Bilgi İşlemsel Düşünmenin Matematik Eğitimine Entegre Edilmesi: Başarı, Motivasyon ve Öğrenme Stratejileri Üzerindeki Etkileri

Year 2024, , 2034 - 2066, 30.06.2024

Onur Top , Taner Arabacıoglu

https://doi.org/10.35675/befdergi.1385749

Abstract

Bu çalışmanın amacı, BİD etkinlikleri ile desteklenmiş matematik öğretiminin matematik başarısı, motivasyon ve öğrenme stratejileri üzerindeki etkisini araştırmaktır. Bu çalışma için öntest-sontest kontrol gruplu yarı deneysel bir araştırma deseni kullanılmıştır. Araştırmanın çalışma grubunu ortaokul altıncı sınıf öğrencileri oluşturmaktadır. Dersler deney grubuna BİD görevleri aracılığıyla verilmiştir. BİD görevleri, blok tabanlı bir kodlama aracı kullanılarak gerçekleştirilmiştir. Sonuçlar, deney grubunun kontrol grubuna göre çok daha yüksek matematik başarısına sahip olduğunu göstermektedir. Ayrıca, motivasyon ölçeğinin öğrenme kontrolü inancı alt boyutunda ve öğrenme stratejileri ölçeğinin zaman ve çalışma ortamı alt boyutunda deney grubu lehine önemli farklılıklar bulunmuştur. Bu araştırmanın sonuçları, BİD etkinlikleri ile desteklenen matematik öğretiminin öğrencilerin matematik başarılarını artırmada etkili olduğunu göstermektedir. Ayrıca, öğrencilerin çalışma zamanlarını ve ortamlarını etkili bir şekilde düzenlemelerine yardımcı olmaktadır. Aynı zamanda öğrenme hedeflerinin başarılı sonuçlar sağladığı inancını da pekiştirmektedir. Bu nedenle, bilgisayar laboratuvarları matematik öğretimi için önemli alternatif olarak düşünülmelidir.

Keywords

Bilgi işlemsel düşünme, scratch, motivasyon, öğrenme stratejileri.

Integrating Computational Thinking into Mathematics Education: Its Effects On Achievement, Motivation, And Learning Strategies

Abstract

This study aimed to explore the impact of mathematics instruction supplemented with CT activities on mathematics achievement, motivation, and learning techniques. A quasi-experimental research design involving a pretest-posttest control group was used for the present study. Research was carried out in a Turkish middle school with sixth-grade pupils in a mathematics class. The courses were provided via CT tasks to the experimental group. CT tasks were performed using a scratch-block-based coding tool. The results showed that the experimental group had much higher mathematical performance than the control group. Furthermore, substantial differences were discovered in favor of the experimental group in the motivation scale sub-dimension of learning control belief and the learning methods scale sub-dimension of time and study environment. The results of this research show that mathematics instruction supplemented with CT activities is effective in enhancing students' mathematical achievement. This helps students to organize their study time and environment effectively. This also reinforces the belief that learning objectives provide successful outcomes. Thus, computer laboratories should be considered essential alternatives for mathematical instruction.

Keywords

Computational thinking, scratch, mathematics education, motivation, learning strategies.

References

• Akkuş-Çakır, N., & Senemoğlu, N. (2016). Analytical thinking skills in higher education. Kastamonu Eğitim Dergisi, 24(3), 1487-1502.
• Angeli, C., Voogt, J., Fluck, A., Webb, M., Cox, M., Malyn-Smith, J., & Zagami, J. (2016). A K-6 computational thinking curriculum framework: Implications for teacher knowledge. Journal of Educational Technology & Society, 19(3), 47-57.
• Aslan, Ö. (2007). Bilgi toplumunda teknolojinin ve teknoloji politikalarının yeri (Tez No. 217574) (Doktora Tezi, İstanbul Üniversitesi- İstanbul). Yükseköğretim Kurulu Başkanlığı Tez Merkezi.
• Atmatzidou, S., & Demetriadis, S. (2016). Advancing students’ computational thinking skills through educational robotics: A study on age and gender relevant differences. Robotics and Autonomous Systems, 75, 661–670. https://doi.org/10.1016/j.robot.2015.10.008.
• Bandura, A. (1997). Self-efficacy: The exercise of control. W H Freeman/Times Books/ Henry Holt & Co.
• 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.
• Başün, A. R. (2016). Oyunla Öğretimin Çarpanlar ve Katlar Alt Öğrenme Alanında Başarı ve Kalıcılığa Etkisi. (Tez No. 442978) (Yüksek Lisans Tezi, Ondokuz Mayıs Üniversitesi- Samsun). Yükseköğretim Kurulu Başkanlığı Tez Merkezi.
• Beletti, C., & Vaillant, D. (2022). Self-regulation and learning strategies of beginner and advanced university students. Cuadernos de Investigación Educativa, 13(2). https://doi.org/10.18861/cied.2022.13.2.3255.
• Betthäuser, B. A., Bach-Mortensen, A.M., & Engzell, P. A. (2023). Systematic review and meta-analysis of the evidence on learning during the COVID-19 pandemic. Nat Hum Behav. 7(3), 375-385. https://doi.org/10.1038/s41562-022-01506-4.
• Bindak, R. (2014). Mann-whitney u ile student’s t testinin i.tip hata ve güç bakımından karşılaştırılması: Monte carlo simülasyon çalışması. Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi. 14, 5-11. https://doi.org/ 10.5578/fmbd.7380.
• Bounou, A., Lavidas, K., Komis, V., Papadakis, S., & Manoli, P. ( 2023). Correlation between high school students’ computational thinking and their performance in stem and language courses. Educ.Sci.,13, 1101. https://doi.org/10.3390/educsci13111101
• Büyüköztürk, Ş., Akgün, Ö. E., Özkahveci, Ö. & Demirel, F. (2004). Güdülenme ve öğrenme stratejileri ölçeğinin Türkçe formunun geçerlik ve güvenirlik çalışması. Kuram ve Uygulamada Eğitim Bilimleri, 4(2), 207-239.
• Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. WW Norton & Company.
• Cansu, S., & Cansu, F. (2019). An overview of computational thinking. International Journal of Computer Science Education in Schools, 3(1), 1-.11. https://doi.org/10.21585/ijcses.v3i1.53.
• Caeiro-Rodríguez, M., Manso-Vázquez, M., Jesmin, T., Terasmaa, J., Tsalapata, H., Heidmann, O., Okkonen, J., White, E., de Carvalho, C.V., & Stefan, I.-A. (2022). Students and teachers’ need for sustainable education: Lessons from the pandemic. Computers, 11, 157. https://doi.org/10.3390/ computers11110157.
• Chen, C. (2009). Self-regulatedl strategies and achievement in an introduction to information systems course. Information Technology, Learning, and Performance Journal, 20(1), 11–25.
• Chongo, S., Osman, K., & Nayan, N. (2020). Level of computational thinking skills among secondary science student: Variation across gender and mathematics achievement. Science Education International, 31(2), 159-.163. https://doi.org/10.33828/sei.v31.i2.4.
• Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2009). Introduction to algorithms (3rd ed.). MIT Press. Cooligan, H. (2009). Research Methods and Statistics in Psychology (5th ed). Hodder Education Group. https://doi.org/10.4324/9780203769836.
• Christensen, L. B., Johnson, R. B. & Turner, L. A. (2015). Araştırma yöntemleri desen ve analiz. (A. Aypay, Çeviri Editörü). Anı Yayıncılık.
• Curzon, P., Bell, T., Waite, J., & Dorling, M. (2019). Computational thinking. In S. A. Fincher & A. Robins (Eds.) The Cambridge handbook of computing education research. (pp. 513-- 546). Cambridge University Press.
• Çetin, Y., & Mirasyedioğlu, Ş. (2019). The effects of the technology Supported problem-based learning activities on students’ achievement in mathematics. Journal of Computer and Education Research, 7(13), 13-.34. https://doi.org/ 10.18009/jcer.494907.
• Çubukçu, Z. (2011). Düşünme becerileri. S. B. Filiz (Ed.), Öğrenme öğretme kuram ve yaklaşımları. (ss.281-331). Pegem Akademi.
• Çetin, İ. & Toluk Uçar, Z. (2020). Bilgi İşlemsel Düşünme Tanımı ve Kapsamı. In Y. Gülbahar (Eds.), Bilgi İşlemsel Düşünmeden Programlamaya (pp. 41-78). Pegem Akademi. https://doi.org/10.14527/9786052411117
• Davenport, T. H., & Kirby, J. (2015). Beyond automation: Strategies for remaining gainfully employed in an era of very smart machines. Harvard Business Review, 93(6), 58-65.
• Denning, P. (2009). The profession of IT beyond computational thinking. Communications of the ACM, 52(6), 28-30. https://doi.org/10.1145/1516046.1516054.
• DePryck, K. (2016, November 2-4). From computational thinking to coding and back. Fourth International Conference on Technological Ecosystems for Enhancing Multiculturality (TEEM’16), Salamanca, Spain.
• Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School Engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74(1), 59–109. https://doi.org/10.3102/00346543074001059.
• Fong, C. J. (2022). Academic motivation in a pandemic context: A conceptual review of prominent theories and an integrative model. Educational Psychology, 42(10), 1204-1222. https://doi.org/10.1080/01443410.2022.202689.1.
• Gliner, J. A., Morgan, G. A. & Leech, N. L. (2016). Uygulamada Araştırma Yöntemleri. (S. Turan, Çeviri Editörü). Nobel Yayıncılık.
• Grover, S., & Pea, R. (2013). Computational thinking in K–12: A review of the state of the field. Educational Researcher, 42(1), 38-43.
• Grover, S., & Pea, R. (2018). Computational thinking: A competency whose time has come. In S. Sentance, E. Barendsen, & C. Schulte (Eds.), Computer Science Education: Perspectives on Teaching and Learning in School (p. 20-38). Bloomsbury Publishing.
• Guzdial, M. (2008). Education paving the way for computational thinking. Communucations of the ACM, 25-27. https://doi.org/10.1145/1378704.1378713.
• Güneş, F. (2012). Öğrencilerin düşünme becerilerini geliştirme. Türklük Bilimi Araştırmaları, 32, 127-146. Helsa, Y., Superman., Juandi, D., Turmudi., & Ghazali, M. B. (2023). A meta-analysis of the utilization of computer technology in enhancing computational thinking skills: Direction for mathematics learning. International Journal of Instruction, 16(2), 735-758. https://doi.org/10.29333/iji.2023.16239a.
• Hoppe, H., & Werneburg, S. (2019). Computational thinking—more than a variant of scientific inquiry!. In A. C. Kong & H. Abelson (eds), Computational thinking education. Springer. https://doi.org/10.1007/978-981-13-6528-7_2
• Husnah, A. U., Hidayat, M. A., & Jannah, M. (2021). The Journey of a math: As a mathematics learning innovation. Indonesian Journal of Multidiciplinary Research, 1(1), 129-136. https://doi.org/10.17509/ijomr.v1i1.33814.
• ISTE (2011). Computational Thinking. Operational Definition. Retrieved October 13, 2023, from https://cdn.iste.org/wwwroot/Computational_Thinking_Operational_Definition_ ISTE.pdf.
• Israel-Fishelson, R., & Hershkovitz, A. (2022). Studying interrelations of computational thinking and creativity:A scoping review (2011–2020). Computers & Education, 176, 104353. https://doi.org/10.1016/j.compedu.2021.104353.
• Kakavas, P., & Ugolini, F. (2019). Computational thinking in primary education: a systematic literatüre review. Research on Education and Media, 11(2), 64-94. https://doi.org/10.2478/rem-2019-0023.
• Kanaki, K., & Kalogiannakis, M. (2022). Assessing algorithmic thinking skills in relation to age in early childhood stem education. Educ. Sci., 12, 380. https://doi.org/10.3390/educsci12060380.
• Kelleher, C., & Pausch, R. (2005). Lowering the barriers to programming: A taxonomy of programming environments and languages for novice programmers. ACM Computing Surveys, 37(2), 83-137.
• Kert, S. B. (2020). Bilgisayar bilimi eğitimine giriş. In Y. Gülbahar (Eds), Bilgi işlemsel düşünmeden programlamaya (pp. 1-22). Pegem Akademi Yayınları. https://doi.org/10.14527/9786052411117.
• Korkmaz, Ö., Çakir, R., & Özden, 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
• Kızılkaya, G., & Aşkar, P. (2009). Problem çözmeye yönelik yansıtıcı düşünme becerisi ölçeğinin geliştirilmesi. Eğitim ve Bilim, 34(154), 82-92.
• Kramer, J. (2007). Is abstraction the key to computing? Commun. ACM, 50, 36–42. https://doi.org/10.1145/1232743.1232745.
• Lei, H., Chiu, M.M., Li, X., Wang, X. & Geng, Y. (2022). Computational thinking and academic achievement: A meta-analysis among students, Children and Youth Services Review, 118, 105439. https://doi.org/10.1016/j.childyouth.2020.105439.
• Liu, C., Shi, Y., & Wang, Y. (2022, May 27-29). Self-determination theory in education: The relationship between motivation and academic performance of primary school, high school, and college students. 3rd International Conference on Mental Health, Education and Human Development, Dalian, China.
• Lv, L., Zhong, B. & Liu, X. (2023) A literature review on the empirical studies of the integration of mathematics and computational thinking. Educ Inf Technol, 28, 8171-8193. https://doi.org/10.1007/s10639-022-11518-2.
• Manavipour, D., & Saeedian, Y. (2016). The role of self-compassion and control belief about learning in university students' self-efficacy. Journal of Contextual Behavioral Science, 5(2), 121–126. https://doi.org/10.1016/j.jcbs.2016.02.003.
• Mindetbay, Y., Bokhove, C., & Woollard, J. (2019). What is the relationship between students’ computational thinking performance and school achievement?. International Journal of Computer Science Education in Schools, 2(5), 3–19. https://doi.org/10.21585/ijcses.v0i0.45
• Mirolo, C., Izu, C., Lonati, V., & Scapin, E. (2022). Abstraction in computer science education: An overview. Informatics in Education, 20(4), 615-639. https://doi.org/10.15388/infedu.2021.27.
• Nordby, S. K., Bjerke, A. H., Mifsud, L. (2022). Computational thinking in the primary mathematics classroom: A systematic review. Digital Experiences in Mathematics Education, 8, 27-49. https://doi.org/10.1007/s40751-022-00102-5.
• Pintrich, P., Smith, D., García, T., & McKeachie, W. (1991). A manual for the use of the motivated strategies for learning questionnaire (MSLQ). Ann Arbor, MI: University of Michigan.
• Pintrich, P. R., Smith, D. A., Garcia, T., Mckeachie, W. J. (1993). Reliability and predictive validity of the motivated strategies for learning questionnaire (MSLQ). Educational and Psychological Measurement, 53(3), 801-813. https://doi.org/10.1177/0013164493053003024.
• Pintrich, P. R. (2000). The role of goal orientation in self-regulated learning. In M. Boekaerts, P. R. Pintrich & M. Zeidner (Eds.), Handbook of self-regulation (pp. 451–502). Academic Press. https://doi.org/10.1016/B978-012109890-2/50043-3.
• Pintrich, P. R. (2004). A conceptual framework for assessing motivation and selfregulated learning in college students. Educ. Psychol. Rev. 16, 385–407. https://doi.org/ 10.1007/s10648-004-0006-x.
• Rabiee, M., & Tjoa, A.M. (2017, May 22-24). From abstraction to implementation: Can computational thinking improve complex real-world problem solving? A Computational Thinking-Based Approach to the SDGs. Information and Communication Technologies for Development. ICT4D 2017, Yogyakarta, Indonesia.
• Refvik, K. A. S. & Bjerke, A. H. (2022). Computational thinking as a tool in primary and secondary mathematical problem solving: a literature review. Nordic Studies in Mathematics Education, 27(3), 5–27.
• Reimers, F. M. (2022). Learning from a Pandemic. The Impact of COVID-19 on education around the world. In: Reimers, F.M. (eds) Primary and Secondary education during Covid-19. Springer. https://doi.org/10.1007/978-3-030-81500-4_1.
• Schunk, D. H. (1991). Self-efficacy and academic motivation. Educational Psychologist, 26, 207-231.
• Schunk, D. H., DiBenedetto, M. K. (2016). Self-efficacy theory in education. In K. R., Wentzel & D. B. Miele, (Eds.), Handbook of motivation at School. Routledge.
• Seymour, P. (1980). Mindstorms: Children, computers, and powerful ideas. Basic Books.
• Shute, V.J., 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.
• Solomon, C., Harvey, B., Kahn, K., Lieberman, H., Miller, M. L., Minsky, M.,. Silverman, B. (2020). History of Logo. Proceedings of the ACM on Programming Languages, 4(HOPL), 1-66. https://doi.org/10.1145/3386329
• Taslibeyaz, E., Kursun, E. & Karaman, S. (2020). How to Develop Computational Thinking: A Systematic Review of Empirical Studies. Informatics in Education, 19(4), 701–719. https://doi.org/ 10.15388/infedu.2020.30.
• Tok, E., & Sevinç, M. (2010). Düşünme Becerileri Eğitiminin Eleştirel Düşünme ve Problem Çözme Becerilerine Etkisi. Pamukkale Üniversitesi Eğitim Fakültesi Dergisi, 27(27), 67-82.
• Tadese, M., Yeshaneh, A. & Mulu, G.B. (2022) Determinants of good academic performance among university students in Ethiopia: a cross-sectional study. BMC Med Educ, 22, 395. https://doi.org/10.1186/s12909-022-03461-0.
• Top, E. (2020). Düşünme Becerilerinin Önemi. Y. Gülbahar (Ed.), Bilgi İşlemsel Düşünmeden Programlamaya (4. baskı, s. 23-40). Pegem Akademi Yayınları. https://doi.org/10.14527/9786052411117.
• Tosik-Gün, E. & Güyer, T. (2019). Bilgi İşlemsel Düşünme Becerisinin Değerlendirilmesine İlişkin Sistematik Alanyazın Taraması. Ahmet Keleşoğlu Eğitim Fakültesi Dergisi (AKEF), 1(2), 99-120. https://doi.org/10.38151/akef.597505.
• Üzümcü, Ö. & Bay, E. (2018). Eğitimde Yeni 21. Yüzyıl Becerisi: Bilgi İşlemsel Düşünme. Uluslararası Türk Kültür Coğrafyasında Sosyal Bilimler Dergisi, 3(2), 1-16.
• Üzümcü, Ö. (2019). Bilgi İşlemsel Düşünme Becerisine Yönelik Program Tasarımının Geliştirilmesi ve Etkililiğinin Değerlendirilmesi. (Tez No. 541874) [Doktora Tezi, Gaziantep Üniversitesi - Gaziantep]. Yükseköğretim Kurulu Başkanlığı Tez Merkezi.
• Voon, X., Wong, S., Wong, L-H., Khambari., M., & Syed-Abdullah, S. (2022). Developing Computational Thinking Competencies through Constructivist Argumentation Learning: A Problem-Solving Perspective. International Journal of Information and Education Technology, 12(6), 529–539. https://doi.org/10.18178/ijiet.2022.12.6.1650.
• Walden, J., Doyle, M., Garns, R., & Hart, Z. (2013. July 1-3). An informatics perspective on computational thinking. 18th ACM conference on Innovation and technology in computer science education, Canterbury, England.
• Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35.
• Wing, J. M. (2008). Computational thinking and thinking about computing. Philosophical Transactions of the Royal Society A, 366, 3717-3725. http://doi.org/10.1098/rsta.2008.0118.
• Wing, J. M. (2011). Research Notebook: Computational thinking—what and why. The link Magazine, 6, 20-23.
• Yadav, A., Mayfield, C., Zhou, N., Hambrusch, S., & Korb, J. T. (2014). Computational thinking in elementary and secondary teacher education. ACM Transactions on Computing Education (TOCE), 14(1), 1-5.
• Yadav, A., Ocak, C., & Oliver, A. (2022). Computational thinking and metacognition. TechTrends 66, 405–411. https://doi.org/10.1007/s11528-022-00695-z.
• Yaman S., & Yalçın, N. (2005). Fen Bilgisi öğretiminde probleme dayalı öğrenme yaklaşımının yaratıcı düşünme becerisine etkisi. İlköğretim Online, 4(1), 42-52.
• Yang Y, Du J, Teo T, Xue, S., & Liu, F. (2023). Effects of goal orientation on environment management in technology-based physics learning. Front. Psychol. 13:1048143. https://doi.org/10.3389/fpsyg.2022.1048143.
• Ye, J., Lai, X. & Wong, G. (2022). The transfer effects of computational thinking: A systematic review with meta-analysis and qualitative synthesis. Journal of Computer Assisted Learning, 38, 1620–1638. https://doi.org/10.1111/jcal.12723.