Computational thinking scale: the predictive role of metacognition in the context of higher order thinking skills

Year 2023, Volume: 11 Issue: 3 - September 2023, 423 - 437, 02.10.2023

Özlem ÜZÜMCÜ

https://doi.org/10.17478/jegys.1355722

Abstract

This study aims to determine the predictive role of cognition in computational thinking. In this context, the research has two problem situations. The first one is the development of a computational thinking scale for prospective teachers. The second is to determine the predictive role of metacognition in computational thinking with this scale. In Study-1, the computational thinking scale was developed with (N= 365) participants. In Study-2 (N=306), the role of metacognition in computational thinking was explained with structural equation modeling. These findings show that, the computational thinking scale consisting of 28 items in Study-1 explained 48% of the total variance with a single factor structure and the internal consistency coefficient was found to be .985. In Study-2, the role of metacognition in computational thinking was tested with structural equation modeling. Accordingly, the planning, debugging and procedural knowledge sub-dimensions of metacognition explained 47% of the variance of computational thinking.

Keywords

computational thinking, metacognition, higher-order thinking skills, structural equation model

References

• Aho, A. V. (2012). Computation and computational thinking. The computer journal, 55(7), 832-835. doi: 10.1093/comjnl/bxs074
• Akın, A., Abacı, R., & Çetin, B. (2007). Bilişötesi Farkındalık Envanteri’nin Türkçe Formunun Geçerlik ve Güvenirlik Çalışması. Educational Sciences: Theory & Practice, 7(2), 655-680.
• Al Rabadi, W. M., & Salem, R. K. (2018). The Level of High-Order Thinking and Its Relation to Quality of Life among Students at Ajloun University College. International Education Studies, 11(6), 8-21. https://doi.org/10.5539/ies.v11n6p8
• Ananiadou, K., & Claro, M. (2009). 21St century skills and competences for new millennium learners in OECD countries. OECD education working papers, no. 41. OECD Publishing (NJ1).
• Anderson, J. R. (2005). Human symbol manipulation within an integrated cognitive architecture. Cognitive Science, 29(3), 313–341. https://doi.org/10.1207/s15516709cog0000_22
• Anderson, J.R. (1995) Cognitive Psychology and its Implications, 4th edition, W. H. Freeman and Company.
• Atkinson, S. (2000). Does the need for high levels of performance curtail the development of creativity in design and technology project work?. International Journal of Technology and Design Education, 10(3), 255-281.
• 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
• Barrón-Estrada, M.L., Zatarain-Cabada, R., Romero-Polo, J.A. et al. Patrony: A mobile application for pattern recognition learning. Educ Inf Technol 27, 1237–1260 (2022). https://doi.org/10.1007/s10639-021-10636-7
• Bayram, N. (2016). Yapısal eşitlik modellemesine giriş. AMOS uygulamaları (3. Baskı). Ankara: EzgiYayınları.
• Bers, M., Flannery, L., Kazakoff, E., & Sullivan, A. (2014). Computational thinking and tinkering: Exploration of an early childhood robotics curriculum. Computers & Education, 72, 145-157. https://doi.org/10.1016/j.compedu.2013.10.020
• Bloom, B., Englehart, M. D., Furst, E. J., Hill, W. H., & Krathwohl, D. R. (1956). Taxonomy of educational objectives: The classification of educational goals. Handbook I: Cognitive domain. New York, NY:David McKay.
• Braithwaite, D. W., & Sprague, L. (2021). Conceptual Knowledge, Procedural Knowledge, and Metacognition in Routine and Nonroutine Problem Solving. Cognitive Science, 45(10), e13048. https://doi.org/10.1111/cogs.13048
• Bravo, A., A. Porzecanski, E. Sterling, N. Bynum, M. Cawthorn, D. S. Fernandez, L. Freeman, S. Ketcham, T. Leslie, J. Mull & D. Vogler (2016). Teaching for higher levels of thinking: developing quantitative and analytical skills in environmental science courses. Ecosphere 7(4):e01290. 10.1002/ecs2.1290
• Brennan, K., & Resnick, M. (2012, April). 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 (Vol. 1, p. 25).
• Brookhart, S. M. (2010). How to Assess Higher-Order Thinking Skills in Your Classroom. Alexandria. Retrieved from http://www.ascd.org/ASCD/pdf/books/brookhart2010b_copyright.pdf
• Browne, M.W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In: Bollen, K.A., & Long, J.S. (Eds.), Testing structural equation models (pp. 136-162). Beverly Hills, CA: Sage.
• Buckley, S. (2012, October). The role of computational thinking and critical thinking in problem solving in a learning environment. In European Conference on e-Learning (pp. 63-70). Academic Conferences International Limited.
• Bussaban, K., & Waraporn, P. (2015). Preparing undergraduate students majoring in computer science and mathematics with data science perspectives and awareness in the age of big data. Procedia - Social and Behavioral Sciences, 197, 1443–1446. https://doi.org/10.1016/j.sbspro.2015.07.092
• Büyüköztürk, Ş. (2011). Sosyal bilimler için veri analizi el kitabı (15. Baskı). Ankara: Pegem Akademi.
• Calderon, A. C., Crick, T., & Tryfona, C. (2015, July). Developing computational thinking through pattern recognition in early years education. In Proceedings of the 2015 British HCI conference (pp. 259-260). https://doi.org/10.1145/2783446.2783600
• Can, A. (2014). SPSS ile bilimsel araştırma sürecinde nicel veri analizi (3. Baskı). Ankara: Pegem Akademi.
• Cañas, A. J., Reiska, P., & Möllits, A. (2017). Developing higher-order thinking skills with concept mapping: A case of pedagogic frailty. Knowledge Management & E-Learning, 9(3), 348–365.
• Casakin, H., Davidovitch, N., & Milgram, R. M. (2010). Creative thinking as a predictor of creative problem solving in architectural design students. Psychology of Aesthetics, Creativity, and the Arts, 4(1), 31–35. https://doi.org/10.1037/a0016965
• Cetin, I., & Dubinsky, E. (2017). Reflective abstraction in computational thinking. The Journal of Mathematical Behavior, 47, 70-80.https://doi.org/10.1016/j.jmathb.2017.06.004
• Choi, J., Lee, Y. & Lee, E. Puzzle Based Algorithm Learning for Cultivating Computational Thinking. Wireless Pers Commun 93, 131–145 (2017). https://doi.org/10.1007/s11277-016-3679-9
• Cross, D. R. & Paris, S. G. (1988). Developmental and instructional analyses of children’s metacognition and reading comprehension. Journal of Educational Psychology, 80(2), 131-142. https://doi.org/10.1037/0022-0663.80.2.131
• CSTA, (2017). Computer Science Teachers Association K-12 Computer Science Standards. Retrieved from 20 May 2022, https://www.csteachers.org/page/standards
• DeVellis, R. F. (2003). Scale development: Theory and applications (2nd ed.). Thousand Oaks, CA: Sage.
• Doğan, A., Ribas, M. A., & Begonya, M. R. (2009). Metacognitive tools in interpreting training: A pilot study. Hacettepe Üniversitesi Edebiyat Fakültesi Dergisi, 26(1).
• Doleck, T., Bazelais, P., Lemay, D.J. et al. (2017). Algorithmic thinking, cooperativity, creativity, critical thinking, and problem solving: exploring the relationship between computational thinking skills and academic performance. J. Comput. Educ. 4, 355–369. https://doi.org/10.1007/s40692-017-0090-9
• Dolmacı, A., & Akhan, N. E. (2020). Bilişimsel Düşünme Becerileri Ölçeğinin Geliştirilmesi: Geçerlik ve Güvenirlik Çalışması. İnsan ve Toplum Bilimleri Araştırmaları Dergisi, 9(3), 3050-3071.
• Drmrod, J.E. (1990). Human Learning. Macmillan P. Company. New York.
• Eby, J. W., & Kujawa, E. (1994). Reflective planning, teaching and evaluation: K-12. New York: Macmillan Publishing Company.
• Ertugrul-Akyol, B. (2019). Development of computational thinking scale: Validity and reliability study. International Journal of Educational Methodology, 5(3), 421-432.
• Fitzgerald, S., Lewandowski, G., McCauley, R., Murphy, L., Simon, B., Thomas, L., & Zander, C. (2008). Debugging: finding, fixing and flailing, a multi-institutional study of novice debuggers. Computer Science Education, 18(2), 93-116.
• Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive–developmental inquiry. American psychologist, 34(10), 906-911.
• Flavell, J.H. (1976). Metacognitive aspects of problem solving. In L. Resnick, ed., The Nature of Intelligence. Hillsdale, NJ: Lawrence Erlbaum As.
• Garner, R., & Alexander, P. A. (1989). Metacognition: Answered and unanswered questions. Educational psychologist, 24(2), 143-158. https://doi.org/10.1207/s15326985ep2402_2
• Ghanizadeh, A. (2017). The interplay between reflective thinking, critical thinking, self-monitoring, and academic achievement in higher education. Higher Education, 74(1), 101-114. https://doi.org/10.1007/s10734-016-0031-y
• Göğüş, A., Göğüş, N.G., & Bahadır, E. (2020). Intersections between critical thinking skills and reflective thinking skills toward problem solving. Pamukkale Üniversitesi Eğitim Fakültesi Dergisi ,49, 1-19. doi:10.9779/pauefd.526407
• Gresse Von Wangenheım, C., Araújo E Sılva De Medeıros, G., Mıssfeldt Fılho, R., Petrı, G., Da Cruz Pınheıro, F., F.
• Ferreıra, M. N., & Hauck, J. C. R. (2019). SplashCode - A Board Game for Learning an Understanding of Algorithms in Middle School. Informatics in Education, 18(2), 259-280. doi:10.15388/infedu.2019.12
• 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
• Gülbahar, Y., Kert, S. B. & Kalelioğlu F. (2018). Bilgi Işlemsel Düşünme Becerisine Yönelik Öz Yeterlik Algısı Ölçeği (BİDBÖA): Geçerlik Ve Güvenirlik Çalışması. Türk Bilgisayar ve Matematik Eğitimi Dergisi. Advance online publication. doi:10.16949/turkbilmat.385097
• Güner, P., & Erbay, H. N. (2021). Prospective mathematics teachers’ thinking styles and problem-solving skills. Thinking Skills and Creativity, 40, 100827. https://doi.org/10.1016/j.tsc.2021.100827
• Gürbüz, S. (2019). AMOS ile yapısal eşitlik modellemesi. Ankara: Seçkin Yayınları.
• Hamzah, H., Hamzah, M. I., & Zulkifli, H. (2022). Systematic Literature Review on the Elements of Metacognition-Based Higher Order Thinking Skills (HOTS) Teaching and Learning Modules. Sustainability, 14(2), 813. https://doi.org/10.3390/su14020813
• Hartman, H. J. (1998). Metacognition in teaching and learning: An introduction [Special issue]. Instructional Science, 1-3.
• He, Z.; Wu, X.; Wang, Q.; Huang, C. (2021) Developing Eighth-Grade Students’ Computational Thinking with Critical Reflection. Sustainability, 13, 11192. https://doi.org/10.3390/su132011192
• Ho R. (2006). Handbook of univariate and multivariate data analysis and interpretation with SPSS. Boca Raton, FL: CRC Press.
• Hooshyar, D., Malva, L., Yang, Y., Pedaste, M., Wang, M., Lim, H. (2021). An adaptive educational computer game: Effects on students’ knowledge and learning attitude in computational thinking. Computers in Hu¬man Behavior, 114, 106575. https://doi.org/https://doi.org/10.1016/j.chb.2020.106575
• Husamah, H., Fatmawati, D., & Setyawan, D. (2018). OIDDE learning model: Improving higher order thinking skills of biology teacher candidates. International Journal of Instruction, 11(2), 249-264.
• Jackson, D. L., Gillaspy J. A., & Purc-Stephenson, R. (2009). Reporting practices in confirmatory factor analysis: an overview and some recommendations. Psychological methods, 14, 6-23.
• 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), 583–596.
• Karalar, H., & Alpaslan, M. M. (2021). Assessment of eighth grade students’ domain-general computational thinking skills. International Journal of Computer Science Education in Schools, 5(1), 35–47. https://doi.org/10.21585/ijcses.v5i1.126
• Kim, C., Yuan, J., Vasconcelos, L. et al. Debugging during block-based programming. Instr Sci 46, 767–787 (2018). https://doi.org/10.1007/s11251-018-9453-5
• Kim, S., Raza, M., & Seidman, E. (2019). Improving 21st-century teaching skills: The key to effective 21st-century learners. Research in Comparative and International Education, 14(1), 99–117. https://doi.org/10.1177/1745499919829214
• Kline, R. B., (2005). Principles and Practice of Structural Equation Modeling (2nd ed.). New York: Guilford Press. Knochel, A. D., & Patton, R. M. (2015). If art education then critical digital making: Computational thinking and creative code. Studies in Art Education, 57(1), 21–38. https://doi.org/10.1080/00393 541.2015.11666280
• 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.
• Kukul, V., & Karatas, S. (2019). Computational thinking self-efficacy scale: Development, validity and reliability. Informatics in Education, 18(1), 151-164.
• Kumar, A. E. (1998). The influence of metacognition on managerial hiring decision making: Implications for management development. Doctoral dissertation, Virginia Polytechnic Institute and State University.
• Lee, H. J. (2005). Understanding and assessing preservice teachers’ reflective thinking. Teaching and teacher education, 21(6), 699-715. https://doi.org/10.1016/j.tate.2005.05.007
• Lee, K. H. (2005). The Relationship between Creative Thinking Ability and Creative Personality of Preschoolers. International Education Journal, 6(2), 194-199.
• Lee, S. J., Francom, G. M., & Nuatomue, J. (2022). Computer science education and K-12 students’ computational thinking: A systematic review. International Journal of Educational Research. https://doi.org/10.1016/j.ijer.2022.102008
• Lu, C., Macdonald, R., Odell, B. et al. (2022). A scoping review of computational thinking assessments in higher education. J Comput High Educ 34, 416–461 https://doi.org/10.1007/s12528-021-09305-y
• Mayer, R. E. (1998). Cognitive, metacognitive, and motivational aspects of problem solving. Instructional science, 26(1), 49-63. https://doi.org/10.1023/A:1003088013286
• Meijer, J., Veenman, M. V., & van Hout-Wolters, B. H. (2006). Metacognitive activities in text-studying and problem-solving: Development of a taxonomy. Educational Research and Evaluation, 12(3), 209-237. doi: 10.1080/13803610500479991
• Meng, Q., Jia, J., & Zhang, Z. (2020). A framework of smart pedagogy based on the facilitating of high order thinking skills. Interactive Technology and Smart Education.
• Miri, B., David, B. C., & Uri, Z. (2007). Purposely teaching for the promotion of higher-order thinking skills: A case of critical thinking. Research in science education, 37(4), 353-369.
• Mumford, M. D., Giorgini, V., Gibson, C., & Mecca, J. (2013). Creative thinking: Processes, strategies and knowledge. In Handbook of research on creativity. Edward Elgar Publishing.
• Newmann, F. M. (1988). Higher Order Thinking in the High School Curriculum. NASSP Bulletin, 72(508), 58–64. https://doi.org/10.1177/019263658807250812
• Nguyễn, T. M. T., & Nguyễn, T. T. L. (2017). Influence of explicit higher-order thinking skills instruction on students’ learning of linguistics. Thinking Skills and Creativity, 26, 113-127.
• Ohtani, K., Hisasaka, T. Beyond intelligence: a meta-analytic review of the relationship among metacognition, intelligence, and academic performance. Metacognition Learning 13, 179–212 (2018). https://doi.org/10.1007/s11409-018-9183-8
• Özgenel, M. & Çetin, M. (2018). Development of the Marmara critical thinking dispositions scale: Validity and reliability analysis. International Journal of Eurasia Social Sciences, Vol: 9, Issue: 32, pp. (991-1015).
• Özmutlu, M., Atay, D., & Erdoğan, B. (2021). Collaboration and engagement based coding training to enhance children’s computational thinking self-efficacy. Thinking Skills and Creativity, 40, 100833.
• Palts, T., & Pedaste, M. (2020). A model for developing computational thinking skills. Informatics in Education, 19(1), 113-128.
• Pedaste M., Palts T., Kori K., Sõrmus M. & Leijen Ä., "Complex Problem Solving as a Construct of Inquiry, Computational Thinking and Mathematical Problem Solving," 2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT), 2019, pp. 227-231, doi: 10.1109/ICALT.2019.00071.
• Pegg, J. (2010), “Promoting the acquisition of higher-order skills and understandings in primary and secondary mathematics”, Research Conference 2010, pp. 35-38. Retrieved from https://research.acer.edu.au/research_conference/RC2010/16august/19/
• Qian, Y., Choi, I. Tracing the essence: ways to develop abstraction in computational thinking. Education Tech Research Dev (2022). https://doi.org/10.1007/s11423-022-10182-0
• Resnick, L. (1987). Education and learning to think. Washington, DC: National Academy.
• Rhodes, M. G. (2019). Metacognition. Teaching of Psychology, 46(2), 168-175. https://doi.org/10.1177/0098628319834381
• Rıjke, W. J., Bollen, L., Eysınk, T. H. S., & Tolboom, J. L. J. (2018). Computational Thinking in Primary School: An Examination of Abstraction and Decomposition in Different Age Groups. Informatics in Education, 17(1), 77-92. doi:10.15388/infedu.2018.05
• Rich, P. J., Larsen, R. A., & Mason, S. L. (2021). Measuring teacher beliefs about coding and computational thinking. Journal of Research on Technology in Education, 53(3), 296-316.
• Rijo-García, S., Segredo, E., & León, C. (2022). Computational Thinking and User Interfaces: A Systematic Review. IEEE Transactions on Education.
• Rodgers, C. (2002). Defining Reflection: Another Look at John Dewey and Reflective Thinking. Teachers College Record, 104(4), 842–866. https://doi.org/10.1111/1467-9620.00181
• Saad, A., & Zainudin, S. (2022). A review of Project-Based Learning (PBL) and Computational Thinking (CT) in teaching and learning. Learning and Motivation, 78, 101802. https://doi.org/10.1016/j.lmot.2022.101802
• Schneider, W. & Lockl, K. (2002). The development of metacognitive knowledge in childrenand adolescents. In Perfect, T. & Schwartz, B. (Eds.), Applied metacognition. Cambridge, UK: Cambridge University Press.
• Schraw, G., & Dennison, R. S. (1994). Assessing metacognitive awareness. Contemporary educational psychology, 19(4), 460-475. https://doi.org/10.1006/ceps.1994.1033.
• Selby, C., & Woollard, J. (2013). Computational thinking: the developing definition. Proceedings of the 45th ACM technical symposium on computer science education. Canterbury, ACM.
• Shute, V. J., Sun, C., & Asbell-Clarke, J. (2017). Demystifying computational thinking. Educational Research Review, 22, 142-158.
• Simamora, R. E., & Saragih, S. (2019). Improving Students' Mathematical Problem Solving Ability and Self-Efficacy through Guided Discovery Learning in Local Culture Context. International Electronic Journal of Mathematics Education, 14(1), 61-72. https://doi.org/10.12973/iejme/3966
• Stanger-Hall, K. F. (2012). Multiple-choice exams: an obstacle for higher-level thinking in introductory science classes. CBE—Life Sciences Education, 11(3), 294-306.
• Tabachnick, B. G., & Fidell, L. S. (2001). Using multivariate statistics (4th ed.). New York: Harper & Row.
• Tang, X., Yin, Y., Lin, Q., Hadad, R., & Zhai, X. (2020). Assessing computational thinking: A systematic review of empirical studies. Computers & Education, 148, 103798. https://doi.org/10.1016/j.compedu.2019.103798
• Tsai, M. J., Liang, J. C., Lee, S. W. Y., & Hsu, C. Y. (2022). Structural validation for the developmental model of computational thinking. Journal of Educational Computing Research, 60(1), 56-73. https://doi.org/10.1177/07356331211017
• Umay, A., & Ariol, S., (2011). A Comprasion Problem Solving Skills in Terms of Holistic and Analytical Thinking Styles. Pamukkale Unıversity Journal of Education , no.30, 27-37.
• Uzumcu, O., Bay, E. (2021). The effect of computational thinking skill program design developed according to interest driven creator theory on prospective teachers. Educ Inf Technol 26, 565–583. https://doi.org/10.1007/s10639-020-10268-3
• Van Borkulo, S., Chytas, C., Drijvers, P., Barendsen, E., & Tolboom, J. (2021, October). Computational thinking in the mathematics classroom: fostering algorithmic thinking and generalization skills using dynamic mathematics software. In The 16th Workshop in Primary and Secondary Computing Education (pp. 1-9).
• Vourletsis, I., Politis, P. & Karasavvidis, I. (2021). The Effect of a Computational Thinking Instructional Intervention on Students’ Debugging Proficiency Level and Strategy Use. In: Tsiatsos, T., Demetriadis, S., Mikropoulos, A., Dagdilelis, V. (eds) Research on E-Learning and ICT in Education. Springer, Cham. https://doi.org/10.1007/978-3-030-64363-8_2
• Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35. https://doi.org/10.1145/1118178.1118215
• Wing, J. M. (2008). Computational thinking and thinking about computing. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 366(1881), 3717-3725.
• 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), 1-16. https://doi.org/10.1145/2576872
• Yadav, A., Ocak, C. & Oliver, A. (2022). Computational Thinking and Metacognition. TechTrends 66, 405–411. https://doi.org/10.1007/s11528-022-00695-z
• Yağcı, M. (2019). A valid and reliable tool for examining computational thinking skills. Education and Information Technologies, 24(1), 929-951.
• Yurdakal, H.İ. (2019). Yaratıcı okuma çalışmalarının yaratıcı düşünme becerilerini geliştirmeye etkisi. Pamukkale Üniversitesi Eğitim Fakültesi Dergisi,47, 130-144. doi: 10.9779/pauefd.492812
• Zain, F. M., Sailin, S. N., & Mahmor, N. A. (2022). Promoting higher order thinking skills among pre-service teachers through group-based flipped learning. International Journal of Instruction, 15(3), 519-542. https://doi.org/10.29333/iji.2022.15329a
• Zhang, L. & Nouri, J. (2019). A systematic review of learning computational thinking through Scratch in K-9. Computers & Education, 141, 103607. https://doi.org/https://doi.org/10.1016/j.compedu.2019.103607
• Zohar, A. (1999). Teachers’ metacognitive knowledge and the instruction of higher order thinking. Teaching and teacher Education, 15(4), 413-429.