近年來各國開始積極推動電腦科學教育與運算思維能力，且隨著日常社會中科技的發展與普及，科技融入教學是近年來重要的教學模式之一，而當中的教育機器人做為一個跨多學科的教育整合工具更是近年來的研究趨勢。本研究旨在探討機器人作為國小階段教學工具融入課程中對於國小學生學習運算思維之影響，以系統性文獻回顧之方式，蒐集Scopus資料庫中2017至2022年近五年間的相關文章，共計16篇文獻並加以分析歸納，回答以下研究問題： (1) 在國小電腦科學學習中使用何種機器人進行教學？ (2) 在國小教育中如何將機器人作為教學工具融入電腦科學教學中？ (3) 國小學生對於機器人融入電腦科學課程中學習到那些運算思維能力？其學習成效為何？ 研究結果發現，國小階段學習運算思維之機器人，多使用不需組裝的機器人，並以視覺化程式設計環境與之搭配，而在機器人活動的教學策略上使用合作學習並搭配問題導向式學習。而國小學生在經歷機器人教學活動後，在學習運算思維方面呈現增長，顯示在國小階段以機器人作為教學工具進行運算思維之學習是有成效的。最後本研究根據研究結果提出相關建議，期望此文獻回顧給予研究人員以及教育工作者相應的支持與一個參考方向。

In recent years, many countries begin to promote computer science education and computational thinking. With the development of technology in our daily life, information technology integrated into instruction has become one of the important teaching modes in recent years. Educational robots, as an interdisciplinary integration tool, become a research trend in recent years. The purpose of this study was to explore the impact of robots as a teaching tool for computational thinking in elementary schools. This paper collected relevant articles in Scopus database in the past five years from 2017 to 2022. A total of 16 literatures were collected and analyzed to answer the following research questions: (1) What kind of robots were used for computer science learning in elementary schools? (2) How the robots as a teaching tool were used for computer science teaching in elementary education? (3) What kinds of computational thinking abilities did elementary school students learn from robotics learning for computer science? And what were their learning outcomes? The results of the study showed that teachers mostly used assembled robots with a visual programming environment as a teaching tool. In addition, cooperative learning along with problem-based learning was the most used teaching strategy in robotics learning activities. After experiencing robotics learning activities, elementary school students have shown an improvement in computational thinking performance. It shows that robotics is an effective teaching tool for enhancing students’ computational thinking performance in elementary schools. According to the research findings, some future research and teaching practice suggestions have been provided for computer science educators and researchers in computational thinking.

朱耀明(2004)。科技教育與教育科技之關係。生活科技教育月刊，37(6)，2-8。
國家教育研究院(2020)。國民小學科技教育及資訊教育課程發展參考說明。
國家教育研究院樂詞網(無日期)。邏輯思維名詞查詢。取自https://terms.naer.edu.tw/detail/ed8d5bcb223e2dbd3db2b58e35830ce5/?startswith=zh
教育部(2018)。十二年國民基本教育課程綱要國民中學暨普通型高級中等學校——科技領域。臺北市：教育部。
許宜婷(2015)。科技教育教學內容之探討。科技與人力教育季刊，2(2)，16-29。
陶淑瑗、莊宗嚴(2015)。數位科技應用於小學低年級學童數學學習之反思。數位學習科技期刊，7(2)，53-71。
臺北市政府教育局(2018)。臺北市科技領域國小資訊科技課程教學綱要。臺北市：臺北市政府教育局。
蔡浩軒、孟瑛如(2020)。擴增實境(AR)之比與比值數學教材對國小六年級學習障礙學生學習及課堂注意力成效提升之探討。特殊教育學報，51，65-99。
簡瑋成(2019)。美國科技教育之發展趨勢。國家教育研究院電子報，183。
Ackermann, E. (2001). Piaget’s constructivism, Papert’s constructionism: What’s the difference. Future of learning group publication, 5(3), 438.
Afari, E., & Khine, M. S. (2017). Robotics as an educational tool: impact of lego mindstorms. International Journal of Information and Education Technology, 7(6), 437-442. https://doi.org/10.18178/ijiet.2017.7.6.908
Ak, O., & Kutlu, B. (2017). Comparing 2D and 3D game‐based learning environments in terms of learning gains and student perceptions. British Journal of Educational Technology, 48(1), 129-144. https://doi.org/10.1111/bjet.12346
Alimisis, D. (2013). Educational robotics: Open questions and new challenges. Themes in Science & Technology Education, 6(1), 63-71.
Angeli, C., & Valanides, N. (2020). Developing young children's computational thinking with educational robotics: An interaction effect between gender and scaffolding strategy. Computers in Human Behavior, 105, 105954. https://doi.org/10.1016/j.chb.2019.03.018
Aristawati, F. A., Budiyanto, C., & Yuana, R. A. (2018). Adopting Educational Robotics to Enhance Undergraduate Students’ Self-Efficacy Levels of Computational Thinking. Journal of Turkish Science Education, 15(Special), 42-50.
Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191–215. https://doi.org/10.1037/0033-295X.84.2.191
Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice-Hall.
Bandura, A. (1991). Social cognitive theory of self-regulation. Organizational behavior and human decision processes, 50(2), 248-287.
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
BBC Bitesize (n.d.). Introduction to computational thinking. Retrieved February 12, 2022, from https://www.bbc.co.uk/bitesize/guides/zp92mp3/revision/1
Benitti, F. B. V. (2012). Exploring the educational potential of robotics in schools: A systematic review. Computers & Education, 58(3), 978-988. https://doi.org/10.1016/j.compedu.2011.10.006
Bers, M. U. (2018). Coding and computational thinking in early childhood: The impact of ScratchJr in Europe. European Journal of STEM Education, 3(3), 8. https://doi.org/10.20897/ejsteme/3868
Bers, M. U., Flannery, L., Kazakoff, E. R., & 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.S. (Ed.). (1956). Taxonomy of educational objectives: The classification of educational goals. Handbook 1: Cognitive domain. New York : David McKay.
Blumenfeld, P. C., Soloway, E., Marx, R. W., Krajcik, J. S., Guzdial, M., & Palincsar, A. (1991). Motivating project-based learning: Sustaining the doing, supporting the learning. Educational psychologist, 26(3-4), 369-398. https://doi.org/10.1080/00461520.1991.9653139
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, 1, 25.
Cai, S., Liu, E., Yang, Y., & Liang, J. C. (2019). Tablet‐based AR technology: Impacts on students’ conceptions and approaches to learning mathematics according to their self‐efficacy. British Journal of Educational Technology, 50(1), 248-263. https://doi.org/10.1111/bjet.12718
Cervera, N., Diago, P. D., Orcos, L., & Yáñez, D. F. (2020). The acquisition of computational thinking through mentoring: An exploratory study. Education Sciences, 10(8), 202. https://doi.org/10.3390/educsci10080202
Chalmers, C. (2018). Robotics and computational thinking in primary school. International Journal of Child-Computer Interaction, 17, 93-100. https://doi.org/10.1016/j.ijcci.2018.06.005
Chambers, J. M., Carbonaro, M., Rex, M., & Grove, S. (2007). Scaffolding knowledge construction through robotic technology: A middle school case study. Electronic Journal for the Integration of Technology in Education, 6, 55-70.
Chen, G., Shen, J., Barth-Cohen, L., Jiang, S., Huang, X., & Eltoukhy, M. (2017). Assessing elementary students’ computational thinking in everyday reasoning and robotics programming. Computers & education, 109, 162-175. https://doi.org/10.1016/j.compedu.2017.03.001
Cheng, Y. W., Sun, P. C., & Chen, N. S. (2018). The essential applications of educational robot: Requirement analysis from the perspectives of experts, researchers and instructors. Computers & education, 126, 399-416.
Chevalier M., Giang C., El-Hamamsy L., Bonnet E., Papaspyros V., Pellet J.-P., Audrin C., Romero M., Baumberger B., & Mondada F. (2022). The role of feedback and guidance as intervention methods to foster computational thinking in educational robotics learning activities for primary school. Computers & Education, 180, 104431. https://doi.org/10.1016/j.compedu.2022.104431
Chevalier, M., Giang, C., Piatti, A., & Mondada, F. (2020). Fostering computational thinking through educational robotics: A model for creative computational problem solving. International Journal of STEM Education, 7(1), 1-18.
Chiazzese, G., Arrigo, M., Chifari, A., Lonati, V., & Tosto, C. (2019). Educational robotics in primary school: Measuring the development of computational thinking skills with the bebras tasks. Informatics, 6(4), 43.
Computer Science Teachers Association (2017). CSTA K-12 Computer Science Standards, Revised 2017. Retrieved from http://www.csteachers.org/standards.
Cooper, S., Dann, W., & Pausch, R. (2000). Alice: A 3-D tool for introductory programming concepts. Journal of computing sciences in colleges, 15(5), 107-116.
Council, D. P. (2006). American competitiveness initiative. Office of Science and Technology Policy. Retrieved April, 16, 2007.
Denis, B., & Hubert, S. (2001). Collaborative learning in an educational robotics environment. Computers in human behavior, 17(5-6), 465-480.
Eady, M., & Lockyer, L. (2013). Tools for learning: Technology and teaching strategies. Learning to teach in the primary school, 71.
Erol, O. (2020). How do students' attitudes towards programming and self-efficacy in programming change in the robotic programming process? International Journal of Progressive Education, 16(4), 13-26. https://doi.org/10.29329/ijpe.2020.268.2
Evripidou, S., Georgiou, K., Doitsidis, L., Amanatiadis, A. A., Zinonos, Z., & Chatzichristofis, S. A. (2020). Educational Robotics: Platforms, Competitions and Expected Learning Outcomes. IEEE Access, 8, 219534-219562.
Gergen, K. J. (1985). Social constructionist inquiry: Context and implications. In The social construction of the person (pp. 3-18). Springer, New York, NY.
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/10.3102/0013189X1246305
Hammond, J., & Gibbons, P. (2005). What is scaffolding. Teachers’ voices, 8, 8-16.
Harel, I. E., & Papert, S. E. (1991). Constructionism. Ablex Publishing.
Hazzan, O., Lapidot, T., & Ragonis, N. (2014). Guide to teaching computer science. London: Springer. https://doi.org/10.1007/978-3-030-39360-1
ISTE (2022). ISTE Computational Thinking Competencies. Retrieved September 12, 2022, from https://www.iste.org/standards/iste-standards-for-computational-thinking
Jethro, O. O., Grace, A. M., & Thomas, A. K. (2012). E-learning and its effects on teaching and learning in a global age. International Journal of Academic Research in Business and Social Sciences, 2(1), 203.
K-12 Computer Science Framework Steering Committee. (2016). K-12 computer science framework. ACM. https://doi.org/10.1145/3079760
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.
Kátai, 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/10.1111/jcal.12070
Kokotsaki, D., Menzies, V., & Wiggins, A. (2016). Project-based learning: A review of the literature. Improving schools, 19(3), 267-277. https://doi.org/10.1177/1365480216659733
Krippendorff, K. (1989). Content analysis. International encyclopedia of communication (Vol. 1, pp. 403-407). New York, NY: Oxford University Press.
Laal, M., & Ghodsi, S. M. (2012). Benefits of collaborative learning. Procedia-social and behavioral sciences, 31, 486-490. https://doi.org/10.1016/j.sbspro.2011.12.091
Lee, D., Watson, S. L., & Watson, W. R. (2020). The relationships between self-efficacy, task value, and self-regulated learning strategies in massive open online courses. The International Review of Research in Open and Distributed Learning, 21(1), 23-39. https://doi.org/10.19173/irrodl.v20i5.4389
Luo, F., Antonenko, P. D., & Davis, E. C. (2020). Exploring the evolution of two girls’ conceptions and practices in computational thinking in science. Computers & Education, 146, 103759. https://doi.org/10.1016/j.compedu.2019.103759
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. https://doi.org/10.1016/j.chb.2014.09.012
Maloney, J., Resnick, M., Rusk, N., Silverman, B., & Eastmond, E. (2010). The scratch programming language and environment. ACM Transactions on Computing Education (TOCE), 10(4), 1-15. https://doi.org/10.1145/1868358.1868363
Massachusetts Department of Elementary and Secondary Education. (2019). 2016 Massachusetts digital literacy and computer science (DLCS) curriculum framework. Malden, MA.
Maybin, J., Mercer, N., & Stierer, B. (1992). Scaffolding learning in the classroom. Thinking voices: The work of the national oracy project, London: Hodder and Stoughton.
Mubin, O., Stevens, C. J., Shahid, S., Al Mahmud, A., & Dong, J. J. (2013). A review of the applicability of robots in education. Technology in Education and Learning, 1, 13.
Noh, J., & Lee, J. (2020). Effects of robotics programming on the computational thinking and creativity of elementary school students. Educational technology research and development, 68(1), 463-484. https://doi.org/10.1007/s11423-019-09708-w
Panskyi T., & Rowińska Z. (2021). A holistic digital game-based learning approach to out-of-school primary programming education. Informatics in Education, 20(2), 255-276. https://doi.org/10.15388/infedu.2021.12
Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. New York: Basic Books.
Phetsrikran T., Harfield A., Charoensiriwath S., & Massagram W. (2021). Arducation bot: Computational thinking courseware with ios mobile application and educational robotics. ICIC Express Letters, Part B: Applications, 12(1), 27-34. https://doi.org/10.24507/icicelb.12.01.27
Piaget, J., Inhelder, B. (1969). The psychology of the child. London：Routledge & Kegan Paul.
Pivec, M., Dziabenko, O., & Schinnerl, I. (2003). Aspects of game-based learning. Proceeding of the 3rd International Conference on Knowledge Management, Graz, Austria.
Prensky, M. (2003). Digital game-based learning. Computers in Entertainment, 1(1), 21. https://doi.org/10.1145/950566.950596
Qian, M., & Clark, K. R. (2016). Game-based learning and 21st century skills: A review of recent research. Computers in human behavior, 63, 50-58. https://doi.org/10.1016/j.chb.2016.05.023
Qu, J. R., & Fok, P. K. (2021). Cultivating students’ computational thinking through student–robot interactions in robotics education. International Journal of Technology and Design Education, 32(4), 1983-2002. https://doi.org/10.1007/s10798-021-09677-3
Sáez López, J. M., Buceta Otero, R., & Lara García-Cervigón, S. D. (2021). Introducing robotics and block programming in elementary education. RIED. Revista Iberoamericana de Educación a Distancia, 24(1), 95-113. http://dx.doi.org/10.5944/ried.24.1.27649
Sáez-López, J. M., Sevillano-García, M. L., & Vazquez-Cano, E. (2019). The effect of programming on primary school students’ mathematical and scientific understanding: educational use of mBot. Educational Technology Research and Development, 67(6), 1405-1425. https://doi.org/10.1007/s11423-019-09648-5
Saidin, N.D., Khalid, F., Martin, R., Kuppusamy, Y., & Munusamy, N.A. (2021). Benefits and challenges of applying computational thinking in education. International Journal of Information and Education Technology, 11(5), 248-254.
Seehorn, D., Carey, S., Fuschetto, B., Lee, I., Moix, D., O'Grady-Cunniff, D., Owens, B.B., Stephenson, C. & Verno, A. (2011). CSTA K-12 Computer Science Standards: Revised 2011. ACM.
Selby, C. C., & Woollard, J. (2013). Computational thinking: the developing definition. Southampton, UK: University of Southampton. Retrieved from https://eprints.soton.ac.uk/id/eprint/356481.
Shen, J., Chen, G., Barth-Cohen, L., Jiang, S., & Eltoukhy, M. (2022). Connecting computational thinking in everyday reasoning and programming for elementary school students. Journal of Research on Technology in Education, 54(2), 205-225. https://doi.org/10.1080/15391523.2020.1834474
Smith, B. L. & MacGregor, J. T. (1992). What is collaborative learning?. In Goodsell , A., Maher, M., Tinto, V., Smith, B. L. & MacGregor J. T. (Eds.), Collaborative Learning: A Sourcebook for Higher Education. Pennsylvania State University; USA, National center on postsecondary teaching, learning, and assessment.
Spolaôr, N., & Benitti, F. B. V. (2017). Robotics applications grounded in learning theories on tertiary education: A systematic review. Computers & Education, 112, 97-107. https://doi.org/10.1016/j.compedu.2017.05.001
Stemler, S. (2000). An overview of content analysis. Practical assessment, research, and evaluation, 7(1), 17.
Stewart, W. H., Baek, Y., Kwid, G., & Taylor, K. (2021). Exploring factors that influence computational thinking skills in elementary students’ collaborative robotics. Journal of Educational Computing Research, 59(6), 1208-1239. https://doi.org/10.1177/0735633121992479
Sullivan, A., & Bers, M. U. (2016). Robotics in the early childhood classroom: learning outcomes from an 8-week robotics curriculum in pre-kindergarten through second grade. International Journal of Technology and Design Education, 26(1), 3-20.
Tengler, K., Kastner-Hauler, O., Sabitzer, B., & Lavicza, Z. (2021). The effect of robotics-based storytelling activities on primary school students’ computational thinking. Education Sciences, 12(1), 10. https://doi.org/10.3390/educsci12010010
Tsai, M. J., Liang, J. C., & Hsu, C. Y. (2021a). The computational thinking scale (CTS) for computer literacy education. Journal of Educational Computing Research, 59(4), 579-602. https://doi.org/10.1177/0735633120972356
Tsai, M. J., Wang, C. Y., Wu, A. H., & Hsiao, C. Y. (2021b). The development and validation of the robotics learning self-efficacy scale (RLSES). Journal of Educational Computing Research, 59(6), 1056-1074. https://doi.org/10.1177/0735633121992594
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/10.1177/07356331211017794
Valencia-Vallejo, N., López-Vargas, O., & Sanabria-Rodríguez, L. (2018). Effect of Motivational Scaffolding on E-Learning Environments: Self-Efficacy, Learning Achievement, and Cognitive Style. Journal of educators online, 15(1), n1. https://doi.org/10.9743/JEO2018.15.1.5
Vygotsky, L. S., & Cole, M. (1978). Mind in society: Development of higher psychological processes. Harvard university press.
Wing, J. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35. https://doi.org/10.1145/1118178.1118215
Wing, J. (2011). Research notebook: Computational thinking—What and why. The link magazine, 6, 20-23.
Wing, J. (2017). Computational thinking’s influence on research and education for all. Italian Journal of Educational Technology, 25(2), 7-14. https://doi.org/10.17471/2499-4324/922
Wood, D. F. (2003). Problem based learning. Bmj, 326(7384), 328-330. https://doi.org/10.1136/bmj.326.7384.328
Wu, S. Y., & Su, Y. S. (2021). Visual programming environments and computational thinking performance of fifth-and sixth-grade students. Journal of Educational Computing Research, 59(6), 1075-1092. https://doi.org/10.1177/0735633120988807
Yokoyama, S. (2019). Academic self-efficacy and academic performance in online learning: A mini review. Frontiers in psychology, 9, 2794. https://doi.org/10.3389/fpsyg.2018.02794
Zimmerman, B. J. (1989). A Social Cognitive View of Self-Regulated Academic Learning. Journal of Educational Psychology, 81, 329-339. https://doi.org/10.1037/0022-0663.81.3.329
Zimmerman, B. J., & Campillo, M. (2003). Motivating self-regulated problem solvers. In J. Davidson & R. Sternberg (Eds.), The psychology of problem solving, 233-262. Cambridge: Cambridge University Press. https://doi.org/10.1017/CBO9780511615771.009