Reliability and Construct Validity of Computational Thinking Scale for Junior High School Students: Thai Adaptation
Keywords:Computational Thinking, Computational Thinking Scale, Psychometric Properties, Junior High Students
Computational thinking (CT) is defined as a broad spectrum of cognitive abilities including creativity, algorithmic reasoning, critical analysis, problem-solving, collaborative thinking, and communication. There are currently not many self-rated CT skill measurements available. One of these tools for measurement is the Korkmaz Computational Thinking Scale (CTS). The purposes of this present study are to adapt the Korkmaz CTS into Thai and to assess its reliability and validity. Employing a convenience sampling method, data from 3,241 junior high school students in Thailand were collected using Thai translated Korkmaz CTS. Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were used for data analysis. According to the findings, Thai version of Korkmaz CTS exhibited reliable psychometric properties. However, one item from the Thai CTS was eliminated during the EFA process whereas six items were removed during the CFA. Thus, the Thai CTS can be used as a self-rating instrument to assess the CT of junior high school students in addition to high school and undergraduate students. Schools can measure students’ CT faster and with cost-saving.
Agogi, E., Rossis, D., & Stylianidou, F. (2014). Creative little scientist: D 6.6 set of recommendations to policy makers and stakeholders.
Aho, A. (2012). Computation and Computational Thinking. The Computer Journal, 55, 832-835. https://doi.org/10.1093/comjnl/bxs074
Alyahya, D., & Alotaibi, A. (2019). Computational thinking skills and its impact on TIMSS achievement: An Instructional Design Approach. Issues and Trends in Educational Technology, 7. https://doi.org/10.2458/azu_itet_v7i1_alyahya
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. https://doi.org/10.1145/1929887.1929905
Brandell, J. R. (2010). Theory & Practice in Clinical Social Work. SAGE Publications.
Brown, W. (2015). Introduction to Algorithmic Thinking. https://www.studocu.com/en-us/document/university-of-arizona/analytical-methods-for-business/introduction-to-algorithmic-thinking/1427940
Csizmadia, A. P., Curzon, P., Dorling, M., Humphreys, S., Ng, T., Selby, C. C., & Woollard, J. (2015). Computational thinking - a guide for teachers.
CSTA. (2017). CSTA K-12 Computer Science Standards. http://www.csteachers.org/standards
Cuny, J., Snyder, L., & Wing, J. M. (2010). Demystifying Computational Thinking for Non-Computer Scientists,. http://www.cs.cmu.edu/~CompThink/resources/TheLinkWing.pdf
Curzon, P. (2015). Computational thinking: Searching to speak. https://teachinglondoncomputing.org/free-workshops/computational-thinking-searching-to-speak/
Deniz, K. (2007). The Adaptation of Psychological Scales. Ankara Universitesi Egitim Bilimleri Fakultesi Dergisi, 40, 001-016. https://doi.org/10.1501/Egifak_0000000160
Denning, P. (2009). The Profession of IT Beyond Computational Thinking. Communications of the ACM, 52, 28-30. https://doi.org/10.1145/1516046.1516054
Doleck, T., Bazelais, P., Lemay, D., Saxena, A., & Basnet, R. (2017). Algorithmic thinking, cooperativity, creativity, critical thinking, and problem solving: exploring the relationship between computational thinking skills and academic performance. Journal of Computers in Education, 4, 355–369https://doi.org/10.1007/s40692-017-0090-9
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
Ertu?rul-Akyol, B. (2019). Development of Computational Thinking Scale: Validity and Reliability Study. International Journal of Educational Methodology, 5(3), 421-432. https://doi.org/10.12973/ijem.5.3.421
Farris, A. V., & Sengupta, P. (2014). Perspectival Computational Thinking for Learning Physics: A Case Study of Collaborative Agent-based Modeling. Proceedings of International Conference of the Learning Sciences, ICLS, 2.
Futschek, G. (2006). Algorithmic Thinking: The Key for Understanding Computer Science. https://doi.org/10.1007/11915355_15
Gana, K., & Broc, G. (2019). Structural Equation Modeling with Iavaan. Wiley.
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
Grover, S., & Pea, R. (2017). Computational Thinking: A Competency Whose Time Has Come. In. https://doi.org/10.5040/9781350057142.ch-003
Grover, S., & Pea, R. (2018). Computational thinking: A competency whose time has come. Computer science education: Perspectives on teaching and learning in school, 19, 1257-1258.
Günbatar, M. S. (2019). Computational thinking within the context of professional life: Change in CT skill from the viewpoint of teachers. Education and Information Technologies, 24(5), 2629-2652. https://doi.org/10.1007/s10639-019-09919-x
Gunuc, S., Odaba?i, H. F., & Kuzu, A. (2013). The definition of the 21. Century students by Pre-service teachers: A Twitter Application. Hypothesis in Education and Application, 9, 436-455.
Hair, J. F., Babin, B. J., Anderson, R. E., & Black, W. C. (2022). Multivariate Data Analysis. Cengage Learning.
Hambleton, R. K., & Patsula, L. N. (1999). Increasing the Validity of Adapted Tests: Myths to be Avoided and Guidelines for Improving Test Adaptation Practices.
Human Age Institute. (2016).More than 980 companies and institutions are part of the Human Age Institute. https://humanageinstitute-org.translate.goog/?_x_tr_sl=es&_x_tr_tl=en&_x_tr_hl=en&_x_tr_pto=sc
Israel, M., Wherfel, Q., Pearson, J., Shehab, S., & Tapia, T. (2015). Empowering K-12 Students With Disabilities to Learn Computational Thinking and Computer Programming. TEACHING Exceptional Children, 48, 45-53. https://doi.org/10.1177/0040059915594790
ISTE. (2015). CT leadership toolkit. https://cdn.iste.org/www-root/2020-10/ISTE_CT_Leadership_Toolkit_booklet.pdf
Jackson, L. A., Witt, E. A., Games, A. I., Fitzgerald, H. E., von Eye, A., & Zhao, Y. (2012). Information technology use and creativity: Findings from the Children and Technology Project. Computers in Human Behavior, 28(2), 370-376. https://doi.org/10.1016/j.chb.2011.10.006
Janpilom, N., Kunlaya, S., Roungrong, P., & Kaewurai , R. (2019). EDUCATIONAL TECHNOLOGY WITHIN THAILAND 4.0. Panyapiwat Journal, 11(1), 304-314.
Kaiser, H. F. (1960). The Application of Electronic Computers to Factor Analysis. Educational and Psychological Measurement, 20(1), 141-151. https://doi.org/10.1177/001316446002000116
Katai, Z. (2015). The challenge of promoting algorithmic thinking of both sciences- and humanities-oriented learners. Journal of Computer Assisted Learning, 31(4), 287-299. https://doi.org/https://doi.org/10.1111/jcal.12070
Korkmaz, Ö. (2012). A validity and reliability study of the Online Cooperative Learning Attitude Scale (OCLAS). Computers & Education, 59, 1162–1169. https://doi.org/10.1016/j.compedu.2012.05.021
Korkmaz, Ö., & Bai, X. (2019). Adapting Computational Thinking Scale (CTS) for Chinese High School Students and Their Thinking Scale Skills Level. Participatory Educational Research.
Korkmaz, Ö., Çakir, R., & Ozden, M. (2017). A validity and reliability study of the Computational Thinking Scales (CTS). Computers in Human Behavior, 72. https://doi.org/10.1016/j.chb.2017.01.005
Koul, R. (2018). Work and Family Identities and Engineering Identity. Journal of Engineering Education, 107(2), 219-237. https://doi.org/https://doi.org/10.1002/jee.20200
Kukul, V. (2019). Computational Thinking Self-Efficacy Scale: Development, Validity and Reliability. Informatics in Education, 18, 151-164. https://doi.org/10.15388/infedu.2019.07
Kules, B. (2016). Computational thinking is critical thinking: Connecting to university discourse, goals, and learning outcomes. Proceedings of the Association for Information Science and Technology, 53(1), 1-6. https://doi.org/https://doi.org/10.1002/pra2.2016.14505301092
Law, K. E., Karpudewan, M., & Zaharudin, R. (2021). Computational thinking in STEM education among matriculation science students. Asia Pacific Journal of Educators and Education, 36, 177-194. https://doi.org/10.21315/apjee2021.36.1.10
Lemay, D.J., Basnet, R. B., Doleck, T., Bazelais, P., & Saxena, A. (2021). Instructional interventions for computational thinking: Examining the link between computational thinking and academic performance. Computers and Education Open, 2, 100056. https://doi.org/https://doi.org/10.1016/j.caeo.2021.100056
Lomax, R. G., & Hahs-Vaughn, D. L. (2013). An Introduction to Statistical Concepts: Third Edition. Taylor & Francis.
Mannila, L., Settle, A., Dagiene, V., Demo, B., Grgurina, N., Mirolo, C., & Rolandsson, L. (2014). Computational Thinking in K-9 Education. ITiCSE-WGR '14: Proceedings of the Working Group Reports of the 2014 on Innovation & Technology in Computer Science Education Conference. New York: Association for Computing Machinery. https://doi.org/10.1145/2713609.2713610
Mindetbay, Y., Bokhove, C., & Woollard, J. (2019). What is the Relationship between Students’ Computational Thinking Performance and School Achievement? International Journal of Computer Science Education in Schools, 2(5), 3–19. https://doi.org/10.21585/ijcses.v0i0.45
Missiroli, M., Russo, D., & Ciancarini, P. (2017). Cooperative Thinking, or: Computational Thinking Meets Agile. 2017 IEEE 30th Conference on Software Engineering Education and Training (CSEE&T), 7-9 Nov.
Nam, C. W. (2014). The effects of trust and constructive controversy on student achievement and attitude in online cooperative learning environments. Computers in Human Behavior, 37, 237-248. https://doi.org/https://doi.org/10.1016/j.chb.2014.05.007
National Research Council. (2011). Report of a Workshop on the Pedagogical Aspects of Computational Thinking. The National Academies Press. https://doi.org/doi:10.17226/13170
O’Rourke, N., & Hatcher, L. (2013). A Step-by-Step Approach to Using SAS for Factor Analysis and Structural Equation Modeling, 2nd ed. SAS Institute.
Özgür, H. (2020). Relationships between Computational Thinking Skills, Ways of Thinking and Demographic Variables: A Structural Equation Modeling. International Journal of Research in Education and Science, 6, 299. https://doi.org/10.46328/ijres.v6i2.862
Paf, M., & Dinçer, B. (2021). A Study of the Relationship between Secondary School Students’ Computational Thinking Skills and Creative Problem-Solving Skills. Turkish Online Journal of Educational Technology, 20, 1-15.
Papert, S. (1996). An exploration in the space of mathematics educations. International Journal of Computers for Mathematical Learning, 1(1), 95-123. https://doi.org/10.1007/BF00191473
R Core Team. (2020). R: A language and environment for statistical computing.
Riley, D. D., & Hunt, K. (2014). Computational Thinking for the Modern Problem Solver.
Sefero?lu, S. S., & Akb?y?k, C. (2006). Teaching critical thinking. Hacettepe Üniversitesi E?itim Fakültesi Dergisi, 30(30), 193-200.
S?rakaya, M., Alsancak S?rakaya, D., & Korkmaz, Ö. (2020). The Impact of STEM Attitude and Thinking Style on Computational Thinking Determined via Structural Equation Modeling. Journal of Science Education and Technology, 29(4), 561-572. https://doi.org/10.1007/s10956-020-09836-6
Standl, B. (2016). A case study on cooperative problem solving processes in small 9th grade student groups. 2016 IEEE Global Engineering Education Conference (EDUCON), 2016, pp. 961-967, https://doi.org/10.1109/EDUCON.2016.7474667
Stevens, J. P. (2012). Applied Multivariate Statistics for the Social Sciences, 5th ed. Taylor & Francis.
Syslo, M., & Kwiatkowska, A. (2013). Informatics for all high school students: A computational thinking approach In I. Diethelm & R.T. Mittermeir (Eds.), Informatics in Schools. Sustainable Informatics Education for Pupils of all Ages. ISSEP 2013. Lecture Notes in Computer Science, vol 7780. Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-642-36617-8_4
Tang, K.-Y., Chou, T.-L., & Tsai, C.-C. (2020). A Content Analysis of Computational Thinking Research: An International Publication Trends and Research Typology. The Asia-Pacific Education Researcher, 29(1), 9-19. https://doi.org/10.1007/s40299-019-00442-8
Tikva, C., & Tambouris, E. (2021). Mapping computational thinking through programming in K-12 education: A conceptual model based on a systematic literature Review. Computers & Education, 162, 104083. https://doi.org/10.1016/j.compedu.2020.104083
Tsai, M.-J., Liang, J.-C., & Hsu, C.-Y. (2021). The Computational Thinking Scale for Computer Literacy Education. Journal of Educational Computing Research, 59(4), 579-602. https://doi.org/10.1177/0735633120972356
Vallance, M., & Towndrow, P. (2016). Pedagogic transformation, student-directed design and computational thinking. Pedagogies: An International Journal, 11, 1-17. https://doi.org/10.1080/1554480X.2016.1182437
Voogt, J., Fisser, P., Good, J., Mishra, P., & Yadav, A. (2015). Computational thinking in compulsory education: Towards an agenda for research and practice. Education and Information Technologies, 20. https://doi.org/10.1007/s10639-015-9412-6
Wing, J. (2006). Computational Thinking. Communications of the ACM, 49, 33-35. https://doi.org/10.1145/1118178.1118215
Wing, J. (2008). Computational thinking and thinking about computing. Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 366, 3717-3725. https://doi.org/10.1098/rsta.2008.0118
Wing, J. M. (2010). Computational Thinking: What and Why?
Wing, J. M. (2014). Computational thinking benefts society.
Wong, G., & Jiang, S. (2018). Computational Thinking Education for Children: Algorithmic Thinking and Debugging. 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE), 2018, pp. 328-334, doi: 10.1109/TALE.2018.8615232
Yadav, A., Stephenson, C., & Hong, H. (2017). Computational thinking for teacher education. Communications of the ACM, 60, 55-62. https://doi.org/10.1145/2994591
Ya?c?, M. (2019). A valid and reliable tool for examining computational thinking skills. Education and Information Technologies, 24(1), 929-951. https://doi.org/10.1007/s10639-018-9801-8
Yilmaz, A., Gülgün, C., Çetinkaya, M., & Do?anay, K. (2018). Initiatives and New Trends Towards STEM Education in Turkey. Journal of Education and Training Studies 6, 1-10. https://doi.org/10.11114/jets.v6i11a.3795
Copyright (c) 2022 Meechai Junpho, Alisa Songsriwittaya, Puthyrom Tep
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
All articles published by IJLTER are licensed under a Creative Commons Attribution Non-Commercial No-Derivatives 4.0 International License (CCBY-NC-ND4.0).