在資訊化時代，日常生活中幾乎每個人都在使用電腦、手機、網路等數位設備來獲取資訊、溝通及處理各種事務，這些智能產品改變了我們的生活形態，同時也為教育帶來了新的挑戰和機遇，如何引導學生活用科技產物甚至透過程式語言與電腦進行對話成為一大重要課題。此外，人們也越來越注重實際應用的能力，傳統的學科知識已經無法滿足現代社會的需求，透過實際動手操作和嘗試，不僅能將理論知識轉化為應用能力，甚至可以透過經驗的累積培養更深層次的科技素養，有助於新生代適應未來充斥著各項科技以及各種新興工作的環境。這樣的時代浪潮下，促使了本研究的發展，為了幫助學生在較枯燥且難以理解的程式設計課程中可以更無負擔的學習，我們開發了一組用於高中程式設計課程的學習輔助教具Pixel Button，旨在短時間內快速幫助學生初步了解循序、選擇、迴圈、排序等基礎程式結構的使用方式並實際運用到專案中。Pixel Button 是一種過渡性學習工具，可以讓初學者從視覺化程式設計語言轉換到以程式語言編碼進行程式設計，學生在還沒真正熟悉基礎程式結構及語法前可以先透過本模組化專題課程，搭配實體教具進行抽象觀念的轉換。課程中以程式設計、焊接電子電路、工程規劃設計等綜合應用來訓練邏輯以及運算思維，讓學生在自己組裝教具、實作互動性遊戲專題的過程中達到更完整且全面性的學習。最終透過實驗課程和問卷調查了解在程式設計課程中融入Pixel Button輔助教學對學生來說是否能提供適切的幫助並提高學生對程式設計的興趣和自信心，也針對Pixel Button的特點進行說明，除了闡述Pixel Button學習輔助工具的應用價值，也為日後設計程式教學相關的輔助教具提供了相關的參考依據，期待未來在程式教育領域可以出現更多元、更豐富的教具型態。

In the digital era, the widespread use of technology has introduced new challenges and opportunities in education. Guiding students to utilize technology and engage in programming conversations effectively is crucial. Additionally, practical application skills have become increasingly important, as theoretical knowledge alone is insufficient. By engaging in hands-on experimentation and exploration, theoretical knowledge can be transformed into practical skills, and a deeper level of technological literacy can be cultivated through accumulated experiences. This is crucial for the younger generation to adapt to a future filled with various technologies and emerging professions. In order to facilitate students' learning in seemingly tedious and challenging programming courses, this study developed a set of learning tool called "Pixel Button" specifically designed for high school programming classes. The aim is to provide a less burdensome learning experience, assisting students in quickly gaining a preliminary understanding of the usage of fundamental programming structures such as selection, loops, and sorting and applying them practically in projects within a short period. Pixel Button is a transitional learning tool that enables beginners to transition from visual programming languages to coding-based programming. Before becoming familiar with basic programming structures and syntax, students can engage in modular project-based courses using this tool, accompanied by physical learning aids, to facilitate the transformation of abstract concepts. The curriculum integrates programming, soldering electronic circuits, engineering planning and design to train logical and computational thinking. Through the process of assembling the learning tool and completing interactive game projects, students can achieve a more comprehensive and holistic learning experience. In the final phase, experimental courses and questionnaires were conducted to determine whether the integration of Pixel Button auxiliary teaching in programming courses can provide appropriate help to students and improve students' interest and self-confidence in programming. In conclusion, the impact and characteristics of Pixel Button on perceived factors were analyzed, providing a reference for designing programming learning aids. Looking forward to the future development of a more diverse range of learning tools in the field of programming education.

台灣行政院教育部. (2012). 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. Retrieved from https://www.k12ea.gov.tw/files/class_schema/%E8%AA%B2%E7%B6%B1/13-%E7%A7%91%E6%8A%80/13-1/%E5%8D%81%E4%BA%8C%E5%B9%B4%E5%9C%8B%E6%B0%91%E5%9F%BA%E6%9C%AC%E6%95%99%E8%82%B2%E8%AA%B2%E7%A8%8B%E7%B6%B1%E8%A6%81%E5%9C%8B%E6%B0%91%E4%B8%AD%E5%AD%B8%E6%9A%A8%E6%99%AE%E9%80%9A%E5%9E%8B%E9%AB%98%E7%B4%9A%E4%B8%AD%E7%AD%89%E5%AD%B8%E6%A0%A1%E2%94%80%E7%A7%91%E6%8A%80%E9%A0%98%E5%9F%9F.pdf
Alciatore, D. G., & Histand, M. B. (2007). Introduction to mechatronics and measurement systems (Vol. 3): McGraw-Hill New York.
Ali, W. (2020). Online and remote learning in higher education institutes: A necessity in light of COVID-19 pandemic. Higher education studies, 10(3), 16-25.
Austin, J., Baker, H., Ball, T., Devine, J., Finney, J., De Halleux, P., . . . Stockdale, G. (2020). The BBC micro: bit: from the UK to the world. Communications of the ACM, 63(3), 62-69.
Bandura, A. (1977). Self-efficacy: toward a unifying theory of behavioral change. Psychological review, 84(2), 191.
Barón, A. Á. B., & Gutiérrez, R. C. (2018). SNAP circuits program promoted by Santo Tomás university and the faculty of electronic engineering. Shimmering Words: Research and Pedagogy E-Journal, 8, 102-114.
Barnard-Brak, L., Paton, V. O., & Lan, W. Y. (2010). Profiles in self-regulated learning in the online learning environment. International Review of Research in Open and Distributed Learning, 11(1), 61-80.
Bdeir, A. (2009). Electronics as material: littleBits. Paper presented at the Proceedings of the 3rd International Conference on Tangible and Embedded Interaction.
Bell, C., & Bell, C. (2021). Introducing Grove. Beginning IoT Projects: Breadboard-less Electronic Projects, 481-509.
Benton, L., Hoyles, C., Kalas, I., & Noss, R. (2017). Bridging primary programming and mathematics: Some findings of design research in England. Digital Experiences in Mathematics Education, 3, 115-138.
Brown, N. C., Sentance, S., Crick, T., & Humphreys, S. (2014). Restart: The resurgence of computer science in UK schools. ACM Transactions on Computing Education (TOCE), 14(2), 1-22.
Cazzola, W., & Olivares, D. M. (2015). Gradually learning programming supported by a growable programming language. IEEE Transactions on Emerging Topics in Computing, 4(3), 404-415.
Çeven, S., & Albayrak, A. (2020). Design and implementation of modular test equipment for process measurements in mechatronics education. Computer Applications in Engineering Education, 28(2), 324-337.
Chang, C.-W., Lee, J.-H., Chao, P.-Y., Wang, C.-Y., & Chen, G.-D. (2010). Exploring the possibility of using humanoid robots as instructional tools for teaching a second language in primary school. Journal of Educational Technology & Society, 13(2), 13-24.
Ching, Y.-H., Hsu, Y.-C., & Baldwin, S. (2018). Developing computational thinking with educational technologies for young learners. TechTrends, 62, 563-573.
Chis, A. E., Moldovan, A.-N., Murphy, L., Pathak, P., & Muntean, C. H. (2018). Investigating flipped classroom and problem-based learning in a programming module for computing conversion course. Journal of Educational Technology & Society, 21(4), 232-247.
Collective, B. s. M., & Shaw, D. (2012). Makey Makey: improvising tangible and nature-based user interfaces. Paper presented at the Proceedings of the sixth international conference on tangible, embedded and embodied interaction.
Dabbagh, N., & Kitsantas, A. (2012). Personal Learning Environments, social media, and self-regulated learning: A natural formula for connecting formal and informal learning. The Internet and higher education, 15(1), 3-8.
Davis, F. D. (1993). User acceptance of information technology: system characteristics, user perceptions and behavioral impacts. International journal of man-machine studies, 38(3), 475-487.
Dewey, J. (1986). Experience and education. Paper presented at the The educational forum.
Duncan, C., & Bell, T. (2015). A pilot computer science and programming course for primary school students. Paper presented at the Proceedings of the Workshop in Primary and Secondary Computing Education.
Frank, M., Lavy, I., & Elata, D. (2003). Implementing the project-based learning approach in an academic engineering course. International Journal of Technology and Design Education, 13, 273-288.
Garris, R., Ahlers, R., & Driskell, J. E. (2002). Games, motivation, and learning: A research and practice model. Simulation & gaming, 33(4), 441-467.
Gomes, A., & Mendes, A. J. (2007). An environment to improve programming education. Paper presented at the Proceedings of the 2007 international conference on Computer systems and technologies.
Holden, R. J., & Karsh, B.-T. (2010). The technology acceptance model: its past and its future in health care. Journal of biomedical informatics, 43(1), 159-172.
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.
Huang, Y.-T., & Chen, C.-H. (2022). Design Interactive Teaching Tools of Programming Language for Senior High School Students. Advances in Human Factors in Training, Education, and Learning Sciences, 59, 48.
Hwang, W.-Y., Wang, C.-Y., Hwang, G.-J., Huang, Y.-M., & Huang, S. (2008). A web-based programming learning environment to support cognitive development. Interacting with Computers, 20(6), 524-534.
Jackson, J. W. (2002). Enhancing self-efficacy and learning performance. The journal of experimental education, 70(3), 243-254.
Körber, N., Bailey, L., Greifenstein, L., Fraser, G., Sabitzer, B., & Rottenhofer, M. (2021). An Experience of Introducing Primary School Children to Programming using Ozobots (Practical Report). Paper presented at the The 16th Workshop in Primary and Secondary Computing Education.
Kalina, C., & Powell, K. (2009). Cognitive and social constructivism: Developing tools for an effective classroom. Education, 130(2), 241-250.
Katai, Z., & Toth, L. (2010). Technologically and artistically enhanced multi-sensory computer-programming education. Teaching and teacher education, 26(2), 244-251.
Kelleher, C., & Pausch, R. (2005). Lowering the barriers to programming: A taxonomy of programming environments and languages for novice programmers. ACM Computing Surveys (CSUR), 37(2), 83-137.
King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information & management, 43(6), 740-755.
Klassner, F., & Anderson, S. D. (2003). Lego MindStorms: Not just for K-12 anymore. IEEE robotics & automation magazine, 10(2), 12-18.
Koorsse, M., Cilliers, C., & Calitz, A. (2015). Programming assistance tools to support the learning of IT programming in South African secondary schools. Computers & Education, 82, 162-178.
Lahtinen, E., Ala-Mutka, K., & Järvinen, H.-M. (2005). A study of the difficulties of novice programmers. Acm sigcse bulletin, 37(3), 14-18.
Law, K. M., Lee, V. C., & Yu, Y.-T. (2010). Learning motivation in e-learning facilitated computer programming courses. Computers & Education, 55(1), 218-228.
Liaw, S.-S. (2008). Investigating students’ perceived satisfaction, behavioral intention, and effectiveness of e-learning: A case study of the Blackboard system. Computers & Education, 51(2), 864-873.
Liaw, S.-S., & Huang, H.-M. (2013). Perceived satisfaction, perceived usefulness and interactive learning environments as predictors to self-regulation in e-learning environments. Computers & Education, 60(1), 14-24.
Lu, O. H., Huang, J. C., Huang, A. Y., & Yang, S. J. (2017). Applying learning analytics for improving students engagement and learning outcomes in an MOOCs enabled collaborative programming course. Interactive Learning Environments, 25(2), 220-234.
Marangunić, N., & Granić, A. (2015). Technology acceptance model: a literature review from 1986 to 2013. Universal access in the information society, 14, 81-95.
Moreno, R., & Mayer, R. (2007). Interactive multimodal learning environments: Special issue on interactive learning environments: Contemporary issues and trends. Educational psychology review, 19, 309-326.
Papadakis, S., Kalogiannakis, M., Orfanakis, V., & Zaranis, N. (2019). The appropriateness of scratch and app inventor as educational environments for teaching introductory programming in primary and secondary education. In Early childhood development: Concepts, methodologies, tools, and applications (pp. 797-819): IGI Global.
Pears, A., Seidman, S., Malmi, L., Mannila, L., Adams, E., Bennedsen, J., . . . Paterson, J. (2007). A survey of literature on the teaching of introductory programming. Working group reports on ITiCSE on Innovation and technology in computer science education, 204-223.
Pintrich, P. R. (2003). A motivational science perspective on the role of student motivation in learning and teaching contexts. Journal of educational Psychology, 95(4), 667.
Rivers, K., Harpstead, E., & Koedinger, K. R. (2016). Learning curve analysis for programming: Which concepts do students struggle with? Paper presented at the ICER.
Schiefele, U. (1991). Interest, learning, and motivation. Educational psychologist, 26(3-4), 299-323.
Schraw, G. (1998). Promoting general metacognitive awareness. Instructional science, 26(1-2), 113-125.
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, 1405-1425.
Stewardson, G. A., Robinson, T. P., Furse, J. S., & Pate, M. L. (2019). Investigating the relationship between VEX robotics and student self-efficacy: An initial look. International Journal of Technology and Design Education, 29, 877-896.
Weintrop, D., Walkoe, J., Walton, M., Bih, J., Moon, P., Elby, A., . . . Kantzer, M. (2022). Sphero. Math: A computational thinking-enhanced fourth grade mathematics curriculum. In Computational Thinking in PreK-5: Empirical Evidence for Integration and Future Directions (pp. 39-46).
Westling, D. L., Fox, L., & Carter, E. W. (2000). Teaching students with severe disabilities: Merrill Upper Saddle River, NJ.
Zheng, L., Li, X., & Chen, F. (2018). Effects of a mobile self-regulated learning approach on students’ learning achievements and self-regulated learning skills. Innovations in Education and Teaching International, 55(6), 616-624.
Zhu, Y., Zhang, J. H., Au, W., & Yates, G. (2020). University students’ online learning attitudes and continuous intention to undertake online courses: A self-regulated learning perspective. Educational technology research and development, 68, 1485-1519.
Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In Handbook of self-regulation (pp. 13-39): Elsevier.
Zimmerman, B. J. (2013). Theories of self-regulated learning and academic achievement: An overview and analysis. Self-regulated learning and academic achievement, 1-36.
Zuffianò, A., Alessandri, G., Gerbino, M., Kanacri, B. P. L., Di Giunta, L., Milioni, M., & Caprara, G. V. (2013). Academic achievement: The unique contribution of self-efficacy beliefs in self-regulated learning beyond intelligence, personality traits, and self-esteem. Learning and individual differences, 23, 158-162.