Skip to main content

Advertisement

SpringerLink
Log in

Modeling of Relationship of Personal and Affective Variables With Computational Thinking and Programming

  • Original research
  • Published:
Technology, Knowledge and Learning Aims and scope Submit manuscript

Abstract

Computational thinking skill is one of the basic skills required for every individual, such as reading and writing. For the development of CT, programming education is seen as the key. In the context of programming and CT relationship, it is very important to model individual characteristics and various affective variables with a holistic approach in the programming process. The purpose of this study is to determine and model the relationships of some individual characteristics, personal and affective variables for programming, with CT. One hundred and eighty-one middle school students participated in the implementation. As a result of the research, it was determined that there is a significant relationship between personal variables and attitude towards programming and interest in programming. On the other hand, it has been determined that there is no significant relationship between personal characteristics and self-efficacy for programming and the importance given to programming. In this model, the most influential predictor of attitude and interest towards programming was gender.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Akçay, T. (2009). Perceptions of students and teachers about the use of A kid’s programming language in computer courses. M.S. Thesis, middle east technical university, the graduate school of natural and applied sciences, Ankara.

  • Altun, A., & Mazman, S. G. (2012). Developing computer programming self-efficacy scale. Journal of Measurement and Evaluation in Education and Psychology, 3(2), 297–308.

    Google Scholar 

  • Angeli, C., & Valanides, N. (2020). Developing young children’s computational thinking with educational robotics: An interaction effect between gender and scaffolding strategy. Computers in Human Behavior, 105, 105954.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • Askar, P., & Davenport, D. (2009). An investigation of factors related to self-efficacy for Java programming among engineering students. TOJET: The Turkish Online Journal of Educational Technology, 8(1). Retrieved from http://files.eric.ed.gov/fulltext/ED503900.pdf.

  • Atman Uslu, N. & Mumcu, F. (2020). Bilişim Teknolojileri öğretmenlerinin programlama eğitimine ilişkin algıladıkları yeterlikleri ve mesleki gelişim beklentileri üzerine bir inceleme. H. F. Odabaşı, B. Akkoyunlu, A. İşman (Ed.), Eğitim Teknolojileri Okumaları. Pegem Akademi, Ankara.

  • Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191–2015.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Baser, M. (2013b). Attitude, gender and achievement in computer programming. Online Submission, 14(2), 248–255.

    Google Scholar 

  • Baser, N. (2013a). Asian Research Consortium. Asian Journal of Research in Banking and Finance, 3(10), 28–40.

    Google Scholar 

  • Bergin, S., & Reilly, R. (2005). Programming: factors that influence success. ACM SIGCSE Bulletin, 37(1), 411–415.

    Article  Google Scholar 

  • Boechler, P., Dragon, K., & Wasniewski, E. (2014). Digital literacy concepts and definitions: Implications for educational assessment and practice. International Journal of Digital Literacy and Digital Competence (IJDLDC), 5(4), 1–18.

    Article  Google Scholar 

  • Brennan, K., & Resnick, M. (2012). New frameworks for studying and assessing the development of computational thinking. In Proceedings of the 2012 annual meeting of the American Educational Research Association, Vancouver, Canada (pp. 1–25).

  • Bruckman, A., Jensen, C., & DeBonte, A. (2002). Gender and programming achievement in a CSCL environment. In Proceedings of the Conference on computer support for collaborative learning: Foundations for a CSCL Community (pp. 119–127).

  • Bubica, N., & Boljat, I. (2018). Assessment of computational thinking. Paper presented at the CTE2018: international conference on computational thinking education 201.

  • Buitrago Flórez, F., Casallas, R., Hernández, M., Reyes, A., Restrepo, S., & Danies, G. (2017). Changing a generation’s way of thinking: Teaching computational thinking through programming. Review of Educational Research, 87(4), 834–860.

    Article  Google Scholar 

  • Burke, Q., & Kafai, Y. B. (2010). Programming & storytelling: Opportunities for learning about coding & composition. In Proceedings of the 9th international conference on interaction design and children (pp. 348–351). ACM.

  • Büyüköztürk, Ş. (2009). Sosyal bilimler için veri analizi el kitabı: İstatistik, araştırma deseni, SPSS uygulamaları ve yorum. Ankara: Pegem Yayınları.

  • Byrne, P., & Lyons, G. (2001). The effect of student attributes on success in programming. ACM SIGCSE Bulletin, 33(3), 49–52.

    Article  Google Scholar 

  • Çelebi Uzgur, B., & Aykaç, N. (2016). The Evaluation of information technologies and software course’s curriculum according to the teacher’s ideas [In Turkish]. Mustafa Kemal University Journal of Social Sciences Institute, 13(34), 273–297.

    Google Scholar 

  • Chen, G., Shen, J., Barth-Cohen, L., Jiang, S., Huang, X., & Eltoukhy, M. (2017). Assessing elementary students’ computational thinking in everyday reasoning and robotics programming. Computers & Education, 109, 162–175.

    Article  Google Scholar 

  • Computer science teachers association (CSTA), (2010). Running on empty: The failure to teach K–12 computer science in the digital age. Retrieved from http://runningonempty.acm.org/fullreport2.pdf on 02.08.2016.

  • Csizmadia, A., Curzon, P., Dorling, M., Humphreys, S., Ng, T., Selby, C., & Woollard, J. (2015). Computational thinking-A guide for teachers. Retrieved from https://community.computingatschool.org.uk/resources/2324/single.

  • DeJarnette, N. (2012). America’s children: Providing early exposure to STEM (science, technology, engineering and math) initiatives. Education, 133(1), 77–84.

    Google Scholar 

  • Delcker, J., & Ifenthaler, D. (2017). Computational thinking as an interdisciplinary approach to computer science school curricula: A German perspective. In P. J. Rich & C. Hodges (Eds.), Emerging research, practice, and policy on computational thinking (pp. 49–62). Springer.

  • Denner, J., Werner, L., & Ortiz, E. (2012). Computer games created by middle school girls: Can they be used to measure understanding of computer science concepts? Computers & Education, 58(1), 240–249.

    Article  Google Scholar 

  • DiSessa, A. A. (2001). Changing minds: Computers, learning, and literacy. Mit Press.

  • Durak, H. (2016). Design and development of an instructional program for teaching programming process to gifted students. Unpublished Doctoral dissertation, Gazi University, Ankara.

  • Durak, H. Y. (2020). The effects of using different tools in programming teaching of secondary school students on engagement, computational thinking and reflective thinking skills for problem solving. Technology, Knowledge and Learning, 25(1), 179–195.

    Article  Google Scholar 

  • Durak, H., & Guyer, T. (2019). Programming with Scratch in primary school, indicators related to effectiveness of education process and analysis of these indicators in terms of various variables. Gifted Education International, 35(3), 237–258.

    Article  Google Scholar 

  • Erçetin, ŞŞ, & Durak, A. (2017). Processing, problems and solution suggestions of information technologies and software course in middle schools: Teacher opinions. Bartın Üniversitesi Eğitim Fakültesi Dergisi, 6(1), 159–176.

    Article  Google Scholar 

  • Erol, O., & Kurt, A. A. (2017). Investigation of CEIT students’ attitudes towards programming. Mehmet Akif Ersoy University Journal of Faculty of Education, 1(41), 314–325.

    Google Scholar 

  • Esteves, M., & Mendes, A. J. (2004). A simulation tool to help learning of object oriented programming basics. In Frontiers in Education, 2004. FIE 2004. 34th Annual (pp. F4C-7). IEEE.

  • Fesakis, G., & 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.

    Article  Google Scholar 

  • Fessakis, G., Gouli, E., & Mavroudi, E. (2013). Problem solvingby 5–6 years old kindergarten children in a computer programming environment: A case study. Computers & Education, 63, 87–97.

    Article  Google Scholar 

  • Fields, D. A., Searle, K. A., Kafai, Y. B., & Min, H. S. (2012). Debuggems to assess student learning in e-textiles. In Proceedings of the 43rd ACM technical symposium on Computer science education (pp. 699–699). ACM.

  • García-Peñalvo, F. J., & Mendes, A. J. (2017). Exploring the computational thinking effects in pre-university education. Computers in human behavior, 80, 407–411.

    Article  Google Scholar 

  • García-Peñalvo, F. J., Reimann, D., Tuul, M., Rees, A., & Jormanainen, I. (2016). An overview of the most relevant literature on coding and computational thinking with emphasis on the relevant issues for teachers. Retrieved from https://gredos.usal.es/jspui/bitstream/10366/131863/1/TACCLE3O5Literaturereview%20-%20final.pdf on 18.12.2017.

  • Grover, S., & Pea, R. (2013). Computational thinking in K–12: A review of the state of the field. Educational Researcher, 42(1), 38–43.

    Article  Google Scholar 

  • Hongwarittorrn, N., & Krairit, D. (2010). Effects of program visualization (jeliot3) on students' performance and attitudes towards java programming. In The spring 8th International conference on Computing, Communication and Control Technologies (pp. 6–9).

  • Jenkins, T. (2002). On the difficulty of learning to program. In Proceedings of the 3rd annual conference of the LTSN centre for information and computer sciences (Vol. 4, No. 2002, pp. 53–58).

  • Kafai, Y. B., & Burke, Q. (2013). Computer programming goes back to school. Phi Delta Kappan, 95(1), 61–65.

    Article  Google Scholar 

  • Kalelioglu, F., Gülbahar, Y., & Kukul, V. (2016). A framework for computational thinking based on a systematic research review. Baltic Journal of Modern Computing, 4(3), 583–596.

    Google Scholar 

  • Karasar, N. (2005). Scientific research method. Nobel Publication Distribution.

  • Katai, Z., & Toth, L. (2010). Technologically and artistically enhanced multi-sensory computer-programming education. Teaching and Teacher Education, 26(2), 244–251.

    Article  Google Scholar 

  • Kazakoff, E. R. (2015). Technology-based literacies for young children: Digital literacy through learning to code. Available at http://link.springer.com/chapter/https://doi.org/10.1007/978-94-017-9184-7_3#page-1.

  • Kazakoff, E. R., Sullivan, A., & Bers, M. U. (2013). The effect of a classroom-based intensive robotics and programming workshop on sequencing ability in early childhood. Early Childhood Education Journal, 41(4), 245–255.

    Article  Google Scholar 

  • Kazimoglu, C., Kiernan, M., Bacon, L., & MacKinnon, L. (2012). Learning programming at the computational thinking level via digital game-play. Procedia Computer Science, 9, 522–531.

    Article  Google Scholar 

  • Kelleher, C., & Pausch, R. (2006). Lessons learned from designing a programming system to support middle school girls creating animated stories. In Visual languages and human-centric computing (VL/HCC'06) (pp. 165–172). IEEE.

  • Kelleher, C., & Pausch, R. (2007). Using storytelling to motivate programming. Communications of the ACM, 50(7), 58–64.

    Article  Google Scholar 

  • Kelleher, C., Pausch, R., & Kiesler, S. (2007). Storytelling alice motivates middle school girls to learn computer programming. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 1455–1464).

  • Koh, K. H., Nickerson, H., Basawapatna, A., & Repenning, A. (2014). Early validation of computational thinking pattern analysis. In Proceedings of the 2014 conference on innovation & technology in computer science education (pp. 213–218). ACM.

  • Kong, S. C., Chiu, M. M., & Lai, M. (2018). A study of primary school students’ interest, collaboration attitude, and programming empowerment in computational thinking education. Computers & Education, 127, 178–189.

    Article  Google Scholar 

  • Kong, S.-C., Lai, M., & Sun, D. (2020). Teacher development in computational thinking: Design and learning outcomes of programming concepts, practices and pedagogy. Computers & Education, 151, 103872.

    Article  Google Scholar 

  • Korkmaz, Ö., Çakır, R., Özden, M. Y., Oluk, A., & Sarıoğlu, S. (2015a). Bireylerin Bilgisayarca Düşünme Becerilerinin Farklı Değişkenler Açısından İncelenmesi. Ondokuz Mayıs Üniversitesi Eğitim Fakültesi Dergisi, 34(2), 68–87.

    Google Scholar 

  • Korkmaz, Ö., Çakır, R., & Özden, M. Y. (2015b). Computational thinking levels scale (CTLS) adaptation for secondary school level. Gazi Journal of Education Sciences, 1(2), 143–162.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Lau, W. W., & Yuen, A. H. (2009). Exploring the effects of gender and learning styles on computer programming performance: Implications for programming pedagogy. British Journal of Educational Technology, 40(4), 696–712.

    Article  Google Scholar 

  • Lau, W. W., & Yuen, A. H. (2011). Modelling programming performance: Beyond the influence of learner characteristics. Computers & Education, 57(1), 1202–1213.

    Article  Google Scholar 

  • Lawanto, K., Close, K., Ames, C., & Brasiel, S. (2017). Exploring strengths and weaknesses in middle school students’ computational thinking in scratch. In P. Rich & C. Hodges (Eds.), Emerging research, practice, and policy on computational thinking. Educational communications and technology: Issues and innovations. Cham: Springer.

    Google Scholar 

  • Lee, I., Martin, F., & Apone, K. (2014). Integrating computational thinking across the K-8 curriculum. ACM Inroads, 5(4), 64–71.

    Article  Google Scholar 

  • Lee, I., Martin, F., Denner, J., Coulter, B., Allan, W., Erickson, J., Malyn-Smith, J., & Werner, L. (2011). Computational thinking for youth in practice. Acm Inroads, 2(1), 32–37.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Maloney, J. H., Peppler, K., Kafai, Y., Resnick, M., & Rusk, N. (2008). Programming by choice: Urban youth learning programming with scratch. ACM SIGCSE Bulletin, 40(1), 367–371.

    Article  Google Scholar 

  • Mims-Word, M. (2012). The importance of technology usage in the classroom, does gender gaps exist. Contemporary Issues in Education Research, 5(4), 271–278.

    Google Scholar 

  • Moreno, J. (2012). Digital competition game to improve programming skills. Journal of Educational Technology & Society, 15(3), 288–297.

    Google Scholar 

  • National research council (US). (2010). Report of a workshop on the scope and nature of computational thinking. National academies press.

  • Özyurt, Ö., & Özyurt, H. (2015). A study for determining computer programming students’ attitudes towards programming and their programming self-efficacy. Journal of Theory and Practice in Education, 11(1), 51–67.

    Google Scholar 

  • Palaigeorgiou, G. E., Siozos, P. D., Konstantakis, N. I., & Tsoukalas, I. A. (2005). A computer attitude scale for computer science freshmen and its educational implications. Journal of Computer Assisted Learning, 21(5), 330–342.

    Article  Google Scholar 

  • Pioro, B. T. (2004). Performance in an introductory computer programming course as a predictor of future success for engineering and computer science majors. In International Conference on Engineering Education, Gainesville, FL.

  • Resnick, M., Maloney, J., Monroy-Hernández, A., Rusk, N., Eastmond, E., Brennan, K., Millner, A., Rosenbaum, E., Silver, J., & Silverman, B. (2009). Scratch: Programming for all. Communications of the ACM, 52(11), 60–67.

    Article  Google Scholar 

  • Román-González, M., Pérez-González, J. C., & Jiménez-Fernández, C. (2017a). Which cognitive abilities underlie computational thinking? Criterion validity of the computational thinking test. Computers in Human Behavior, 72, 678–691.

    Article  Google Scholar 

  • Román-González, M., Pérez-González, J. C., Moreno-León, J., & Robles, G. (2017b). Extending the nomological network of computational thinking with non-cognitive factors. Computers in Human Behavior, 80, 441–459.

    Article  Google Scholar 

  • Rushkoff, D. (2010). Program or be programmed: Ten commands for a digital age. O/R Books.

  • Saritepeci, M. (2020). Developing computational thinking skills of high school students: Design-based learning activities and programming tasks. The Asia-Pacific Education Researcher, 29(1), 35–54. https://doi.org/10.1007/s40299-019-00480-2

    Article  Google Scholar 

  • Saritepeci, M., & Durak, H. (2017). Analyzing the effect of block and robotic coding activities on computational thinking in programming education. In I. Koleva & G. Duman (Eds.), Educational research and practice, Chapter 49 (pp. 490–501). St. Kliment Ohridski University Press.

  • Sarpong, K. A. M., Arthur, J. K., & Amoako, P. Y. O. (2013). Causes of failure of students in computer programming courses: The teacher-learner perspective. International Journal of Computer Applications, 77(12), 27–32.

    Article  Google Scholar 

  • Sax, L. J., Lehman, K. J., Jacobs, J. A., Kanny, M. A., Lim, G., Monje-Paulson, L., & Zimmerman, H. B. (2017). Anatomy of an enduring gender gap: The evolution of women’s participation in computer science. The Journal of Higher Education, 88(2), 258–293.

    Article  Google Scholar 

  • Shin, S., Park, P., & Bae, Y. (2013). The effects of an information-technology gifted program on friendship using scratch programming language and clutter. International Journal of Computer and Communication Engineering, 2(3), 246.

    Article  Google Scholar 

  • Shute, V. J., Sun, C., & Asbell-Clarke, J. (2017). Demystifying computational thinking. Educational Research Review, 22, 142–158.

    Article  Google Scholar 

  • Sonnleitner, P., Brunner, M., Keller, U., & Martin, R. (2014). Differential relations between facets of complex problem solving and students’ immigration background. Journal of Educational Psychology, 106(3), 681.

    Article  Google Scholar 

  • Tang, K.-Y., Chou, T.-L., & Tsai, C.-C. (2019). A content analysis of computational thinking research: An international publication trends and research typology. The Asia-Pacific Education Researcher, 29, 9–19.

    Article  Google Scholar 

  • Uslu, N. A. (2018). Görsel programlama etkinliklerinin ortaokul öğrencilerinin bilgi-işlemsel düşünme becerilerine etkisi. Ege Eğitim Teknolojileri Dergisi, 2(1), 19–31.

    Google Scholar 

  • Werner, L., Denner, J., Campe, S., & Kawamoto, D. C. (2012). The fairy performance assessment: measuring computational thinking in middle school. In Proceedings of the 43rd ACM technical symposium on computer science education (pp. 215–220). ACM.

  • Wing, J. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35.

    Article  Google Scholar 

  • Yağcı, M. (2016). Effect of attitudes of information technologies (IT) preservice teachers and computer programming (CP) students toward programming on their perception regarding their self-sufficiency for programming. Journal of Human Sciences, 13(1), 1418–1432.

    Article  Google Scholar 

  • Yildiz Durak, H., & Güyer, T. (2018). Design and development of an instructional program for teaching programming processes to gifted students using scratch. In Jessica Cannaday (Ed.), Curriculum development for gifted education programs (pp. 61–99). IGI Global.

  • 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.

    Article  Google Scholar 

  • Yildiz Durak, H., Saritepeci, M., Topçu, A., & Durak, A. (2020). Investigation of variables related to computational thinking self-efficacy level in middle school students: Are demographic variables, academic success, or programming-related variables more important? In Michail Kalogiannakis & Stamatios Papadakis (Eds.), Handbook of research on tools for teaching computational thinking in P-12 education (pp. 54–74). IGI Global.

  • Zhang, L., & Nouri, J. (2019). A systematic review of learning computational thinking through Scratch in K-9. Computers & Education, 141, 103607.

    Article  Google Scholar 

Download references

Funding

We have not received any funding or other support to present the views expressed in this paper.

Author information

Authors and Affiliations

  1. Department of Computer Technology and Information Systems, Faculty of Science, Bartin University, Bartın, Turkey

    Hatice Yildiz Durak

  2. Department of Computer Education and Instructional Technology, Necmettin Erbakan University, Konya, Turkey

    Mustafa Saritepeci

  3. Bartın University, Bartin, Turkey

    Aykut Durak

Authors
  1. Hatice Yildiz Durak
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mustafa Saritepeci
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Aykut Durak
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hatice Yildiz Durak.

Ethics declarations

Conflict of interests

The authors declare no conflicts of interest with respect to the authorship or the publication of this paper.

Ethical Statement

The research was conducted in a school in Turkey and approved by the school administration. Participation was voluntary and anonymous. Informed consent was obtained from all participants.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yildiz Durak, H., Saritepeci, M. & Durak, A. Modeling of Relationship of Personal and Affective Variables With Computational Thinking and Programming. Tech Know Learn 28, 165–184 (2023). https://doi.org/10.1007/s10758-021-09565-8

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10758-021-09565-8

Keywords

Search

Navigation