Üniversite Öğrencilerinin Problem Çözme ve Algoritmik Düşünme Beceri Düzeylerinin İncelenmesi: Çanakkale Teknik Bilimler MYO Örneği

Year 2022, Volume: 22 Issue: 4, 1607 - 1620, 29.12.2022

Ümit Demir

https://doi.org/10.17240/aibuefd.2022..-781021

Abstract

Bu araştırmada programlamanın temelleri ve matematik dersi almış olan önlisans öğrencilerinin algoritmik düşünme yeterlilikleri ile problem çözme becerileri belirlenerek bu iki yeterlilik arasındaki ilişkinin saptanması amaçlanmıştır. Bu çalışmada, betimsel araştırma yöntemlerinden tarama araştırma modeli kullanılmıştır. Araştırma kapsamında Hepner ve Petersen (1982) tarafından hazırlanan Taylan (1990) tarafından Türkçeye uyarlaması gerçekleştirilen problem çözme ölçeği ile araştırmacı tarafından geliştirilen algoritma başarı testi kullanılmıştır. Geliştirilen başarı testinin ön uygulama güvenirlik katsayısı 0,70 olarak saptanmıştır. Çalışmanın örneklemi Çanakkale Onsekiz Mart Üniversitesi Teknik Bilimler MYO’ da Bilgisayar Teknolojileri bölümünde öğrenimlerini sürdürmekte olan 52 öğrencidir. Araştırma sonucunda; bilgisayar teknolojileri alan öğrencilerinin algoritmik düşünme beceri puan ortalaması 64,04 (100 puan üzerinden), problem çözme beceri puan ortalamaları 151,64 (210 puan üzerinden) olarak bulunmuştur. Öğrencilerin algoritmik düşünme yeterliliklerinin programlamanın temelleri (F(7-44)=2,733; p<0,05) ve matematik dersi (F(8-43)=3,080; p<0,01) notlarına göre anlamlı düzeyde farklılık gösterirken, cinsiyet (t =-0,618; p>0,05) durumuna göre ise anlamlı farklılık göstermediği bulunmuştur. Problem çözme becerilerinin ise cinsiyet (t =-0,053; p>0,05) ve matematik (F(8-43)=1,876; p>0,05) ders notlarına göre anlamlı farklılık göstermezken, programlama temelleri (F(7-44)=2,080; p<0,05) dersi notlarına göre anlamlı farklılık gösterdiği bulunmuştur. Algoritma başarısı ile problem çözme becerisi arasında ise orta düzeyde pozitif yönde (r=0,423; p<0,01) anlamlı bir ilişki olduğu belirlenmiştir.

Keywords

Problem Çözme, Algoritmik Düşünme, Programlama, Kodlama, Problem-solving, algorithmic thinking, programming, coding

Thanks

.

Investigation of the Problem Solving and Algorithmic Thinking Skill Levels of University Students: Sample of Canakkale Vocational College of Technical Sciences

Abstract

This research was carried out in order to determine the relationship between these two competencies by determining the algorithmic thinking competencies and problem-solving skills of university students who have taken the basics of programming and mathematics at the associate degree education level. In this study, a survey research model, one of the descriptive research methods, was used. Within the scope of the research, the problem-solving scale prepared by Hepner and Petersen (1982) and adapted into Turkish by Taylan (1990) and the algorithm achievement test developed by the researcher were used. The pre-application reliability coefficient of the developed achievement test was determined as 0.70. The sample of the study is 52 students who are continuing their education in the Department of Computer Technologies at Çanakkale Onsekiz Mart University Technical Sciences Vocational School. As a result of the research; The mean score of algorithmic thinking skills of computer technology students was found to be 64.04 (out of 100 points), and the mean of problem-solving skills was found to be 151.64 (out of 210 points). While students' algorithmic thinking competencies differ according to their grades in programming fundamentals (F(7-44)=2.733, p<0.05) and mathematics course (F(8-43)=3.080, p<0.01), gender (t =-0.618, p > 0.05) did not differ according to the condition. While problem solving skills did not differ significantly according to gender (t =-0.053, p>0.05) and mathematics (F(8-43)=1.876; p>0.05) course grades, programming fundamentals (F(7-44)=2.080, p<0.05) differed according to course grades. It was determined that there was a moderately positive (r=0.423; p<0.01) significant relationship between algorithm success and problem-solving skills.

Keywords

Problem-solving, algorithmic thinking, programming, coding

References

• Akpınar, Y., ve Altun, A. (2014). Bilgi toplumu okullarında programlama eğitimi gereksinimi. İlköğretim Online, 13(1),1-4.
• Ala-Mutka, K. (2004). Problems in learning and teaching programming-a literature study for developing visualizations in the Codewitz-Minerva project. Codewitz needs analysis, 20.
• Balanskat, A., ve Engelhardt, K. (2015). Computing our future. Computer programming and coding. Priorities, school curricula and initiatives across Europe. European Schoolnet, Brussels.
• Bergersen, G. R. ve Gustafsson, J. E. (2011). Programming skill, knowledge, and working memory among professional software developers from an investment theory perspective. Journal of Individual Differences, 32(4), 201-209.
• Bozkurt, F., ve Çoşkun, D. (2018). 21. YY Okuryazarlığı: Öğretmen Adaylarının Medya Algılarına Genel Bir Bakış. Erciyes İletişim Dergisi, 5(4), 493-511.
• Brown, Q., Mongan, W., Kusic, D., Garbarine, E., Fromm, E., ve Fontecchio, A. (2013). Computer aided instruction as a vehicle for problem solving: Scratch programming environment in the middle years classroom. Retrieved September, 22(6.1), 1.
• Choi, J., Lee, Y., ve Lee, E. (2017). Puzzle based algorithm learning for cultivating computational thinking. Wireless Personal Communications, 93(1), 131-145.
• Clements, D. H., ve Sarama, J. (2003). Strip mining for gold: Research and policy in educational technology—A response to “Fool’s Gold”. AACE Journal, 11(1), 7-69.
• Coull, N. J., ve Duncan, I. M. (2011). Emergent requirements for supporting introductory programming. Innovation in Teaching and Learning in Information and Computer Sciences, 10(1), 78-85.
• Crescenzi, P., Malizia, A., Verri, M. C., Díaz, P., ve Aedo, I. (2012). Integrating algorithm visualization video into a first-year algorithm and data structure course. Journal of Educational Technology ve Society, 15(2), 115-124.
• Doğan, U., ve Kert, S. B. (2016). Bilgisayar oyunu geliştirme sürecinin, ortaokul öğrencilerinin eleştirel düşünme becerilerine ve algoritma başarılarına etkisi. Boğaziçi Üniversitesi Eğitim Dergisi, 33(2), 21-42.
• Erümit, K. A., Karal, H., Şahin, G., Aksoy, D. A., Aksoy, A., ve Benzer, A. İ. (2018). Programlama öğretimi için bir model önerisi: yedi adımda programlama. Eğitim ve Bilim, 44(197), 155-183.
• Fesakis, G. ve Serafeim, K. (2009). Influence of the familiarization with scratch on future teachers’ opinions and attitudes about programming and ICT in education. ACM SIGCSE Bulletin, 41(3), 258- 262.
• Futschek, G. (2006). Algorithmic thinking: the key for understanding computer science. In International conference on informatics in secondary schools evolution and perspectives (pp. 159-168). Springer, Berlin, Heidelberg.
• Grover, S., ve Pea, R. (2013). Computational thinking in K–12: A review of the state of the field. Educational researcher, 42(1), 38-43.
• Helminen, J., ve Malmi, L. (2010, October). Jype-a program visualization and programming exercise tool for Python. In Proceedings of the 5th international symposium on Software visualization (pp. 153-162). ACM.
• International Society for Technology in Education. (2016). ISTE standards for students. Erişim Adresi http://www.iste.org/standards/standards/for-students-2016
• Kafai, Y. B., ve Burke, Q. (2014). Connected code: Why children need to learn programming. Mit Press.
• Kalelioğlu, F., ve Gülbahar, Y. (2014). The Effects of Teaching Programming via Scratch on Problem Solving Skills: A Discussion from Learners' Perspective. Informatics in Education, 13(1), 33-50.
• Kim, B., Kim, T., ve Kim, J. (2013). and-Pencil Programming Strategy toward Computational Thinking for Non-Majors: Design Your Solution. Journal of Educational Computing Research, 49(4), 437-459.
• Kobsiripat, W. (2015). Eff ects of the media to promote the scratch programming capabilities creativity of elementary school students. Procedia-Social and Behavioral Sciences, 174, 227- 232.
• Korkmaz, Ö. (2012). The impact of critical thinking and logico-mathematical intelligence on algorithmic design skills. Journal of Educational Computing Research, 46(2), 173-193.
• Korkmaz, Ö., Çakır, R., Özden, M. Y., Oluk, A., ve Sarıoğlu, S. (2015). Bireylerin bilgisayarca düşünme becerilerinin farklı değişkenler açısından incelenmesi. Ondokuz Mayıs Üniversitesi Eğitim Fakültesi Dergisi, 34(2), 68-87.
• Lahtinen, E., Ala-Mutka, K. ve Jarvinen, H. (2005) A Study of Difficulties of Novice Programmers. In Acm Sigcse Bulletin, ACM, 37(3), 14-18.
• Lai, C. S., ve Lai, M. H. (2012). Using computer programming to enhance science learning for 5th graders in Taipei. In 2012 International Symposium on Computer, Consumer and Control (pp. 146-148). IEEE.
• Lai, A. F., ve Yang, S. M. (2011). The learning effect of visualized programming learning on 6 th graders' problem solving and logical reasoning abilities. In 2011 International Conference on Electrical and Control Engineering (pp. 6940-6944). IEEE.
• Law, K. M., Lee, V. C. ve Yu, Y. T. (2010). Learning motivation in e-learning facilitated computer programming courses. Computers ve Education, 55(1), 218-228.
• Lye, S. Y., ve 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.
• Maddrey, E. (2011). The Effect of Problem-Solving Instruction on the Programming Self-efficacyand Achievement of Introductory Computer Science Students. Nova Southeastern University doctoral dissertation.
• Oluk, A., Korkmaz, Ö., ve Oluk, H. (2018). Scratch’ın 5. Sınıf Öğrencilerinin Algoritma Geliştirme ve Bilgi-İşlemsel Düşünme Becerilerine Etkisi. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 9 (1), 54-71.
• Psycharis, S., ve Kallia, M. (2017). The effects of computer programming on high school students’ reasoning skills and mathematical self-efficacy and problem solving. Instructional Science, 45(5), 583-602.
• Sáez-López, J. M., Román-González, M., ve Vázquez-Cano, E. (2016). Visual programming languages integrated across the curriculum in elementary school: A two year case study using “Scratch” in five schools. Computers ve Education, 97, 129-141.
• Sanford, J. F., ve Naidu, J. T. (2016). Computational thinking concepts for grade school. Contemporary Issues in Education Research, 9(1), 23-32.
• Sayın, Z., ve Seferoğlu, S. S. (2016). Yeni bir 21. yüzyıl becerisi olarak kodlama eğitimi ve kodlamanın eğitim politikalarına etkisi. Akademik Bilişim Konferansı, 3-5.
• Shih, I. J. (2014). The effect of scratch programming on the seventh graders’ mathematics abilities and problem solving attitudes (Yayımlanmamış yüksek lisans tezi). Taipei University, Taiwan.
• Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (Sixth edition). United States: Pearson Education.
• Trilling, B., ve Fadel, C. (2009). 21st Century Skills.: Learning for Life in Our Times. John Wiley ve Sons.
• Utting, I., Cooper, S., Kölling, M., Maloney, J., ve Resnick, M. (2010). Alice, greenfoot, and scratch--a discussion. ACM Transactions on Computing Education (TOCE), 10(4), 17.
• Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35.
• Wing, J.M. (2017). Computational thinking’s influence on research and education for all. Italian Journal of Educational Technology, 25(2), 7-14.
• Yıldız, M., Çiftçi, E., ve Karal, H. (2017). Bilişimsel düşünme ve programlama. Eğitim Teknolojileri Okumaları, TOJET.
• Ziatdinov, R., ve Musa, S. (2013). Rapid mental computation system as a tool for algorithmic thinking of elementary school students development. European researcher, Series A, (7), 1105-1110.