Developing Computational Thinking Scale for Primary School Students and Examining Students' Thinking Levels According to Different Variables

Year 2023, Volume: 8 Issue: 1, 113 - 123, 08.01.2023

Ergün Yıldırım Çelebi Uluyol

https://doi.org/10.53850/joltida.1176173

Abstract

In recent years, computational thinking has been considered as one of the 21st century skills that all students should have. Researchers emphasize the importance of determining and developing students' computational thinking levels from the earliest possible age. However, no measurement tool has been found in the literature that aims to reveal the computational thinking levels of primary school students. In this study, it was aimed to develop the computational thinking scale for primary school students and to examine the computational thinking levels of primary school students according to different variables (grade level, daily computer use time). In the first stage of the study, a scale with appropriate psychometric properties was developed to measure computational thinking. In the scale development phase of the research, exploratory sequential mixed methods research design was used. In the other phase of the study, it was investigated whether the computational thinking levels of primary school students differed according to the grade level and daily computer usage time without any intervention. For this reason, the research was carried out in accordance with the general survey model, which is one of the descriptive research types. For the first stage, the study group of the research consisted of 287 students studying in the 1st, 2nd, 3rd and 4th grades of primary schools in Ankara Golbasi district in the second term of the 2021-2022 academic year. In the process of examining the students' computational. thinking levels according to the variables of grade level and daily computer usage time, the study group consisted of a total of 96 students attending the primary education classes of a private school in Ankara in the second term of the 2021-2022 academic year. In this context, the one-dimensional computational thinking scale consisting of 17 items was applied to 287 primary school students and the obtained data were subjected to validity and reliability analysis. According to the explanatory factor analysis, the scale explains 46% of the total variance. When the results of the explanatory factor analysis are examined, it is seen that the factor loads of 17 items in the scale vary between 56 and .86. The internal consistency coefficient of the scale was found to be Cronbach Alpha .92. The developed scale was applied to primary school students in the next stage. As a result of the study, it was found that the generally computational thinking levels of primary school students differed significantly according to the grade level. On the other hand, it was observed that the students' computational thinking levels differed significantly according to the time spent in front of the computer daily, and the mean of the students' computational thinking scale increased as the daily computer use time increased.

Keywords

Computational thinking, Scale development, Primary school, Grade level, Daily computer usage time

References

• Ambrosio, A. P., Almeida, L. S., Macedo, J., & Franco, A. H. R. (2014). Exploring core cognitive skills of computational thinking. Proceedings from Psychology of Programming Interest Group Annual Conference, 25-34.
• Barr, V. & Stephenson, C. (2011). Bringing computational thinking to K-12: What is involved and what is the role of the computer science education community? ACM Inroads, 2(1), 48- 54. http://dx.doi.org/10.1145/1929887.1929905.
• Berikan, B. (2018). Bilgi işlemsel düşünme becerisine yönelik tasarlanan 'veri setleriyle problem çözme' öğrenme deneyiminin biçimlendirici değerlendirmesi [Formative evaluation of 'problem solving with data sets' learning experience designed for computational thinking skills]. Doktora Tezi. Gazi Üniversitesi, Eğitim Bilimleri Enstitüsü, Ankara.
• Bryman, A. & Cramer, D. (2001). Quantitative data analysis with spss release 10 for windows. USA and Canada:.Routledge is an imprint of the Taylor & Francis Group.
• Buyukozturk, S. (2012). Sosyal bilimler için veri analizi el kitabı [Manual of data analysis for social sciences]. Ankara: Pegem Akademi.
• Buyukozturk, S., Akgun, O. E., Karadeniz, S. Demirel, F., & Kilic, E. (2016). Bilimsel araştırma yöntemleri [Scientific research methods]. Ankara: Pegem Akademi.
• Cohen, L., Manion, L., & Morrison, K. (2007). Research methods in education. London: Routledge/Taylor & Francis Group.
• Catana Kuleli, S. (2018). Öğretmen adaylarının çevrimiçi öğrenmeye hazırbulunuşluk düzeyleri ve bilgi işlemsel düşünme becerilerinin değerlendirilmesi [Evaluation of pre-service teachers' readiness for online learning and computational thinking skills]. Yüksek Lisans Tezi. Düzce Üniversitesi, Sosyal Bilimler Enstitüsü, Düzce.
• Demir, O. & Seferoglu, S. S. (2017). Yeni kavramlar, farklı kullanımlar: bilgi-işlemsel düşünmeyle ilgili bir değerlendirme [New concepts, different uses: An evaluation of computational thinking]. H. F. Odabası, B.
• Akkoyunlu ve A. Isman (Ed.), Eğitim teknolojileri okumaları, 2017 (ss. 468- 484) içinde. Ankara: TOJET-Sakarya Üniversitesi.
• DODEA- Department of Defense Education Activity, (2014). The 21st century principal. Retrieved January 13, 2021, from https://content.dodea.edu/teach_learn/professional_ development/21/docs/principals/principal_paper_draft.pdf
• Fraenkel, J. R. & Wallen, N. E. (2009). How to design and evaluate research in education. New York: McGraw-Hill.
• Ghasemi, A. & Zahediasl, S. (2012). Normality tests for statistical analysis: a guide for non-statisticians. International Journal of Endocrinology and Metabolism, 10, 486-489. https://doi.org/10.5812%2Fijem.3505
• Gouws, L. A., Bradshaw, K., & Wentworth, P. (2013). Computational thinking in educational activities: An evaluation of the educational game light-bot. Paper presented at the 18th ACM Conference on Innovation and Technology in Computer Science Education, Italy.
• Gravetter, F. J. & Forzano, L. B. (2012). Research methods for the behavioral sciences. Belmont, CA: Wadsworth.
• Grover, S. & Pea, R. (2013). Computational thinking in K–12: A review of the state of the field. Educational Researcher, 42(1), 38-43. http://dx.doi.org/10.3102/0013189X12463051
• Grover, S., Pea, R., & Cooper, S. (2015). “Systems of assessments” for deeper learning of computational thinking in K-12. Paper presented at the Annual Meeting of the American Educational Research Association, Chicago, United States of Amerika.
• Gulbahar, Y. Kert, S. B., & Kalelioglu F. (2019). Bilgi işlemsel düşünme becerisine yönelik öz yeterlik algısı ölçeği: Geçerlik ve güvenirlik çalışması [Self-efficacy perception scale for computational thinking skills: Validity and reliability study]. Türk Bilgisayar ve Matematik Eğitimi Dergisi 10(1), 1-29. https://doi.org/10.16949/turkbilmat.385097
• Hart, M. C. (1996). Improving the discrimination of SERVQUAL by using magnitude scaling. G. K. Kanji (Ed.), Total Quality Management in Action. London: Chapman and Hall.
• ISTE- International Society for Technology in Education, (2011). Computational thinking in K–12 education leadership toolkit. Retrieved February 12, 2022 from http:// www.iste.org/docs/ct documents/ctleadershipt-toolkit.pdf?sfvrsn=4.
• Kalelioglu, F., Gulbahar, Y., & Kukul, V. (2016). A framework for computational thinking based on a systematic research review. Baltic Journal of Modern Computing, 4(3), 583-596.
• Kalemkus, F. & Bulut Ozek, M. (2021). 21. yüzyıl becerileri konusunda araştırma eğilimleri: 2000-2020 [Research trends in 21st century skills: 2000-2020]. Manas Sosyal Araştırmalar Dergisi, 10(2), 878-900. https://doi.org/10.33206/mjss.774848
• Karasar, N. (2017). Bilimsel araştırma yöntemi [Scientific research method]. Ankara: Nobel Yayıncılık.
• Kilic, S. (2016). Cronbachs alpha reliability coefficient. Journal of Mood Disorders 6(1),1. https://doi.org/10.5455/jmood.20160307122823.
• Kline, P. (1994). An easy guide to factor analysis. London: Routledge.
• Koklu, N. (1997). Tutumların ölçülmesi ve likert tipi ölçeklerde kullanılan seçenekler [Measuring attitudes and options used in likert-type scales]. Ankara Üniversitesi Eğitim Bilimleri Fakültesi Dergisi, 28(2), 81-93.
• Korkmaz, O., Cakir, R., & Ozden, M. Y. (2017). A validity and reliability study of the computational thinking scales (CTS). Computers in Human Behavior, 72, 558-569. https://doi.org/10.1016/j.chb.2017.01.005
• Korkmaz, O., Cakir, R., & Ozden, M. Y., Oluk, A., & Sarioglu, S., (2015). Bireylerin bilgisayarca düşünme becerilerinin farklı değişkenler açısından incelenmesi [Examination of individuals' computational thinking skills in terms of different variables]. Ondokuz Mayıs Üniversitesi Eğitim Fakültesi Dergisi, 34(2), 68-87.
• Leech, N. L., Barrett, K. C., & Morgan, G. A. (2005). SPSS for Intermediate Statistics: Use and Interpretation. NJ: Lawrence Erlbaum Associates, Inc.
• MEB- Milli Egitim Bakanlıgı, (2018). Bilişim teknolojileri ve yazılım dersi öğretim programı (İlkokul 1, 2, 3 ve 4. sınıflar) [Information technologies and software curriculum (Primary School 1st, 2nd, 3rd and 4th grades)]. Ankara: Milli Eğitim Bakanlığı Yayınları.
• Miller, R. G. (1969). Simultaneous statistical inference. New York: McGraw-Hill.
• Oluk, A. & Korkmaz, O. (2016). Comparing students’ scratch skills with their computational thinking skills in terms of diff erent variables. I. J. Modern Education and Computer Science, 11, 1-7. http://dx.doi.org/10.5815/ijmecs.2016.11.01
• Ozbey, N. & Kuçukoglu, A. (2018). Ingiltere, Avustralya ve Türkiye’nin bilişim teknolojileri öğretim programlarının karşılaştırılması [The comparison of curriculum regarding ınformation technologies in England, Australia and Turkey]. International Journal of Innovative Approaches in Education, 2(3), 76-109. https://doi.org/10.29329/ijiape.2018.177.2
• Ozkan, R. & Bindak, R. (2021). Likert tipi ölçeklerde katılım düzeyi sayısındaki değişikliğin psikometrik özelliklerinin incelenmesi [Examining the psychometric properties of the change in the number of participation levels in Likert-type scales). Nicel Bilimler Dergisi, 3(2), 150-172. https://doi.org/10.51541/nicel.1028839
• Ozmen, B. (2016). Ortaokul öğrencilerine yönelik bilgi işlemsel düşünme becerileri testinin geliştirilmesi: geçerlik ve güvenirlik çalışması [Development of computational thinking skills test for secondary school students: validity and reliability study]. Conference: 4th International Instructional Technologies & Teacher Education Symposium. Elazığ.
• P21- Partnershipfor 21 Century Skills, (2009). P21 framework definitions. Retrieved February 02, 2022 from http://www.p21.org/storage/documents/P21Framework Definitions.pdf.
• Polat, C. (2006). Bilgi çağında üniversite eğitimi için bir açılım: Bilgi okuryazarlığı öğretimi [An opening for university education in the information age: Information literacy teaching]. A. Ü. Türkiyat Araştırmaları Enstitüsü Dergisi, 30, 249-266.
• Qualls, J. A. & Sherrell, L. B. (2010). Why computational thinking should be integrated into the curriculum. Journal of Computing Sciences in Colleges, 25(5), 66-71.
• Saritepeci, M. (2017). Ortaöğretim düzeyinde bilgi-işlemsel düşünme becerisinin çeşitli değişkenler açısından incelenmesi [Examination of computational thinking skills at secondary education level in terms of various variables]. Fifth International Instructional Technologies Teacher Education Symposium (ITTES), İzmir, Türkiye.
• Scherer, R. F., Wiebe, F. A., Luther, D. C., & Adams, J. S. (1988). Dimensionality of coping: Factor stability using the Ways of Coping Questionnaire. Psychological Reports, 62(3), 763–770. https://doi.org/10.2466/pr0.1988.62.3.763
• Seiter, L., & Foreman, B. (2013). Modeling the learning progressions of computational thinking of primary grade students. Proceedings of the Ninth Annual International ACM Conference on International Computing Education Research, 59-66.
• Selby, C. & Woollard, J. (2013). Computational thinking: the developing definition. Special Interest Group on Computer Science Education (SIGCSE), Atlanta GA.
• Sim, J. & Wright, C. (2002). Research in health care: concepts, designs and methods. United Kingdom, Cheltenham: Nelson Thornes.
• Singh, K. (2007). Quantitative social research methods. Los Angeles: Sage Publications. Snow, E., Katz, I., Elliott-Tew, A., & Feldman, J. (2012). Assessing computational thinking. NSF-CE21 Community Meeting, Washington, United States of America.
• Sendurur, P. (2012). Identification of factors affecting integration of information and communication technologies in basic education schools grades from 4 through 8. Doktora Tezi. Orta Doğu Teknik Üniversitesi, Fen Bilimleri Enstitüsü, Ankara.
• Tavsancil, E. (2010). Tutumların ölçülmesi ve SPSS ile veri analizi [Measuring attitudes and data analysis with SPSS]. Ankara: Nobel Yayın Dağıtım.
• Werner, L., Denner, J., Campe, S., & Kawamoto, D. C. (2012). The fairy performance assessment: measuring computational thinking in middle school. Proceedings of the 43rd ACM Technical Symposium on Computer Science Education, 215-220.
• Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35. https://doi.org/10.1145/1118178.1118215
• Wing, J. M. (2008). Computational thinking and thinking about computing. Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 366(1881), 3717-3725. https://doi.org/10.1098/rsta.2008.0118
• Wing, J. M. (2011). Research notebook: computational thinking -what and why? The Link Magazine, 20-23. Retrieved January 10, 2022 from https://www.cs.cmu.edu/link/research-notebook-computational-thinking-what-andwhy
• Yildiz Durak, H. & Saritepeci, M. (2018). Analysis of the relation between computational thinking skills and various variables with the structural equation model. Computers & Education, 116, 191–202. https://doi.org/10.1016/j.compedu.2017.09.004
• Yildiz, M. (2021). Bilgi işlemsel düşünme becerisinin süreç temelli ölçülmesi ve değerlendirilmesi [Process-based measurement and evaluation of computational thinking skills]. Doktora Tezi. Trabzon Üniversitesi, Lisansüstü Eğitim Enstitüsü, Trabzon.
• Yolcu, V. (2018). Programlama eğitiminde robotik kullanımının akademik başarı, bilgi-işlemsel düşünme becerisi ve öğrenme transferine etkisi [The effect of using robotics in programming education on academic achievement, computational thinking skills and learning transfer]. Yüksek Lisans Tezi. Süleyman Demirel Üniversitesi, Eğitim Bilimleri Enstitüsü, Isparta.