi.org/10.15354/sief.22.or072

Shute, V., Sun, C., & Asbell-Clarke, J. (2017). Demystifying computational thinking. Educational Research Review, 22, 142-158. https://doi.org/10.1016/j.edurev.2017.09.003

Strat, T. T. S., Henriksen, E. K., & Jegstad, K. M. (2024). Inquiry-based science education in science teacher education: a systematic review. Studies in Science Education, 60(2), 191-249. https://doi.org/10.1080/03057267.2023.2207148

Sweller, J., Ayres, P., & Kalyuga, S. (2011). The expertise reversal effect. In Cognitive load theory (pp. 155-170). New York, NY: Springer New York. https://doi.org/10.1007/978-1-4419-8126-4_12

Üzümcü, Ö. (2023). Computational thinking scale: the predictive role of metacognition in the context of higher order thinking skills. Journal for the Education of Gifted Young Scientists, 11(3), 423-437. https://doi.org/10.17478/jegys.1355722

Voogt, J., & Roblin, N. P. (2012). A comparative analysis of international frameworks for 21st century competences: Implications for national curriculum policies. Journal of Curriculum Studies, 44, 299-321. https://doi.org/10.1080/00220272.2012.668938

Voogt, J., Fisser, P., Good, J., Mishra, P., & Yadav, A. (2015). Computational thinking in compulsory education: Towards an agenda for research and practice. Education and information technologies, 20(4), 715-728. https://doi.org/10.1007/s10639-015-9412-6   

Weintrop, D., Beheshti, E., Horn, M., Orton, K., Jona, K., Trouille, L., & Wilensky, U. (2016). Defining computational thinking for mathematics and science classrooms. Journal of Science Education and Technology, 25(1), 127-147. https://doi.org/10.1007/s10956-015-9581-5

Wing, J. M. (2006) Computational thinking. Communications of the ACM, 49, 33-35. https://doi.org/10.1145/1118178.1118215

Yurdal, M. O., & Toraman, Ç. (2023). Self-directed learning, academic achievement and motivation: A meta-analytical study. Alberta Journal of Educational Research, 69(2), 1–21.  https://doi.org/10.55016/ojs/ajer.v69i2.75098

Yükseköğretim Kurulu (YÖK). (2018). Yeni Öğretmen Yetiştirme Lisans Programları. YÖK Yayınları.

Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70. https://doi.org/10.1207/s15430421tip4102_2