Abstract
Büyük veriyi değerlendirme ve anlamlandırma süreci bazı beceri seviyelerini ön plana çıkarmaktadır. Küresel dünyada artan bu ihtiyaca bağlı olarak, Z kuşağının sayısal düşünme süreçlerinin işlevsel yapısını ve akış şemasını doğru bir şekilde anlamaları ve öğrenmelerine aktarmaları gerekmektedir. Bu yeterliliğin kazanılmasında öncelikle öğrencilerin mevcut düşünme beceri düzeyleri belirlenmelidir. Bu gereklilik çerçevesinde fen lisesinde öğrenim gören öğrencilerin sayısal düşünme beceri düzeylerinin belirlenmesi amaçlanmıştır. Araştırmanın çalışma grubunu Ankara'da bir fen lisesinde öğrenim gören 203 öğrenci oluşturmaktadır. Veriler, 22 sorudan oluşan Bilgisayar Düşünme Becerileri Ölçeği (BDDÖ) kullanılarak toplanmıştır. Araştırma bulguları incelendiğinde öğrencilerin sayısal düşünme becerileri ortalama puan değeri 100 üzerinden 77 olarak bulunmuştur. Bulunan değer öğrencilerin sayısal düşünme beceri düzeylerinin yüksek olduğunu göstermektedir. Ayrıca, ölçeğin alt boyutlarında fen liselerinde öğrenim gören öğrencilerin yaratıcılık, algoritmik düşünme, işbirlikçi, eleştirel düşünme, problem çözme ve sayısal düşünme beceri düzeyleri, cinsiyet ve sınıf düzeyi gibi alt boyutlarındaki istatistiksel anlamlılık belirlenmiştir. Bu sonuçlara göre eleştirel düşünme boyutunda cinsiyete göre anlamlı bir farklılık olduğu söylenebilir. Sonuç olarak, öğrenme hedefleri olarak öne çıkan düşünme becerisi süreçlerini anlamak ve bu süreçleri doğru kurgulamak hayati önem taşımaktadır. Bir düşünme becerisi süreci olan sayısal düşünme becerisi de bu çerçevede değerlendirilmektedir. Bu sürecin gelişimine katkı sağlayacak uygulama odaklı çalışmalara ihtiyaç her geçen gün artmaktadır.
References
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- Karasar, N. (1999). Bilimsel araştırma yöntemi: Kavramlar, ilkeler, teknikler. Ankara: Nobel Yayınevi.
- Kline, R. B. (2005). Principles and practice of structural equation modeling (2nd ed). New York: Guilford Press.
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- Wing, J. M. (2008). Computational thinking and thinking about computing. Philosopical Transactions of the Royal Society A Mathematichal, Physical and Engineering Science, 366, 3717–3725 http://dx.doi.org/10.1098/rsta.2008.0118
Abstract
The process of evaluating and making sense of big data brings some skill levels to the forefront. Depending on this increasing need in the global world, generation Z needs to understand the functional structure and flow chart of the computational thinking processes correctly and transfer it to their learning. In gaining this competence, firstly, the students' current thinking skill levels should be determined. Within the framework of this requirement, it aimed to determine the students' computational thinking skills levels studying at the science high school. The study group of the study consists of 203 students studying in a science high school in Ankara. The data were collected using the Computer Thinking Skills Scale (CAPS), composed of 22 questions. When the research findings were examined, the students' computational thinking skills' average point value was found to be 77 out of 100. The value found shows that students' computational thinking skill level is high. Besides, statistical significance in the sub-dimensions of the scale, such as creativity, algorithmic thinking, collaborative, critical thinking, problem-solving, and computational thinking skill levels, gender, and grade level of students studying at science high schools was determined in this study. According to these results, it can be said that there is a significant difference according to gender in the critical thinking dimension. As a result, it is vital to understand the thinking skill processes that come to the forefront as learning goals and construct these processes correctly. The computational thinking skill, which is a thinking skill process, is also evaluated within this framework. The need for application-oriented studies that will contribute to this process's development is increasing day by day
References
- Balcı, A. (2009). Sosyal bilimlerde araştırma: Yöntem, teknik ve ilkeler. Ankara: PegemA Yayınevi.
- Barr, D., Harrison, J., & Conery, L. (2011). Computational thinking: A digital age skill for everyone. Erişim: at: http://files.eric.ed.gov/fulltext/EJ918910.pdf, Erişim Tarihi: 15.10.2015
- Bescherer, C., & Fest, A. (2018). Computational thinking in primary schools: Theory and casual model. In A. Tatnall & M. Webb (Eds.), Tomorrow's learning: Involving everyone. IFIP advances in information and communication technology, Springer.
- Bundy, A. (2007). Computational thinking is pervasive. Erişim:http://www.inf.ed.ac.uk/publications/online/1245.pdf, Erişim Tarihi: 17.10.2015
- Büyüköztürk, Ş. (2002). Sosyal bilimler için veri analizi el kitabı. Ankara: PegemA Yayıncılık.
- CSTA & ISTE (2011). Operational definition of computational thinking for K–12 education. Available at: http://csta.acm.org/Curriculum/sub/CurrFiles/CompThinkingFlyer.pdf, Erişim Tarihi: 01.03.2021.
- Curzon, P. (2015). Computational thinking: Searching to speak. Available at: http://teachinglondoncomputing.org/free-workshops/computational-thinking-searching-to-speak/, Erişim Tarihi: 19.10.2015
- Field, A. P. (2000). Discovering statistics using SPSS for Windows (Chapter 1). London: Sage Publication.
- Google (2016). Computational thinking for educators. Erişim: https://computationalthinkingcourse.withgoogle.com/unit?lesson=8&unit=1
- Gulbahar, Y., Kalelioglu, F. & Kert, S. B. (2018). Teaching computational thinking to in-service computer science teachers through a massive open online course. In Proceedings of E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (pp. 922-928). Las Vegas, NV, United States: Association for the Advancement of Computing in Education (AACE). Erişim: January 7, 2021 from https://www.learntechlib.org/primary/p/185051/.
- Günüç, S., Odabaşı, F., & Kuzu, A. (2013). The defining characteristics of students of the 21st century by student teachers: A twitter activity. Journal of Theory and Practice in Education, 9(4), 436-455.
- Hambleton, R. K., & Patsula, L. (1999). Increasing the validity of adapted tests: myths to be avoided a guidelines for improving test adaptation practices. Journal of Applied Testing Technology, 15, 270-276.
- ISTE. (2015). CT leadership toolkit. Available at http://www.iste.org/docs/ct-documents/ct-leadershipt-toolkit.pdf?sfvrsn=4.
- Kandemir, C. M., Kalelioğlu, F., & Gülbahar, Y. (2021). Pedagogy of teaching introductory text‐based programming in terms of computational thinking concepts and practices. Computer Applications in Engineering Education, 29(1), 29-45. http://dx.doi.org/10.1002/cae.22374.
- Karasar, N. (1999). Bilimsel araştırma yöntemi: Kavramlar, ilkeler, teknikler. Ankara: Nobel Yayınevi.
- Kline, R. B. (2005). Principles and practice of structural equation modeling (2nd ed). New York: Guilford Press.
- Kong, S. C., Abelson, H., Sheldon, J., Lao, A., Tissenbaum, M., Lai, M., Lang, K., & Lao, N. (2017). Curriculum activities to foster primary school students' computational practices in block-based programming environments. In S. C. Kong, J. Sheldon&K. Y. Li (Eds.), Conference Proceedings of International Conference on Computational Thinking Education 2017 (pp. 84–89). Hong Kong: The Education University of Hong Kong.
- Korkmaz, Ö., Çakır, R., & Özden, M. Y. (2015). A validity and reliability study of the Computational Thinking Scales (CTS). Computers in Human Behavior Journal, 72, 558-569.
- Lockwood, J., & Mooney, A. (2017). Computational thinking in education: Where does it fit? A systematic literary review. Retrieved August 16, 2018 from https://arxiv.org/ftp/arxiv/papers/1703/1703.07659.pdf.
- Özden, M. Y. (2015). Computational thinking. Available at: http://myozden.blogspot.com.tr/2015/06/ computational-thinking-bilgisayarca.html. Erişim Tarihi: 08.10.2015
- Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th Ed.). United States: Pearson Education.
- Tabesh, Y. (2017). Computational thinking: A 21st century skill. Olympiads in Informatics, 11(SI), 65-70. URL: https://ioinformatics.org/journal/v11si_2017_65_70.pdf.
- Tissenbaum, M., Sheldon, J., & Sherman, M. (2018). The state of the field in computational thinking assessment. In To Appear in the Proceedings of the 2018 International Conference of the Learning Sciences. London.
- Wing, J. M. (2006). Computational thinking. Communication of Assosiation for Computing Machinery, 49, 33-35.
- Wing, J. M. (2008). Computational thinking and thinking about computing. Philosopical Transactions of the Royal Society A Mathematichal, Physical and Engineering Science, 366, 3717–3725 http://dx.doi.org/10.1098/rsta.2008.0118
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Studies on Education |
| Journal Section | Educational Sciences and Field Education Studies |
| Authors | |
| Publication Date | March 30, 2021 |
| Submission Date | February 9, 2021 |
| Acceptance Date | March 23, 2021 |
| Published in Issue | Year 2021 Volume: 7 Issue: 1 |
