Abstract
This study investigated the effects of the single programming approach (plugged-in and unplugged) and the mixed programming approach (plugged-in-first and unplugged-first) on the computational thinking (CT) skills of first-grade students. However, focusing only on the programming learning approach itself is insufficient. Therefore, the influences of students’ gender, programming experience, programming interest, and programming confidence factors on CT skills were also examined. 121 students from China were divided into four experimental and one control groups and engaged in the programming activities intervention for 10 weeks. The data consisted mainly of students’ CT skill scores before and after the programming activities intervention. The results showed that both single and mixed programming approaches significantly improved students’ CT skills, with the mixed programming approaches being more effective. Furthermore, the study found that the implementation of unplugged activities in the first stage attenuated the effects of programming experience. Furthermore, it was found that the unplugged-first programming approach was able to diminish the effect of students’ programming experience on the development of CT skills and could be an essential condition to promote the development of equal CT skills. We also clarified the important role of programming interest and programming confidence in students’ CT development. More importantly, a chain mediation effect of programming experience and programming interest between programming confidence and CT was also found. Finally, this study further discusses ideas and approaches for the future of CT education for primary school students and provides certain practical suggestions and insights for teachers and researchers.
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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.
Bandura, A., & Wessels, S. (1994). Self-efficacy (Vol. 4). na.
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.
Bell, T., & Vahrenhold, J. (2018). CS Unplugged—How Is It Used, and Does It Work? In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications (pp. 497–521). https://doi.org/10.1007/978-3-319-98355-4_29.
Beyer, S. (2014). Why are women underrepresented in Computer Science? Gender differences in stereotypes, self-efficacy, values, and interests and predictors of future CS course-taking and grades. Computer Science Education, 24(2–3), 153–192. https://doi.org/10.1080/08993408.2014.963363.
Bocconi, S., Chioccariello, A., Kampylis, P., Dagienė, V., Wastiau, P., Engelhardt, K., Earp, J., Horvath, M. A., Jasutė, E., & Malagoli, C. (2022). Reviewing computational thinking in Compulsory Education. Joint Research Centre. https://digital-skills-jobs.europa.eu/en/inspiration/research/reviewing-computational-thinking-compulsory-education-jrc-2022-1 (Seville site).
Brackmann, C. P., Román-González, M., Robles, G., Moreno-León, J., Casali, A., & Barone, D. (2017). Development of computational thinking skills through unplugged activities in primary school. Proceedings of the 12th workshop on primary and secondary computing education (pp.65–72).
Bray, M. (2018). Plugged in: The dangers of modern technology. https://go.gale.com/.
Brennan, K., & Resnick, M. (2012). New frameworks for studying and assessing the development of computational thinking. Proceedings of the 2012 annual meeting of the American educational research association (pp. 1–25), Vancouver, Canada.
Bybee, R. W., Taylor, J. A., Gardner, A., Van Scotter, P., Powell, J. C., Westbrook, A., & Landes, N. (2006). The BSCS 5E instructional model: Origins and effectiveness. Colorado Springs Co: BSCS, 5, 88–98.
Chiu, M. M., & Klassen, R. M. (2010). Relations of mathematics self-concept and its calibration with mathematics achievement: Cultural differences among fifteen-year-olds in 34 countries. Learning and Instruction, 20(1), 2–17. https://doi.org/10.1016/j.learninstruc.2008.11.002.
Clements, D. H., Sarama, J., Wolfe, C. B., & Spitler, M. E. (2015). Sustainability of a Scale-Up intervention in early mathematics: A longitudinal evaluation of implementation fidelity [Article]. Early Education and Development, 26(3), 427–449. https://doi.org/10.1080/10409289.2015.968242.
Code.org (2013). Anybody can learn. https://hourofcode.com/us/zh.
Computer Science Teachers Association (CSTA), & International Society for Technology in Education (ISTE) (2011). Operational Defnition of Computational Thinking for K-12 Education. http://www.iste.org/docs/pdfs/Operational-Defnition-of-Computational-Thinking.pdf.
Cronbach, L. J., & Meehl, P. E. (1955). Construct validity in psychological tests. Psychological Bulletin, 52(4), 281.
Deci, E. L., Olafsen, A. H., & Ryan, R. M. (2017). Self-determination theory in work organizations: The state of a science. Annual Review of Organizational Psychology and Organizational Behavior, 4, 19–43. https://doi.org/10.1146/annurev-orgpsych-032516-113108.
del Olmo-Muñoz, J., Cózar-Gutiérrez, R., & González-Calero, J. A. (2020). Computational thinking through unplugged activities in early years of Primary Education. Computers & Education. https://doi.org/10.1016/j.compedu.2020.103832
Dewey, J. (1913). Interest and effort in education. London: Forgotten Books.
European Commission (2020). Digital Education Action Plan 2021–2027. https://education.ec.europa.eu/focus-topics/digital-education/about-digital-education.
Fessakis, G., Gouli, E., & Mavroudi, E. (2013). Problem solving by 5–6 years old kindergarten children in a computer programming environment: A case study. Computers & Education, 63, 87–97. https://doi.org/10.1016/j.compedu.2012.11.016.
Gao, X., & Hew, K. F. (2021). Toward a 5E-Based flipped Classroom Model for Teaching Computational thinking in Elementary School: Effects on Student Computational thinking and problem-solving performance. Journal of Educational Computing Research, 60(2), 512–543. https://doi.org/10.1177/07356331211037757.
Gao, H., Hasenbein, L., Bozkir, E., Göllner, R., & Kasneci, E. (2022). Exploring gender differences in computational thinking learning in a VR Classroom: Developing machine learning models using Eye-Tracking Data and explaining the models. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-022-00316-z.
Gunbatar, M. S., & Karalar, H. (2018). Gender differences in middle school students’ attitudes and self-efficacy perceptions towards mBlock programming. European Journal of Educational Research, 7(4), 925–933. https://doi.org/10.12973/EU-JER.7.4.925.
Gur, R. C., Richard, J., Calkins, M. E., Chiavacci, R., Hansen, J. A., Bilker, W. B., Loughead, J., Connolly, J. J., Qiu, H., Mentch, F. D., Abou-Sleiman, P. M., Hakonarson, H., & Gur, R. E. (2012). Age group and sex differences in performance on a computerized neurocognitive Battery in children age 8–21. Neuropsychology, 26(2), 251–265. https://doi.org/10.1037/a0026712.
Helmlinger, B., Sommer, M., Feldhammer-Kahr, M., Wood, G., Arendasy, M. E., & Kober, S. E. (2020). Programming experience associated with neural efficiency during figural reasoning. Scientific Reports, 10(1), 13351. https://doi.org/10.1038/s41598-020-70360-z.
Hermans, F., & Aivaloglou, E. (2017). To Scratch or not to Scratch? Proceedings of the 12th Workshop on Primary and Secondary Computing Education (pp. 49–56).
Hidi, S. (2006). Interest: A unique motivational variable. Educational Research Review, 1(2), 69–82.
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.
Israel, M., Pearson, J. N., Tapia, T., Wherfel, Q. M., & Reese, G. (2015). Supporting all learners in school-wide computational thinking: A cross-case qualitative analysis. Computers & Education, 82, 263–279. https://doi.org/10.1016/j.compedu.2014.11.022.
Izu, C., Mirolo, C., Settle, A., Mannila, L., & Stupuriene, G. (2017). Exploring Bebras tasks Content and performance: A multinational study. Informatics in Education, 16(1), 39–59. https://doi.org/10.15388/infedu.2017.03.
Jiang, S., & Wong, G. K. W. (2021). Exploring age and gender differences of computational thinkers in primary school: A developmental perspective. Journal of Computer Assisted Learning, 38(1), 60–75. https://doi.org/10.1111/jcal.12591.
Jong, M. S. Y., Geng, J., Chai, C. S., & Lin, P. Y. (2020). Development and predictive validity of the computational thinking disposition questionnaire. Sustainability. https://doi.org/10.3390/su12114459
Kale, U., & Yuan, J. (2020). Still a new kid on the Block? Computational thinking as Problem solving in Code.org. Journal of Educational Computing Research, 59(4), 620–644. https://doi.org/10.1177/0735633120972050.
Kalelioglu, F., Gulbahar, Y., & Kukul, V. (2016). A framework for computational thinking based on a systematic research review.
Kalelioğlu, F. (2015). A new way of teaching programming skills to K-12 students: Code.org. Computers in Human Behavior, 52, 200–210. https://doi.org/10.1016/j.chb.2015.05.047.
Kim, B., Kim, T., & Kim, J. (2014). Paper-and-Pencil Programming Strategy toward Computational thinking for non-majors: Design your solution. Journal of Educational Computing Research, 49(4), 437–459. https://doi.org/10.2190/EC.49.4.b.
Kolb, D. A., Boyatzis, R. E., & Mainemelis, C. (2014). Experiential learning theory: Previous research and new directions. Perspectives on thinking, learning, and cognitive styles (pp. 227–248). England: Routledge.
Kong, S. C. (2016). A framework of curriculum design for computational thinking development in K-12 education. Journal of Computers in Education, 3(4), 377–394. https://doi.org/10.1007/s40692-016-0076-z.
Kong, S. C., & Wang, Y. Q. (2020). Formation of computational identity through computational thinking perspectives development in programming learning: A mediation analysis among primary school students. Computers in Human Behavior. https://doi.org/10.1016/j.chb.2019.106230
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. https://doi.org/10.1016/j.compedu.2018.08.026.
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. https://doi.org/10.1016/j.chb.2017.01.005.
Li, F., Wang, X., He, X., Cheng, L., & Wang, Y. (2022). The effectiveness of unplugged activities and programming exercises in computational thinking education: A Meta-analysis. Education and Information Technologies, 27(6), 7993–8013. https://doi.org/10.1007/s10639-022-10915-x.
Liao, Y. K. C., & Bright, G. W. (1991). Effects of computer programming on cognitive outcomes: A meta-analysis. Journal of Educational Computing Research, 7(3), 251–268.
Liu, Y. C., Huang, T. H., & Sung, C. L. (2021). The determinants of impact of personal traits on computational thinking with programming instruction. Interactive Learning Environments. https://doi.org/10.1080/10494820.2021.1983610
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.
Mason, S. L., & Rich, P. J. (2020). Development and analysis of the elementary student coding attitudes survey. Computers & Education. https://doi.org/10.1016/j.compedu.2020.103898
Ministry of Education (2022). Compulsory Information Technology Curriculum Standards http://www.gov.cn/zhengce/zhengceku/2022-04/21/content_5686535.htm.
Ministry of Education (2018). Education Informatization 2.0 Action Plan. http://www.moe.gov.cn/srcsite/A16/s3342/201804/t20180425_334188.html.
Moreno-León, J., Robles, G., & Román-González, M. (2015). Dr. Scratch: Automatic analysis of scratch projects to assess and foster computational thinking. RED Revista de Educación a Distancia, 46, 1–23.
Mouza, C., Pan, Y. C., Yang, H., & Pollock, L. (2020). A multiyear investigation of Student Computational thinking concepts, practices, and perspectives in an after-School Computing Program. Journal of Educational Computing Research, 58(5), 1029–1056. https://doi.org/10.1177/0735633120905605.
Ouahbi, I., Kaddari, F., Darhmaoui, H., Elachqar, A., & Lahmine, S. (2015). Learning basic programming concepts by creating games with scratch programming environment. Procedia-Social and Behavioral Sciences, 191, 1479–1482. https://doi.org/10.1016/j.sbspro.2015.04.224.
Papert, S. A. (1980). Mindstorms: Children, computers, and powerful ideas. New York: Basic books.
Passey, D. (2017). Computer science (CS) in the compulsory education curriculum: Implications for future research. Education and Information Technologies, 22, 421–443. https://doi.org/10.1007/s10639-016-9475-z.
Piaget, J., & Cook, M. (1952). The origins of intelligence in children (Vol. 8). United States: International Universities Press.
Polat, E., & Yilmaz, R. M. (2022). Unplugged versus plugged-in: Examining basic programming achievement and computational thinking of 6th-grade students. Education and Information Technologies, 27(7), 9145–9179. https://doi.org/10.1007/s10639-022-10992-y.
Sáez-López, J. M., Román-González, M., & Vázquez-Cano, E. (2016). Visual programming languages integrated across the curriculum in elementary school: A two year case study using scratch in five schools. Computers & Education, 97, 129–141. https://doi.org/10.1016/j.compedu.2016.03.003.
Saxena, A., Lo, C. K., Hew, K. F., & Wong, G. K. W. (2019). Designing Unplugged and plugged activities to cultivate computational thinking: An exploratory study in early Childhood Education. The Asia-Pacific Education Researcher, 29(1), 55–66. https://doi.org/10.1007/s40299-019-00478-w.
Schiefele, U. (2008). Lernmotivation und Interesse. Handbuch Der pädagogischen Psychologie, 10, 38–49.
Selby, C., & Woollard, J. (2013). Computational thinking: the developing definition.
Shang, X., Jiang, Z., Chiang, F. K., Zhang, Y., & Zhu, D. (2023). Effects of robotics STEM camps on rural elementary students’ self-efficacy and computational thinking. Educational Technology Research and Development. https://doi.org/10.1007/s11423-023-10191-7.
Sigayret, K., Tricot, A., & Blanc, N. (2022). Unplugged or plugged-in programming learning: A comparative experimental study. Computers & Education, 184, 104505.
Sun, L., Hu, L., & Zhou, D. (2021aa). Improving 7th-graders’ computational thinking skills through unplugged programming activities: A study on the influence of multiple factors. Thinking Skills and Creativity. https://doi.org/10.1016/j.tsc.2021.100926
Sun, L., Hu, L., & Zhou, D. (2021b). Single or combined? A study on programming to promote Junior High School Students’ computational thinking skills. Journal of Educational Computing Research, 60(2), 283–321. https://doi.org/10.1177/07356331211035182.
Sun, L., Hu, L., & Zhou, D. (2022aa). The bidirectional predictions between primary school students’ STEM and language academic achievements and computational thinking: The moderating role of gender. Thinking Skills and Creativity. https://doi.org/10.1016/j.tsc.2022.101043
Sun, L., Hu, L., & Zhou, D. (2022b). Programming attitudes predict computational thinking: Analysis of differences in gender and programming experience. Computers & Education. https://doi.org/10.1016/j.compedu.2022.104457
Tsai, C. Y. (2019). Improving students’ understanding of basic programming concepts through visual programming language: The role of self-efficacy [Article]. Computers in Human Behavior, 95, 224–232. https://doi.org/10.1016/j.chb.2018.11.038.
Tsai, M. J., Wang, C. Y., & Hsu, P. F. (2018). Developing the Computer Programming Self-Efficacy Scale for Computer Literacy Education. Journal of Educational Computing Research, 56(8), 1345–1360. https://doi.org/10.1177/0735633117746747.
Tsai, M. J., Liang, J. C., & Hsu, C. Y. (2020). The computational thinking scale for Computer Literacy Education. Journal of Educational Computing Research, 59(4), 579–602. https://doi.org/10.1177/0735633120972356.
Tsarava, K., Moeller, K., Pinkwart, N., Butz, M., Trautwein, U., & Ninaus, M. (2017). Training computational thinking: Game-based unplugged and plugged-in activities in primary school. European conference on games based learning (pp. 687–695).
Usher, E. L., & Pajares, F. (2008). Sources of self-efficacy in school: Critical review of the literature and future directions. Review of Educational Research, 78(4), 751–796. https://doi.org/10.3102/0034654308321456.
Webb, M., Davis, N., Bell, T., Katz, Y. J., Reynolds, N., Chambers, D. P., & Sysło, M. M. (2017). Computer science in K-12 school curricula of the 2lst century: Why, what and when? Education and Information Technologies, 22, 445–468. https://doi.org/10.1007/s10639-016-9493-x.
Weber, K., Martin, M. M., & Cayanus, J. L. (2005). Student interest: A two-study re-examination of the concept. Communication Quarterly, 53(1), 71–86. https://doi.org/10.1080/01463370500055996.
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 A: Mathematical Physical and Engineering Sciences, 366(1881), 3717–3725. https://doi.org/10.1098/rsta.2008.0118.
Wong, G. K. W., & Cheung, H. Y. (2020). Exploring children’s perceptions of developing twenty-first century skills through computational thinking and programming. Interactive Learning Environments, 28(4), 438–450. https://doi.org/10.1080/10494820.2018.1534245.
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.
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Sun, L., Liu, J. Different programming approaches on primary students’ computational thinking: a multifactorial chain mediation effect. Education Tech Research Dev 72, 557–584 (2024). https://doi.org/10.1007/s11423-023-10312-2
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DOI: https://doi.org/10.1007/s11423-023-10312-2