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International Journal of Robotics and Automation Technology

Articles

Vol. 12 (2025)

Investigating How Robotics Activities Shape Elementary Students’ Attitudes Toward Music Composition and Coding

DOI
https://doi.org/10.31875/2409-9694.2025.12.02
Submitted
August 12, 2025
Published
17.08.2025

Abstract

This mixed methods sequential explanatory study explored how computational thinking influences elementary students' attitudes by engaging the students in coding a music composition with an autonomous robot. Pre and post tests were used to document students’ attitudes toward music composition and coding over the course of six weeks. Eighty fifth-grade students participated in the music composition project, coding for one hour each week in an engineering class. The students were randomly organized into four study groups: individualized, collaborative, traditional, and Use-Modify-Create (UMC). Findings indicate that students experienced a significant increase in positive attitudes toward music composition after the robotics coding activity. Both the individualized and collaborative groups reported enhanced enthusiasm for music composition, while the UMC group showed increased positivity towards coding and greater confidence in their coding abilities. These results suggest that music educators can enhance student attitudes toward both computational thinking and music composition by integrating robotics into the music curriculum. The main contributions of this study include: (1) empirical evidence of positive attitude changes toward music and coding through robotics; (2) comparative analysis of four instructional modes; and (3) a practical framework for integrating robotics into elementary music education.

References

  1. Adu, P. (2019). A step-by-step guide to qualitative data coding (1st ed.). Routledge. https://doi.org/10.4324/9781351044516-1
  2. Amri, S., Budiyanto, C. W., Fenyvesi, K., Yuana, R. A., & Widiastuti, I. (2022). Educational robotics: Evaluating the role of computational thinking in attaining 21st century skills. Open Education Studies, 4(1), 322-338. https://doi.org/10.1515/edu-2022-0174
  3. Atmatzidou, S., Demetriadis, S., & Nika, P. (2018). How does the degree of guidance support students’ metacognitive and problem-solving skills in educational robotics? Journal of Science Education and Technology, 27, 70-85. https://doi.org/10.1007/s10956-017-9709-x
  4. Baek, Y., & Taylor, K. (2020). Not just composing, but programming music in group robotics. Music Education Research, 22(3), 315-330. https://doi.org/10.1080/14613808.2020.1767558
  5. Belbase, S., Mainali, B. R., Kasemsukpipat, W., Tairab, H., Gochoo, M., & Jarrah, A. (2021). At the dawn of science, technology, engineering, arts, and mathematics (STEAM) education: Prospects, priorities, processes, and problems. International Journal of Mathematical Education in Science and Technology, 1-37. https://doi.org/10.1080/0020739X.2021.1922943
  6. Bell, J., & Bell, T. (2018). Integrating computational thinking with a music education context. Informatics in Education, 17(2), 151-166. https://doi.org/10.15388/infedu.2018.09
  7. Brown, A. R., & Sorensen, A. (2009). Interacting with Generative Music through Live Coding. Contemporary Music Review, 28(1), 17-29. https://doi.org/10.1080/07494460802663991
  8. Chung, C. J. C. J., Cartwright, C., & Chung, C. (2014). Robot music camp 2013: An experiment to promote STEM and computer science. 2014 IEEE Integrated STEM Education Conference. https://doi.org/10.1109/ISECon.2014.6891012
  9. Clarke, V., & Braun, V. (2016). Thematic analysis. The Journal of Positive Psychology, 12, 297-298. https://doi.org/10.1080/17439760.2016.1262613
  10. Creswell, J. W. (2009). Research design: Qualitative, quantitative, and mixed methods approach (3rd Ed.). Sage.
  11. Durak, H. Y., Yilmaz, F. G. K., & Yilmaz, R. (2019). Computational thinking, programming self-efficacy, problem solving and experiences in the programming process conducted with robotic activities. Contemporary Educational Technology, 10(2), 173-197. https://doi.org/10.30935/cet.554493
  12. Eguchi, A. (2012). Educational robotics theories and practice. In B. Barker, G. Nugent, N. Grandgenett, & V. Adamchuk (Eds.), Robots in K-12 education: A new technology for learning (pp. 1-30). IGI Global.
  13. Elkin, M., Sullivan, A., & Bers, M. U. (2016). Programming with the KIBO robotics kit in preschool classrooms. Computers in the Schools, 33, 169-186. https://doi.org/10.1080/07380569.2016.1216251
  14. Engelman, S., Magerko, B., McKlin, T., Miller, M., Edwards, D., & Freeman, J. (2017). Creativity in authentic STEAM education with EarSketch. Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education. https://doi.org/10.1145/3017680.3017763
  15. 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
  16. Fanchamps, N., Van Gool, E., Folkertsma, A., & De Meyst, K. (2024). The influence of music producing and creativity on computational thinking in primary school children. Education Sciences, 14(12), 1380. https://doi.org/10.3390/educsci14121380
  17. Gorson, J., Patel, N., Beheshti, E., Magerko, B., & Horn, M. (2017). TunePad. Proceedings of the 2017 Conference on Interaction Design and Children. https://doi.org/10.1145/3078072.3084313
  18. Grandgenett, N. F., Ostler, C., Topp, N., & Goeman, R. (2012). Robotics and problem-based learning in STEM formal educational environments. In B. Barker, G. Nugent, N. F. Grandgenett, & S. Adamchuk (Eds.), Robots in K-12 education: A new technology for learning (pp. 94-119). IGI Global. https://doi.org/10.4018/978-1-4666-0182-6.ch005
  19. Hollstein, B. (2014). Qualitative approaches. In J. Scott, & P. J. Carrington (Eds.), The SAGE handbook of social network analysis (pp. 404-416). Sage. https://doi.org/10.4135/9781446294413.n27
  20. Hwang, W.Y., & Wu, S.Y. (2014). A case study of collaboration with multi-robots and its effect on children’s interaction. Interactive Learning Environments, 22, 429-443. https://doi.org/10.1080/10494820.2012.680968
  21. Kafol, B. S., Denac, O., Žnidaršič, J., & Zalar, K. (2015). Analysis of music education objectives in learning domains. Procedia - Social and Behavioral Sciences, 186, 95-104. https://doi.org/10.1016/j.sbspro.2015.04.069
  22. Karim, M. E., Lemaignan, S., & Mondada, F. 2015. A review: Can robots reshape K-12 STEM education? Proceedings in IEEE International Workshop on Advanced Robotics and Its Social Impacts (ARSO) (pp. 1-8). Lyon, France. June 30-July 2, 2015. https://doi.org/10.1109/ARSO.2015.7428217
  23. Kernighan, B. W., & Ritchie, D. M. (1978). The C programming language. https://usuaris.tinet.cat/ bertolin/pdfs/c_programming_language.pdf
  24. Khine, M. S. (2018). Robotics in STEM education: Redesigning the learning experience. Springer.
  25. Komm, D., Regez, A., Hauser, U., Gassner, M., Lütscher, P., Puchegger, R., & Kohn, T. (2020). Problem solving and creativity: Complementing programming education with robotics. Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education. https://doi.org/10.1145/3341525.3387420
  26. Ioannou, A., & Makridou, E. (2018). Exploring the potential of educational robotics in the development of computational thinking: A summary of current research and practical proposal for future work. Education and Information Technologies, 23, 2531-2544. https://doi.org/10.1007/s10639-018-9729-z
  27. Leach, J., & Fitch, J. (1995). Nature, music, and algorithmic composition. Computer Music Journal, 19(2), 23-33. https://doi.org/10.2307/3680598
  28. Liu, Z., Zhang, S., Israel, M., Smith, R., Xing, W., & Minces, V. (2025, February 26-March 1). Engaging K-12 students with flow-based music programming: An experience report on its impact on teaching and learning. In Proceedings of the 56th ACM Technical Symposium on Computer Science Education V. 1 (SIGCSE TS 2025) (pp. 708-714). ACM. https://doi.org/10.1145/3641554.3701902
  29. Magnusson, T. (2011). Algorithms as scores: Coding live music. Leonardo Music Journal, 21, 19-23. https://doi.org/10.1162/LMJ_a_00056
  30. Mason, S. L. ,& Rich, P. J. (2020). Development and analysis of the Elementary Student Coding Attitudes Survey. Computers & Education, 153. https://doi.org/10.1016/j.compedu.2020.103898
  31. Misra, A., Blank, D., & Kumar, D. (2009). A music context for teaching introductory computing. Proceedings of the 14th Annual ACM SIGCSE Conference on Innovation and Technology in Computer Science Education - ITiCSE ’09. https://doi.org/10.1145/1562877.1562955
  32. Ng, A., Kewalramani, S., & Kidman, G. (2022). Integrating and navigating STEAM (inSTEAM) in early childhood education: An integrative review and inSTEAM conceptual framework. Eurasia Journal of Mathematics Science and Technology Education, 18(7), em2133. https://doi.org/10.29333/ejmste/12174
  33. Noh, J., & Lee, J. (2019). Effects of robotics programming on the computational thinking and creativity of elementary school students. Educational Technology Research and Development, 68, 463-484. https://doi.org/10.1007/s11423-019-09708-w
  34. Nooraie, R. Y., Sale, J. E. M., Marin, A., & Ross, L. E. (2020). Social network analysis: An example of fusion between quantitative and qualitative methods. Journal of Mixed Methods Research, 14, 110-124. https://doi.org/10.1177/1558689818804060
  35. Pandey, P., Jamshidi, F., & Marghitu, D. (2023). Introducing Computer Science and Arts for All (CSA4ALL): Developing an inclusive curriculum and portal for K5 children. In Lecture notes in computer science (pp. 326-341). https://doi.org/10.1007/978-3-031-35897-5_24
  36. Papadakis, S., & Kalogiannakis, M. (2020). Exploring preservice teachers' attitudes about the usage of educational robotics in preschool education. In Handbook of research on tools for teaching computational thinking in P-12 Education (pp. 339-355). IGI Global. https://doi.org/10.4018/978-1-7998-4576-8.ch013
  37. Pignato, J. M. (2017). Situating technology within and without music education. The Oxford handbook of technology and music education, 203. https://doi.org/10.1093/oxfordhb/9780199372133.013.19
  38. Shen, J., Chen, G., Barth-Cohen, L., Jiang, S., & Eltoukhy, M. (2020b). 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
  39. Sonoyama, Y., & Nakajima, T. (2023). Investigation of Usefulness of “PMusic”: A Live Coding Language Using Probability Music. IEEE. https://doi.org/10.1109/GCCE59613.2023.10315462
  40. Sullivan, A., Strawhacker, A., & Bers, M. U. (2017). Dancing, drawing, and dramatic robots: Integrating robotics and the arts to teach foundational STEAM concepts to young children. In M. S. Khine (Ed.), Robotics in STEM education: Redesigning the learning experience (pp. 231-260). Springer. https://doi.org/10.1007/978-3-319-57786-9_10
  41. United States Patent Application. (2024). Automated music composition and generation system (U.S. Patent Application No. US 2024/0371347 A1). United States Patent and Trademark Office. https://patents.google.com/patent/US20240371347A1/en
  42. Zhang, Y., Luo, R., Zhu, Y., & Yin, Y. (2021). Educational Robots Improve K-12 students’ computational thinking and STEM Attitudes: Systematic review. Journal of Educational Computing Research, 59(7), 1450-1481. https://doi.org/10.1177/0735633121994070