Computational thinking (CT) skills are accepted as fundamental literacy. Although the idea that K-12 teachers should teach students CT skills in an interdisciplinary context is heavily expressed, there is a need for a measurement tool in Turkish that measures teachers' self-efficacy in this regard. This study aims to adapt the T-STEM CT scale, developed by Boulden et al. (2021), into Turkish and to carry out validity and reliability studies of this scale. The original scale consists of a 5-point Likert scale and 13 items. The participants of this study consisted of 168 teachers from different branches working in K-12 schools. It was carried out by selecting for application purposes and a convenient sampling method. Various validity and reliability methods were used to validate the scale. According to the results, the two-factor (Factor1: T-STEM CT self-efficacy, Factor2: T-STEM CT outcome expectancy) and thirteen-item structure had an acceptable fit with the data. Consequently, the validity and reliability of a Turkish tool measuring teaching efficacy beliefs for computational thinking skills were confirmed.
Aho, A. V. (2012). Computation and computational thinking. The Computer Journal, 55(7), 832-835. https://doi.org/10.1093/comjnl/bxs074
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
Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74-94.
Barr, D., Harrison, J., & Conery, L. (2011). Computational thinking: A digital age skill for everyone. Learning & Leading with Technology, 38(6), 20-23.
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. https://doi.org/10.1145/1929887.1929905
Basogain, X., Olabe, M., Olabe, J., Maiz, I., & Castaño, C. (2012). Mathematics Education through Programming Languages. 21st Annual World Congress on Learning Disabilities, In 21st annual world congress on learning disabilities (pp. 553-559).
Bell, J., & Bell, T. (2018). Integrating computational thinking with a music education context. Informatics in Education, 17(2), 151-166. https://www.doi.org/10.15388/infedu.2018.09
Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological bulletin, 88(3), 588. https://doi.org/10.1037/0033-2909.88.3.588
Boulden, D. C., Rachmatullah, A., Oliver, K. M., & Wiebe, E. (2021). Measuring in-service teacher self-efficacy for teaching computational thinking: development and validation of the T-STEM CT. Education and Information technologies, 26(4), 4663-4689. https://doi.org/10.1007/s10639-021-10487-2
Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (Vol. 154, pp. 136). SAGE Publications. https://doi.org/10.1177/0049124192021002005
Bryman, A., & Cramer, D. (2002). Quantitative data analysis with SPSS release 10 for Windows: A guide for social scientists. Routledge. https://doi.org/10.4324/9780203471548
Bundy, A. (2007). Computational thinking is pervasive. Journal of Scientific and Practical Computing, 1(2), 67-69.
Cattell, R. (2012). The scientific use of factor analysis in behavioral and life sciences. Springer Science & Business Media. https://doi.org/10.1007/978-1-4684-2262-7
Çokluk, Ö., Şekercioğlu, G., & Büyüköztürk, Ş. (2014). Sosyal bilimler için çok değişkenli istatistik: SPSS ve LISREL uygulamaları [Multivariate statistics for social sciences: SPSS and LISREL applications]. Pegem Academy.
Durak, H. Y., & 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
Gadanidis, G. (2017). Artificial intelligence, computational thinking, and mathematics education. The International Journal of Information and Learning Technology. https://doi.org/10.1108/IJILT-09-2016-0048
Gefen, D., Straub, D., & Boudreau, M.-C. (2000). Structural equation modeling and regression: Guidelines for research practice. Communications of the association for information systems, 4(1), 7. https://doi.org/10.17705/1CAIS.00407
Gorsuch, R. L. (1983). Factor analysis. Lawrence Erlbaum.
Grover, S., & Pea, R. (2013). Computational thinking in K–12: A review of the state of the field. Educational researcher, 42(1), 38-43. https://doi.org/10.3102/0013189X12463051
Guo, J., Marsh, H. W., Parker, P. D., Morin, A. J., & Yeung, A. S. (2015). Expectancy-value in mathematics, gender and socioeconomic background as predictors of achievement and aspirations: A multi-cohort study. Learning and Individual Differences, 37, 161-168. https://doi.org/10.1016/j.lindif.2015.01.008b
Hair, J. F. (2009). Multivariate data analysis: A global perspective. Prentice Hall
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. https://doi.org/10.1016/j.compedu.2018.07.004
ISTE. (2016a). ISTE Standards for Educators. Retrieved from https://www.iste.org/standards/for-educators
ISTE. (2016b). ISTE Standards for Students. Retrieved from https://www.iste.org/standards/iste-standards-for-students
Kline, R. B. (2016). Principles and practice of structural equation modeling (4 ed.). Guilford publications.
Lai, Y.-H., Chen, S.-Y., Lai, C.-F., Chang, Y.-C., & Su, Y.-S. (2021). Study on enhancing AIoT computational thinking skills by plot image-based VR. Interactive Learning Environments, 29(3), 482-495. https://doi.org/10.1080/10494820.2019.1580750
Lee, I., Martin, F., & Apone, K. (2014). Integrating computational thinking across the K--8 curriculum. Acm Inroads, 5(4), 64-71. https://doi.org/10.1145/2684721.2684736
Li, Y., Schoenfeld, A. H., diSessa, A. A., Graesser, A. C., Benson, L. C., English, L. D., & Duschl, R. A. (2020). On computational thinking and STEM education. Journal for STEM Education Research, 3(2), 147-166. https://doi.org/10.1007/s41979-020-00044-w
Lindberg, R. S., Laine, T. H., & Haaranen, L. (2019). Gamifying programming education in K‐12: A review of programming curricula in seven countries and programming games. British Journal of Educational Technology, 50(4), 1979-1995. https://doi.org/10.1111/bjet.12685
Mohaghegh, D. M., & McCauley, M. (2016). Computational thinking: The skill set of the 21st century. International Journal of Computer Science and Information Technologies, 7(3), 1524-1530.
Özgün, Z., & Saritepeci, M. (2021). Determination of the factors affecting teachers’ perceptions of classroom management competence in technology assisted courses. Technology, Pedagogy and Education, 30(5), 673-691. https://doi.org/10.1080/1475939X.2021.1956579
Papadakis, S. (2022). Apps to promote computational thinking concepts and coding skills in children of preschool and pre-primary school age. In Research Anthology on Computational Thinking, Programming, and Robotics in the Classroom (pp. 610-630). IGI Global. https://www.doi.org/10.4018/978-1-6684-2411-7.ch028
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.
R Core Team (2020). R: A Language and environment for statistical computing. (Version 4.0) [Computer software]. Retrieved from https://cran.r-project.org. (R packages retrieved from MRAN snapshot 2020-08-24).
Rosseel, Y., et al. (2018). lavaan: Latent Variable Analysis. [R package]. Retrieved from https://cran.r-project.org/package=lavaan
Rubinstein, A., & Chor, B. (2014). Computational thinking in life science education. PLoS computational biology, 10(11), e1003897. https://doi.org/10.1371/journal.pcbi.1003897
Sanford, J. F., & Naidu, J. T. (2016). Computational thinking concepts for grade school. Contemporary Issues in Education Research (CIER), 9(1), 23-32. https://doi.org/10.19030/cier.v9i1.9547
Saritepeci, M. (2020). Developing computational thinking skills of high school students: design-based learning activities and programming tasks. Asia-Pacific Education Researcher, 29(1), 35-54. https://doi.org/10.1007/s40299-019-00480-2
Saritepeci, M. (2021). Modelling the effect of TPACK and computational thinking on classroom management in technology enriched courses. Technology, Knowledge and Learning, 1-15. https://doi.org/10.1007/s10758-021-09529-y
Sarıtepeci, M., & Durak, H. (2017). Analyzing the effect of block and robotic coding activities on computational thinking in programming education. In G. D. Irina Koleva (Ed.), Educational research and practice. St. Kliment Ohridski University Press.
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
Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (6 ed.). Pearson.
The jamovi project (2021). Jamovi. (Version 1.6) [Computer Software]. Retrieved from https://www.jamovi.org.
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. https://www.doi.org/10.1007/s10956-015-9581-5
Wigfield, A., & Eccles, J. S. (2000). Expectancy–value theory of achievement motivation. Contemporary Educational Psychology, 25(1), 68-81. https://doi.org/10.1006/ceps.1999.1015
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 of London A: mathematical, physical and engineering sciences, 366(1881), 3717-3725. https://doi.org/doi/10.1098/rsta.2008.0118
Wing, J. M. (2014). Computational thinking benefits society. 40th Anniversary Blog of Social Issues in Computing, 2014, 26.
Wolz, U., Stone, M., Pearson, K., Pulimood, S. M., & Switzer, M. (2011). Computational thinking and expository writing in the middle school. ACM Transactions on Computing Education (TOCE), 11(2), 1-22. https://doi.org/10.1145/1993069.1993073
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, 14(1), 5. https://doi.org/10.1145/2576872
Yıldız-Durak, H., Sarıtepeci, M., & Aksu-Dünya, B. (2021). Examining the Relationship between Computational Thinking, Lifelong Learning Competencies and Personality Traits Using Path Analysis. Bartın University Journal of Faculty of Education, 10(2), 284-294. https://doi.org/10.14686/buefad.888374
Yildiz Durak, H. (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. https://doi.org/10.1007/s10758-018-9391-y
Yildiz Durak, H. (2021). Modeling of relations between K-12 teachers’ TPACK levels and their technology integration self-efficacy, technology literacy levels, attitudes toward technology and usage objectives of social networks. Interactive Learning Environments, 29(7), 1136-1162. https://doi.org/10.1080/10494820.2019.1619591
Yildiz Durak, H., Saritepeci, M., & Durak, A. (2021). Modeling of Relationship of Personal and Affective Variables with Computational Thinking and Programming. Technology, Knowledge and Learning, 1-20. https://doi.org/10.1007/s10758-021-09565-8
Zhao, L., Liu, X., Wang, C., & Su, Y. S. (2022). Effect of different mind mapping approaches on primary school students’ computational thinking skills during visual programming learning. Computers & Education, 104445. https://doi.org/10.1016/j.compedu.2022.104445
Year 2022,
Volume: 6 Issue: Special Issue, 47 - 56, 30.04.2022
Aho, A. V. (2012). Computation and computational thinking. The Computer Journal, 55(7), 832-835. https://doi.org/10.1093/comjnl/bxs074
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
Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74-94.
Barr, D., Harrison, J., & Conery, L. (2011). Computational thinking: A digital age skill for everyone. Learning & Leading with Technology, 38(6), 20-23.
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. https://doi.org/10.1145/1929887.1929905
Basogain, X., Olabe, M., Olabe, J., Maiz, I., & Castaño, C. (2012). Mathematics Education through Programming Languages. 21st Annual World Congress on Learning Disabilities, In 21st annual world congress on learning disabilities (pp. 553-559).
Bell, J., & Bell, T. (2018). Integrating computational thinking with a music education context. Informatics in Education, 17(2), 151-166. https://www.doi.org/10.15388/infedu.2018.09
Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological bulletin, 88(3), 588. https://doi.org/10.1037/0033-2909.88.3.588
Boulden, D. C., Rachmatullah, A., Oliver, K. M., & Wiebe, E. (2021). Measuring in-service teacher self-efficacy for teaching computational thinking: development and validation of the T-STEM CT. Education and Information technologies, 26(4), 4663-4689. https://doi.org/10.1007/s10639-021-10487-2
Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (Vol. 154, pp. 136). SAGE Publications. https://doi.org/10.1177/0049124192021002005
Bryman, A., & Cramer, D. (2002). Quantitative data analysis with SPSS release 10 for Windows: A guide for social scientists. Routledge. https://doi.org/10.4324/9780203471548
Bundy, A. (2007). Computational thinking is pervasive. Journal of Scientific and Practical Computing, 1(2), 67-69.
Cattell, R. (2012). The scientific use of factor analysis in behavioral and life sciences. Springer Science & Business Media. https://doi.org/10.1007/978-1-4684-2262-7
Çokluk, Ö., Şekercioğlu, G., & Büyüköztürk, Ş. (2014). Sosyal bilimler için çok değişkenli istatistik: SPSS ve LISREL uygulamaları [Multivariate statistics for social sciences: SPSS and LISREL applications]. Pegem Academy.
Durak, H. Y., & 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
Gadanidis, G. (2017). Artificial intelligence, computational thinking, and mathematics education. The International Journal of Information and Learning Technology. https://doi.org/10.1108/IJILT-09-2016-0048
Gefen, D., Straub, D., & Boudreau, M.-C. (2000). Structural equation modeling and regression: Guidelines for research practice. Communications of the association for information systems, 4(1), 7. https://doi.org/10.17705/1CAIS.00407
Gorsuch, R. L. (1983). Factor analysis. Lawrence Erlbaum.
Grover, S., & Pea, R. (2013). Computational thinking in K–12: A review of the state of the field. Educational researcher, 42(1), 38-43. https://doi.org/10.3102/0013189X12463051
Guo, J., Marsh, H. W., Parker, P. D., Morin, A. J., & Yeung, A. S. (2015). Expectancy-value in mathematics, gender and socioeconomic background as predictors of achievement and aspirations: A multi-cohort study. Learning and Individual Differences, 37, 161-168. https://doi.org/10.1016/j.lindif.2015.01.008b
Hair, J. F. (2009). Multivariate data analysis: A global perspective. Prentice Hall
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. https://doi.org/10.1016/j.compedu.2018.07.004
ISTE. (2016a). ISTE Standards for Educators. Retrieved from https://www.iste.org/standards/for-educators
ISTE. (2016b). ISTE Standards for Students. Retrieved from https://www.iste.org/standards/iste-standards-for-students
Kline, R. B. (2016). Principles and practice of structural equation modeling (4 ed.). Guilford publications.
Lai, Y.-H., Chen, S.-Y., Lai, C.-F., Chang, Y.-C., & Su, Y.-S. (2021). Study on enhancing AIoT computational thinking skills by plot image-based VR. Interactive Learning Environments, 29(3), 482-495. https://doi.org/10.1080/10494820.2019.1580750
Lee, I., Martin, F., & Apone, K. (2014). Integrating computational thinking across the K--8 curriculum. Acm Inroads, 5(4), 64-71. https://doi.org/10.1145/2684721.2684736
Li, Y., Schoenfeld, A. H., diSessa, A. A., Graesser, A. C., Benson, L. C., English, L. D., & Duschl, R. A. (2020). On computational thinking and STEM education. Journal for STEM Education Research, 3(2), 147-166. https://doi.org/10.1007/s41979-020-00044-w
Lindberg, R. S., Laine, T. H., & Haaranen, L. (2019). Gamifying programming education in K‐12: A review of programming curricula in seven countries and programming games. British Journal of Educational Technology, 50(4), 1979-1995. https://doi.org/10.1111/bjet.12685
Mohaghegh, D. M., & McCauley, M. (2016). Computational thinking: The skill set of the 21st century. International Journal of Computer Science and Information Technologies, 7(3), 1524-1530.
Özgün, Z., & Saritepeci, M. (2021). Determination of the factors affecting teachers’ perceptions of classroom management competence in technology assisted courses. Technology, Pedagogy and Education, 30(5), 673-691. https://doi.org/10.1080/1475939X.2021.1956579
Papadakis, S. (2022). Apps to promote computational thinking concepts and coding skills in children of preschool and pre-primary school age. In Research Anthology on Computational Thinking, Programming, and Robotics in the Classroom (pp. 610-630). IGI Global. https://www.doi.org/10.4018/978-1-6684-2411-7.ch028
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.
R Core Team (2020). R: A Language and environment for statistical computing. (Version 4.0) [Computer software]. Retrieved from https://cran.r-project.org. (R packages retrieved from MRAN snapshot 2020-08-24).
Rosseel, Y., et al. (2018). lavaan: Latent Variable Analysis. [R package]. Retrieved from https://cran.r-project.org/package=lavaan
Rubinstein, A., & Chor, B. (2014). Computational thinking in life science education. PLoS computational biology, 10(11), e1003897. https://doi.org/10.1371/journal.pcbi.1003897
Sanford, J. F., & Naidu, J. T. (2016). Computational thinking concepts for grade school. Contemporary Issues in Education Research (CIER), 9(1), 23-32. https://doi.org/10.19030/cier.v9i1.9547
Saritepeci, M. (2020). Developing computational thinking skills of high school students: design-based learning activities and programming tasks. Asia-Pacific Education Researcher, 29(1), 35-54. https://doi.org/10.1007/s40299-019-00480-2
Saritepeci, M. (2021). Modelling the effect of TPACK and computational thinking on classroom management in technology enriched courses. Technology, Knowledge and Learning, 1-15. https://doi.org/10.1007/s10758-021-09529-y
Sarıtepeci, M., & Durak, H. (2017). Analyzing the effect of block and robotic coding activities on computational thinking in programming education. In G. D. Irina Koleva (Ed.), Educational research and practice. St. Kliment Ohridski University Press.
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
Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (6 ed.). Pearson.
The jamovi project (2021). Jamovi. (Version 1.6) [Computer Software]. Retrieved from https://www.jamovi.org.
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. https://www.doi.org/10.1007/s10956-015-9581-5
Wigfield, A., & Eccles, J. S. (2000). Expectancy–value theory of achievement motivation. Contemporary Educational Psychology, 25(1), 68-81. https://doi.org/10.1006/ceps.1999.1015
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 of London A: mathematical, physical and engineering sciences, 366(1881), 3717-3725. https://doi.org/doi/10.1098/rsta.2008.0118
Wing, J. M. (2014). Computational thinking benefits society. 40th Anniversary Blog of Social Issues in Computing, 2014, 26.
Wolz, U., Stone, M., Pearson, K., Pulimood, S. M., & Switzer, M. (2011). Computational thinking and expository writing in the middle school. ACM Transactions on Computing Education (TOCE), 11(2), 1-22. https://doi.org/10.1145/1993069.1993073
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, 14(1), 5. https://doi.org/10.1145/2576872
Yıldız-Durak, H., Sarıtepeci, M., & Aksu-Dünya, B. (2021). Examining the Relationship between Computational Thinking, Lifelong Learning Competencies and Personality Traits Using Path Analysis. Bartın University Journal of Faculty of Education, 10(2), 284-294. https://doi.org/10.14686/buefad.888374
Yildiz Durak, H. (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. https://doi.org/10.1007/s10758-018-9391-y
Yildiz Durak, H. (2021). Modeling of relations between K-12 teachers’ TPACK levels and their technology integration self-efficacy, technology literacy levels, attitudes toward technology and usage objectives of social networks. Interactive Learning Environments, 29(7), 1136-1162. https://doi.org/10.1080/10494820.2019.1619591
Yildiz Durak, H., Saritepeci, M., & Durak, A. (2021). Modeling of Relationship of Personal and Affective Variables with Computational Thinking and Programming. Technology, Knowledge and Learning, 1-20. https://doi.org/10.1007/s10758-021-09565-8
Zhao, L., Liu, X., Wang, C., & Su, Y. S. (2022). Effect of different mind mapping approaches on primary school students’ computational thinking skills during visual programming learning. Computers & Education, 104445. https://doi.org/10.1016/j.compedu.2022.104445
Sarıtepeci, M., & Durak, A. (2022). Adaptation of T-STEM CT Scale to Turkish: Teacher Self-Efficacy and Outcome Expectancy for Teaching Computational Thinking. Research on Education and Psychology, 6(Special Issue), 47-56. https://doi.org/10.54535/rep.1080132