本研究以運算思維導向程式設計教學模式教授國小學生迴圈概念，並探討此模式對學生學習成就、運算思維和學習態度之影響。該模式是在學生進行程式設計解決問題之前，先透過逐步運算的過程中觀察解題規律，以理解迴圈的運作過程，然後再使用迴圈結構解決問題，並進而培養學生運算思維。本研究採準實驗研究設計，共102位國小四年級學生參與實驗。實驗組（53位）採用運算思維導向程式設計教學模式，對照組（49位）則使用傳統教學模式。研究工具包括：學習單、成就測驗、運算思維測驗、態度問卷、及Dash機器人。
研究結果顯示，兩組學生在成就測驗、運算思維能力及態度皆無顯著差異。根據課堂觀察沒有達到顯著差異的原因可能為：（1）兩種教學模式從不同面向幫助學生學習迴圈，（2）傳統模式在講解迴圈觀念也強調運算思維，（3）教學設計的安排，讓實驗組擁有較少的練習時間，（4）教材中的巢狀迴圈圖形對於國小學生過於複雜。建議未來研究應考慮學生的先備知識及認知能力，並給予學生更多的鷹架引導，以及更多的時間思考及練習；並可以將運算思維導向程式設計教學模式運用在其他主題，釐清是否因概念之差異而有不同之效果。

This study applied the computational thinking-based programming teaching model to teach elementary school students to learn loop concepts. The effects of the model on students' learning achievement, computational thinking, and learning attitudes were investigated. This study adopts a quasi-experimental research design. One hundred and two 4th grade elementary school students participated in the experiment. The experimental group (53 students) used the computational thinking-based programming teaching method, while the control group (49 students) used the traditional teaching method. The research tools used in this study included activity worksheets, an achievement test, a computational thinking test, an attitude questionnaire, and the Dash robot.
The results showed that there was no significant difference between the computational thinking-based method and the traditional method in terms of students’ achievement test, computational thinking ability, and attitudes toward learning. Possible reasons for the results were: (1) the two teaching methods equally helped students learn loop concepts but from different perspectives, (2) the traditional method also embedded the merit of computational thinking, (3) the experimental group did not have enough time on practice activities, and (4) the nested loops were too complicated for elementary students to learn. Future studies on applying the computational thinking-based method should consider students' prior knowledge and cognitive abilities, provide scaffolding during learning, and allow more time on practice. It is suggested to apply the computational thinking-based model on other programming topics to explore how the effects may be different due to the nature of the concepts.

林裕雲（2010）。實施電腦 LOGO 程式設計教學對台灣國小學生解題能力之影響─ 國小六年級學生之個案研究。國立屏東師範學院數理教育研究所碩士論文。
林育慈、吳正己（2016）。運算思維與中小學資訊科技課程。 教育脈動，6，5-20。
陳怡芬、林育慈、翁禎苑（2018）。運算思維導向程式設計教學─ 以 [動手玩音樂] 模組化程式設計為例。中等教育。
教育部（2014）。十二年國民基本教育課程綱要總綱。取自https://www.naer.edu.tw/ezfiles/0/1000/attach/87/pta_18543_581357_62438.pdf
教育部（2018）。十二年國民基本教育課程綱要科技領域。取自https://www.k12ea.gov.tw/files/class_schema/課綱/13-科技/13-1/十二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.pdf
黃文聖（2001）。國小學童在 Logo 學習環境中數學學習與解題之研究。國立新竹師範學院數理教育研究所碩士論文。
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。
胡秋帆、吳正己、林育慈、游志弘（已接受）。高中生運算思維測驗發展。數位學習科技期刊。
ACARA. (2017). Retrieved from https://www.australiancurriculum.edu.au/f-10-curriculum/technologies/introduction/
Angeli, C., Voogt, J., Fluck, A., Webb, M., Cox, M., Malyn-Smith, J., & Zagami, J. (2016). A K-6 computational thinking curriculum framework: Implications for teacher knowledge. Journal of Educational Technology & Society, 19(3), 47-57.
APFC. (2017). Preparing students for South Korea’s creative economy: The successes and challenges of educational reform. Retrieved from http://www.asiapacific.ca/research-report/preparing-students-south-koreas-creative-economy-successes
Barr, D., Harrison, J., & Conery, L. (2011). Computational Thinking: A Digital Age Skill for Everyone. Learning & Leading with Technology, 38(6), 20-23.
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., Alexander, J., Freeman, I., & Grimley, M. (2009). Computer science unplugged: School students doing real computing without computers. The New Zealand Journal of Applied Computing and Information Technology, 13(1), 20-29.
Benakli, N., Kostadinov, B., Satyanarayana, A., & Singh, S. (2017). Introducing computational thinking through hands-on projects using R with applications to calculus, probability and data analysis. International Journal of Mathematical Education in Science and Technology, 48(3), 393-427.
Berland, M., Martin, T., Benton, T., Petrick Smith, C., & Davis, D. (2013). Using learning analytics to understand the learning pathways of novice programmers. Journal of the Learning Sciences, 22(4), 564-599.
Bocconi, S., Chioccariello, A., Dettori, G., Ferrari, A., & Engelhardt, K. (2016). Developing computational thinking in compulsory education-Implications for policy and practice (No. JRC104188). Joint Research Centre (Seville site).
Brennan, K., & Resnick, M. (2012, April). New frameworks for studying and assessing the development of computational thinking. In Proceedings of the 2012 annual meeting of the American Educational Research Association, Vancouver, Canada (Vol. 1, p. 25).
Bruckman, A., Jensen, C., & DeBonte, A. (2002). Gender and programming achievement in a CSCL environment.Dann, W., & Cooper, S. (2009). Education-Alice 3: concrete to abstract. Communications of the ACM, 52(8), 27-29.
De Raadt, M., Watson, R., & Toleman, M. (2002). Language trends in introductory programming courses. In Proceedings of the 2002 Informing Science+ Information Technology Education Joint Conference (InSITE 2002) (pp. 229-337). Informing Science Institute.
Djambong, T., & Freiman, V. (2016). Task-Based Assessment of Students' Computational Thinking Skills Developed through Visual Programming or Tangible Coding Environments. International Association for Development of the Information Society.
England, N. C. (2013). National curriculum in England: computing programmes of study. Gov. Uk. Retrieved from: https://www.gov.uk/government/publications/national-curriculum-in-england-computing-programmes-of-study
Google for Education (2015). Exploring Computational Thinking. Retrieved from https://edu.google.com/resources/programs/exploring-computational-thinking/
Grover, S., & Pea, R. (2013). Computational thinking in K–12: A review of the state of the field. Educational researcher, 42(1), 38-43.
Grover, S., Cooper, S., & Pea, R. (2014, June). Assessing computational learning in K-12. In Proceedings of the 2014 conference on Innovation & technology in computer science education (pp. 57-62).
Grover, S., & Basu, S. (2017, March). Measuring student learning in introductory block-based programming: Examining misconceptions of loops, variables, and boolean logic. In Proceedings of the 2017 ACM SIGCSE technical symposium on computer science education (pp. 267-272).
Grover, S., Jackiw, N., & Lundh, P. (2019). Concepts before coding: non-programming interactives to advance learning of introductory programming concepts in middle school. Computer Science Education, 29(2-3), 106-135.
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.
ISTE, I., & CSTA, C. (2011). Operational Definition of Computational Thinking for K–12 Education. National Science Foundation.
Jenkins, T., & Davy, J. (2002). Diversity and motivation in introductory programming. Innovation in Teaching and Learning in Information and Computer Sciences, 1(1), 1-9.
K-12 Computer Science Framework Steering Committee. (2016). K-12 computer science framework. ACM.
Kalelioğlu, F. (2015). A new way of teaching programming skills to K-12 students: Code. org. Computers in Human Behavior, 52, 200-210.
Lee, I., Martin, F., Denner, J., Coulter, B., Allan, W., Erickson, J., ... & Werner, L. (2011). Computational thinking for youth in practice. Acm Inroads, 2(1), 32-37.
Libeskind-Hadas, R., & Bush, E. (2013). A first course in computing with applications to biology. Briefings in bioinformatics, 14(5), 610-617.
Linn, M. C., & Dalbey, J. (1985). Cognitive consequences of programming instruction: Instruction, access, and ability. Educational Psychologist, 20(4), 191-206.
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.
Mladenović, M., Boljat, I., & Žanko, Ž. (2018). Comparing loops misconceptions in block-based and text-based programming languages at the K-12 level. Education and Information Technologies, 23(4), 1483-1500.
Pratt, D. (1994). Curriculum planning: A handbook for professionals. Wadsworth Publishing Company.
Saeli, M., Perrenet, J., Jochems, W. M., & Zwaneveld, B. (2011). Teaching programming in Secondary school: A pedagogical content knowledge perspective. Informatics in Education, 10(1), 73-88.
Saito, D., Washizaki, H., & Fukazawa, Y. (2016, July). Influence of the programming environment on programming education. In Proceedings of the 2016 ACM Conference on Innovation and Technology in Computer Science Education (pp. 354-354).
Selby, C. C. (2012, November). Promoting computational thinking with programming. In Proceedings of the 7th workshop in primary and secondary computing education (pp. 74-77).
Sentance, S., Barendsen, E., & Schulte, C. (Eds.). (2018). Computer Science Education: Perspectives on Teaching and learning in school. Bloomsbury Publishing.
Seow, P., Looi, C. K., How, M. L., Wadhwa, B., & Wu, L. K. (2019). Educational Policy and Implementation of Computational Thinking and Programming: Case Study of Singapore. In Computational Thinking Education (pp. 345-361). Springer, Singapore.
Shute, V. J., Sun, C., & Asbell-Clarke, J. (2017). Demystifying computational thinking. Educational Research Review, 22, 142-158.
Weintrop, D., Beheshti, E., Horn, M., Orton, K., Jona, K., Trouille, L., & Wilensky, U. (2016). Defining computational thinking for mathematics and science classrooms. Journal of Science Education and Technology, 25(1), 127-147.
Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35.
Wing, J. (2011). Research notebook: Computational thinking–What and why? The Link: The Magazine of the Carnegie Mellon University School of Computer Science 6: 20–23.
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.
Wing, J. M. (2014). Computational thinking benefits society. 40th Anniversary Blog of Social Issues in Computing, 2014, 26.