Research Article
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Investigating computational thinking skills based on different variables and determining the predictor variables

Year 2020, Volume: 7 Issue: 2, 102 - 114, 01.08.2020
https://doi.org/10.17275/per.20.22.7.2

Abstract

This study aimed to determine how secondary school students’ computational thinking skills changed according to gender, technology use i.e. mobile device ownership, technology competence, daily technology use periods, attitude towards science and attitude towards math. In addition, the relationships between these variables was determined in this study. The research, which was carried out with the participation of 722 secondary school students, was conducted with relational survey model. Convenience sampling method was used to determine the participants. Computational thinking scale, attitudes towards science scale and attitudes towards mathematics scale were used in the study as data collection tools. Descriptive statistics, independent samples t-test, single factor analysis of variance (ANOVA) and multiple regression analysis tests were used in this study. According to results, while computational thinking skills did not significantly differ according to gender; there was a significant difference in computational thinking skills according to mobile device ownership, technology competence, daily technology use periods, attitudes towards science and attitudes towards math. Three of the four models developed as a result of hierarchical regression analysis were found to be statistically significant. Accordingly, it can be argued that attitudes towards science, attitudes towards math and mobile device ownership are important predictors of computational thinking.

References

  • Aho, A. V. (2012). Computation and computational thinking. The Computer Journal, 55(7), 832–835.
  • Alsancak Sırakaya, D. (2019). Programlama öğretiminin bilgi işlemsel düşünme becerisine etkisi. Turkish Journal of Social Research/Turkiye Sosyal Arastirmalar Dergisi, 23(2), 575–590.
  • Angeli, C., & Valanides, N. (2019). Developing young children’s computational thinking with educational robotics: An interaction effect between gender and scaffolding strategy. Computers in Human Behavior.
  • 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.
  • 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? Inroads, 2(1), 48–54.
  • Basu, S., Kinnebrew, J. S., & Biswas, G. (2014). Assessing student performance in a computational-thinking based science learning environment. In International conference on intelligent tutoring systems (pp. 476–481).
  • Benakli, N., Kostadinov, B., Satyanarayana, A., & Singh, S. (2017). Introducing computational thinking through hands-on projects using R with applications to calculus, probability and data analysis. International Journal of Mathematical Education in Science and Technology, 48(3), 393–427.
  • Blikstein, P., & Wilensky, U. (2009). An atom is known by the company it keeps: A constructionist learning environment for materials science using agent-based modeling. International Journal of Computers for Mathematical Learning, 14(2), 81–119.
  • Büyüköztürk, Ş. (2007). Sosyal Bilimler İçin Veri Analizi El Kitabı. Ankara: PegemA Yayıncılık.
  • Büyüköztürk, Ş., Kılıç Çakmak, E., Akgün, Ö., E., Karadeniz, Ş., & Demirel, F. (2008). Bilimsel Araştırma Yöntemleri. Ankara: Pegem Akademi.
  • Denning, P. J. (2009). The profession of IT Beyond computational thinking. Communications of the ACM, 52(6), 28–30.
  • Denning, P. J. (2017). Remaining trouble spots with computational thinking. Communications of the ACM, 60(6), 33–39.
  • Farris, A. V., & Sengupta, P. (2016). Democratizing children’s computation: Learning computational science as aesthetic experience. Educational Theory, 66(1–2), 279–296.
  • Garcia-Peñalvo, F. J., & Mendes, A. J. (2018). Exploring the computational thinking effects in pre-university education. Elsevier.
  • 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., & Cooper, S. (2015). Designing for deeper learning in a blended computer science course for middle school students. Computer Science Education, 25(2), 199–237.
  • Hsu, T.-C., Chang, S.-C., & Hung, Y.-T. (2018). How to learn and how to teach computational thinking: Suggestions based on a review of the literature. Computers & Education, 126, 296–310.
  • 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.
  • Karasar, N. (2012). Bilimsel araştırma yöntemi (Scientific research methods). Ankara: Nobel Pub.
  • Kazimoglu, C., Kiernan, M., Bacon, L., & MacKinnon, L. (2011). Understanding computational thinking before programming: developing guidelines for the design of games to learn introductory programming through game-play. International Journal of Game-Based Learning (IJGBL), 1(3), 30–52.
  • 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.
  • 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.
  • Korkmaz, Ö., Çakır, R., & Özden, M. Y. (2015). Computational thinking levels scale (ctls) adaptation for secondary school level. Gazi Journal of Educational Science, 1(2).
  • Korkmaz, Ö., Çakır, R., Özden, M. Y., Oluk, A., & Sarıoğlu, S. (2015). Bireylerin bilgisayarca düşünme becerilerinin farklı değişkenler açısından incelenmesi. Ondokuz Mayıs Üniversitesi Eğitim Fakültesi Dergisi, 34(2), 68–87.
  • Korucu, A., Gencturk, A., & Gundogdu, M. (2017). Examination of the computational thinking skills of students. Journal of Learning and Teaching in Digital Age, 2(1), 11–19.
  • Libeskind-Hadas, R., & Bush, E. (2013). A first course in computing with applications to biology. Briefings in Bioinformatics, 14(5), 610–617.
  • Oluk, A., & Korkmaz, Ö. (2016). Comparing students’ scratch skills with their computational thinking skills in terms of different variables. Online Submission, 8(11), 1–7.
  • Özcan, H., & Koca, E. (2019). STEM’e yönelik tutum ölçeğinin Türkçeye uyarlanması: Geçerlik ve güvenirlik araştırması. Hacettepe Üniversitesi Eğitim Fakültesi Dergisi, 34(2), 387--401.
  • Pérez-Marín, D., Hijón-Neira, R., Bacelo, A., & Pizarro, C. (2018). Can computational thinking be improved by using a methodology based on metaphors and scratch to teach computer programming to children? Computers in Human Behavior.
  • Repenning, A. (2012). Programming goes back to school. Communications of the ACM, 55(5), 38–40.
  • Rich, K., & Yadav, A. (2019). Infusing computational thinking instruction into elementary mathematics and science: patterns of teacher implementation. In Society for Information Technology & Teacher Education International Conference (pp. 330–334).
  • Roman-Gonzalez, M., Perez-Gonzalez, J.-C., & Jimez-Fernandez, C. (2017). Which cognitive abilities underlie computational thinking? Criterion validity of the Computational Thinking Test. Computers in Human Behavior, 72, 678–691.
  • Román-González, M., Pérez-González, J.-C., Moreno-León, J., & Robles, G. (2018). Can computational talent be detected? Predictive validity of the Computational Thinking Test. International Journal of Child-Computer Interaction, 18, 47–58.
  • Selby, C., & Woollard, J. (2013). Computational thinking: the developing definition. In Proceedings of the 45th ACM Technical Symposium on Computer Science Education. Canterbury: ACM: University of Southampton (E-prints).
  • Sengupta, P., Kinnebrew, J. S., Basu, S., Biswas, G., & Clark, D. (2013). Integrating computational thinking with K-12 science education using agent-based computation: A theoretical framework. Education and Information Technologies, 18(2), 351–380.
  • Shute, V. J., Sun, C., & Asbell-Clarke, J. (2017). Demystifying computational thinking. Educational Research Review, 22, 142–158.
  • Snodgrass, M. R., Israel, M., & Reese, G. C. (2016). Instructional supports for students with disabilities in K-5 computing: Findings from a cross-case analysis. Computers & Education, 100, 1–17.
  • Swanson, H., Anton, G., Bain, C., Horn, M., & Wilensky, U. (2017). Computational thinking in the science classroom. In International Conference on Computational Thinking Education 2017.
  • Weintrop, D., Beheshti, E., Horn, M., Orton, K., Jona, K., Trouille, L., & Wilensky, U. (2016). Defining computational thinking for mathematics and science classrooms. Journal of Science Education and Technology, 25(1), 127–147.
  • Wing. (2014). Computational thinking benefits society. 40th Anniversary Blog of Social Issues in Computing, 2014.
  • Wing, J. (2008). Computational thinking and thinking about computing. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 366(1881), 3717–3725.
  • Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35.
  • Yağcı, M. (2018). Lise öğrencilerinin bilgi-işlemsel düşünme beceri düzeylerinin incelenmesi. International Online Journal of Educational Sciences, 10(2).
  • 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.
  • Yilmaz, Ç., Altun, S. A., & Olkun, S. (2010). Factors affecting students’ attidude towards Math: ABC theory and its reflection on practice. Procedia-Social and Behavioral Sciences, 2(2), 4502–4506.
Year 2020, Volume: 7 Issue: 2, 102 - 114, 01.08.2020
https://doi.org/10.17275/per.20.22.7.2

Abstract

References

  • Aho, A. V. (2012). Computation and computational thinking. The Computer Journal, 55(7), 832–835.
  • Alsancak Sırakaya, D. (2019). Programlama öğretiminin bilgi işlemsel düşünme becerisine etkisi. Turkish Journal of Social Research/Turkiye Sosyal Arastirmalar Dergisi, 23(2), 575–590.
  • Angeli, C., & Valanides, N. (2019). Developing young children’s computational thinking with educational robotics: An interaction effect between gender and scaffolding strategy. Computers in Human Behavior.
  • 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.
  • 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? Inroads, 2(1), 48–54.
  • Basu, S., Kinnebrew, J. S., & Biswas, G. (2014). Assessing student performance in a computational-thinking based science learning environment. In International conference on intelligent tutoring systems (pp. 476–481).
  • Benakli, N., Kostadinov, B., Satyanarayana, A., & Singh, S. (2017). Introducing computational thinking through hands-on projects using R with applications to calculus, probability and data analysis. International Journal of Mathematical Education in Science and Technology, 48(3), 393–427.
  • Blikstein, P., & Wilensky, U. (2009). An atom is known by the company it keeps: A constructionist learning environment for materials science using agent-based modeling. International Journal of Computers for Mathematical Learning, 14(2), 81–119.
  • Büyüköztürk, Ş. (2007). Sosyal Bilimler İçin Veri Analizi El Kitabı. Ankara: PegemA Yayıncılık.
  • Büyüköztürk, Ş., Kılıç Çakmak, E., Akgün, Ö., E., Karadeniz, Ş., & Demirel, F. (2008). Bilimsel Araştırma Yöntemleri. Ankara: Pegem Akademi.
  • Denning, P. J. (2009). The profession of IT Beyond computational thinking. Communications of the ACM, 52(6), 28–30.
  • Denning, P. J. (2017). Remaining trouble spots with computational thinking. Communications of the ACM, 60(6), 33–39.
  • Farris, A. V., & Sengupta, P. (2016). Democratizing children’s computation: Learning computational science as aesthetic experience. Educational Theory, 66(1–2), 279–296.
  • Garcia-Peñalvo, F. J., & Mendes, A. J. (2018). Exploring the computational thinking effects in pre-university education. Elsevier.
  • 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., & Cooper, S. (2015). Designing for deeper learning in a blended computer science course for middle school students. Computer Science Education, 25(2), 199–237.
  • Hsu, T.-C., Chang, S.-C., & Hung, Y.-T. (2018). How to learn and how to teach computational thinking: Suggestions based on a review of the literature. Computers & Education, 126, 296–310.
  • 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.
  • Karasar, N. (2012). Bilimsel araştırma yöntemi (Scientific research methods). Ankara: Nobel Pub.
  • Kazimoglu, C., Kiernan, M., Bacon, L., & MacKinnon, L. (2011). Understanding computational thinking before programming: developing guidelines for the design of games to learn introductory programming through game-play. International Journal of Game-Based Learning (IJGBL), 1(3), 30–52.
  • 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.
  • 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.
  • Korkmaz, Ö., Çakır, R., & Özden, M. Y. (2015). Computational thinking levels scale (ctls) adaptation for secondary school level. Gazi Journal of Educational Science, 1(2).
  • Korkmaz, Ö., Çakır, R., Özden, M. Y., Oluk, A., & Sarıoğlu, S. (2015). Bireylerin bilgisayarca düşünme becerilerinin farklı değişkenler açısından incelenmesi. Ondokuz Mayıs Üniversitesi Eğitim Fakültesi Dergisi, 34(2), 68–87.
  • Korucu, A., Gencturk, A., & Gundogdu, M. (2017). Examination of the computational thinking skills of students. Journal of Learning and Teaching in Digital Age, 2(1), 11–19.
  • Libeskind-Hadas, R., & Bush, E. (2013). A first course in computing with applications to biology. Briefings in Bioinformatics, 14(5), 610–617.
  • Oluk, A., & Korkmaz, Ö. (2016). Comparing students’ scratch skills with their computational thinking skills in terms of different variables. Online Submission, 8(11), 1–7.
  • Özcan, H., & Koca, E. (2019). STEM’e yönelik tutum ölçeğinin Türkçeye uyarlanması: Geçerlik ve güvenirlik araştırması. Hacettepe Üniversitesi Eğitim Fakültesi Dergisi, 34(2), 387--401.
  • Pérez-Marín, D., Hijón-Neira, R., Bacelo, A., & Pizarro, C. (2018). Can computational thinking be improved by using a methodology based on metaphors and scratch to teach computer programming to children? Computers in Human Behavior.
  • Repenning, A. (2012). Programming goes back to school. Communications of the ACM, 55(5), 38–40.
  • Rich, K., & Yadav, A. (2019). Infusing computational thinking instruction into elementary mathematics and science: patterns of teacher implementation. In Society for Information Technology & Teacher Education International Conference (pp. 330–334).
  • Roman-Gonzalez, M., Perez-Gonzalez, J.-C., & Jimez-Fernandez, C. (2017). Which cognitive abilities underlie computational thinking? Criterion validity of the Computational Thinking Test. Computers in Human Behavior, 72, 678–691.
  • Román-González, M., Pérez-González, J.-C., Moreno-León, J., & Robles, G. (2018). Can computational talent be detected? Predictive validity of the Computational Thinking Test. International Journal of Child-Computer Interaction, 18, 47–58.
  • Selby, C., & Woollard, J. (2013). Computational thinking: the developing definition. In Proceedings of the 45th ACM Technical Symposium on Computer Science Education. Canterbury: ACM: University of Southampton (E-prints).
  • Sengupta, P., Kinnebrew, J. S., Basu, S., Biswas, G., & Clark, D. (2013). Integrating computational thinking with K-12 science education using agent-based computation: A theoretical framework. Education and Information Technologies, 18(2), 351–380.
  • Shute, V. J., Sun, C., & Asbell-Clarke, J. (2017). Demystifying computational thinking. Educational Research Review, 22, 142–158.
  • Snodgrass, M. R., Israel, M., & Reese, G. C. (2016). Instructional supports for students with disabilities in K-5 computing: Findings from a cross-case analysis. Computers & Education, 100, 1–17.
  • Swanson, H., Anton, G., Bain, C., Horn, M., & Wilensky, U. (2017). Computational thinking in the science classroom. In International Conference on Computational Thinking Education 2017.
  • Weintrop, D., Beheshti, E., Horn, M., Orton, K., Jona, K., Trouille, L., & Wilensky, U. (2016). Defining computational thinking for mathematics and science classrooms. Journal of Science Education and Technology, 25(1), 127–147.
  • Wing. (2014). Computational thinking benefits society. 40th Anniversary Blog of Social Issues in Computing, 2014.
  • Wing, J. (2008). Computational thinking and thinking about computing. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 366(1881), 3717–3725.
  • Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35.
  • Yağcı, M. (2018). Lise öğrencilerinin bilgi-işlemsel düşünme beceri düzeylerinin incelenmesi. International Online Journal of Educational Sciences, 10(2).
  • 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.
  • Yilmaz, Ç., Altun, S. A., & Olkun, S. (2010). Factors affecting students’ attidude towards Math: ABC theory and its reflection on practice. Procedia-Social and Behavioral Sciences, 2(2), 4502–4506.
There are 45 citations in total.

Details

Primary Language English
Subjects Other Fields of Education
Journal Section Research Articles
Authors

Didem Alsancak 0000-0002-1821-5275

Publication Date August 1, 2020
Acceptance Date April 25, 2020
Published in Issue Year 2020 Volume: 7 Issue: 2

Cite

APA Alsancak, D. (2020). Investigating computational thinking skills based on different variables and determining the predictor variables. Participatory Educational Research, 7(2), 102-114. https://doi.org/10.17275/per.20.22.7.2

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