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研究生: 洪阡珈
Hong, Chien-Chia
論文名稱: 置入運算思維於學習鷹架中對高中程式寫作課程中之自我效能與學習成效之影響
The Influences of Inserting Computational Thinking in Learning Scaffolding on Self-Efficacy and Learning Outcome in High School Computer Programming
指導教授: 王健華
Wang, Chang-Hwa
口試委員: 趙貞怡
Chao, Jen-Yi
周遵儒
Chou, Tzren-Ru
王健華
Wang, Chang-Hwa
口試日期: 2022/09/26
學位類別: 碩士
Master
系所名稱: 圖文傳播學系
Department of Graphic Arts and Communications
論文出版年: 2022
畢業學年度: 110
語文別: 中文
論文頁數: 93
中文關鍵詞: 程式設計學習運算思維學習鷹架學習成效自我效能
英文關鍵詞: programming learning, computational thinking, learning scaffolds, learning outcome, self-efficacy
研究方法: 準實驗設計法
DOI URL: http://doi.org/10.6345/NTNU202201797
論文種類: 學術論文
相關次數: 點閱:93下載:19
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  • 程式設計內容相互具有關聯性,然而程式概念抽象不易理解且複雜性高,因此多數學習者對於程式學習產生消極的信念,造成自我效能降低並影響學習表現。為了解決程式學習的困境,教學者多運用運算思維的概念來設計教學策略。本研究假設教學者提供學習鷹架來協助學習者與已知知識相互融會貫通,因後設認知鷹架能喚起學習者的已知知識,藉以輔助學習新知識,但其缺乏邏輯系統性的引導方式,所以本研究設計將運算思維流程置入於後設認知鷹架中來引導學習者學習程式設計。研究目的在於探討運算思維流程與傳統教學兩者鷹架引導方式對於學習者自我效能與學習成效之影響。研究方法為實驗法,實驗對象為普通高中一年級學生共39人,實驗組以運算思維流程之步驟來設計鷹架引導內容,對照組以傳統教學方式之程式敘述順序來設計鷹架引導內容,以學習成效測驗試題與自我效能量表作為量化研究工具,並在實驗結束後透過半結構式訪談收集質性資料。研究結果發現在進行實驗課程後,使用運算思維流程鷹架引導方式之學習成效與自我效能表現皆優於使用傳統教學鷹架引導方式,且自我效能與學習成效表現具有相關性。建議未來相關研究可深入探討影響自我效能與學習成效的其他因素。

    Programming content is interconnected. However, programming concepts are mostly abstract, difficult to comprehend, and highly complicated. As a result, most learners have negative impressions about learning programming, leading to lower self-efficacy as well as learning performance. To solve this dilemma, instructors employed the idea of computational thinking when devising teaching strategies. Metacognitive scaffolds can evoke learners’ known knowledge. Thus, this study hypothesized that by providing learning scaffolds, instructors, can help learners integrate new content with their existing knowledge so as to facilitate their learning of new knowledge. However, a logical and systematic scaffolding is lacking. Therefore, this study was designed to integrate computational thinking processes into metacognitive scaffolds to guide learners for studying programming. This study aimed to investigate the influences of the teaching scaffolds of computational thinking processes and traditional teaching approach on learners’ self-efficacy and learning outcome. The experimental method was selected as the study methodology and 39 first-year high school students were chosen as the study subjects. For the experimental group, we adopted the steps of computational thinking processes to design the scaffolding content. For the control group, the scaffolding content was designed following the programming narrative sequence of the traditional teaching approach. We selected learning outcome related test questions and self-efficacy scale as quantitative research tools, and garnered qualitative data through semi-structured interviews at the end of the experiment. The study findings indicated that after the experimental sessions ended, learning outcome and self-efficacy driven by scaffolding of computational thinking were better than those under the traditional teaching approach. Moreover, a correlation existed between self-efficacy and learning outcome performance. It is suggested that future research works should investigate other factors affecting learners’ self-efficacy and learning outcome.

    第一章 緒論 1    第一節 研究背景與動機 1    第二節 研究目的與問題 3    第三節 研究範圍與限制 4    第四節 研究流程 5    第五節 名詞釋義 6 第二章 文獻探討 8    第一節 程式設計學習之困境 8    第二節 運算思維 9    第三節 鷹架理論 11    第四節 自我效能與程式設計學習成效 14    第五節 文獻探討小結 15 第三章 研究設計 17 第一節 研究架構 17    第二節 研究方法 19    第三節 研究對象 19    第四節 研究工具 20    第五節 教學內容設計 23    第六節 研究實施 29    第七節 資料處理與分析 31 第四章 研究結果與討論 33    第一節 不同鷹架引導方式之學習成效差異分析 34    第二節 不同鷹架引導方式之自我效能差異分析 35    第三節 自我效能與學習成效相關性分析 36 第四節 訪談內容分析 37 第五章 結論與建議 42    第一節 研究結論 42    第二節 後續研究建議 43 參考文獻 45 附錄一 教學課程教案 54 附錄二 教學教材 70 附錄三 課程練習題 75 附錄四 學習成效測驗試卷 81 附錄五 自我效能量表 84 附錄六 學習成效測驗評分表 90 附錄七 訪談大綱 91

    一、   中文部分
    香港賽馬會(2016)。賽馬會運算思維教育。取自https://www.coolthink.hk
    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:國家教育研究院。
    吳昀臻、鄭雅婷(2020)。淺談鷹架理論與課程的效益。臺灣教育評論月刊,9(2),69-73。

    二、   外文部分
    Azevedo, R., & Hadwin, A. (2005). Scaffolding self-regulated learning and metacognition: Implications for the design of computer-based scaffolds. Instructional Science, 33, 367-379.
    An, Y. J. (2010). Scaffolding wiki-based, ill-structured problem solving in an online environment. MERLOT Journal of Online Learning and Teaching, 6(4), 723-734.
    Abdullahi, M. S. I., Salleh, N., Nordin, A., & Alwan, A. A. (2018). Cloud-based learning system for improving students’ programming skills and self-efficacy. Journal of Information and Communication Technology, 17(4), 629-651.
    Alshaye, I., Tasir, Z., Jumaat, N.F. (2019). The conceptual framework of online problem-based learning towards problem-solving ability and programming skills. Proceedings of the 2019 IEEE Conference e-Learning, e-Management and e-Services (pp. 12-14), Penang, Malaysia.
    Almjally, A., Howland, K., & Good, J. (2020). Investigating children's spontaneous gestures when programming using TUIs and GUIs. Proceedings of the 2020 ACM Interaction Design and Children Conference (pp. 36-48), London, United Kingdom.
    Angeli, C., & Giannakos, M. (2020). Computational thinking education: Issues and challenges. Computers in Human Behavior, 105, 106185.
    Bruner, J. S. (1985). Child’s talk: Learning to use language. New York: Norton.
    Bandura, A. (1994). Self-efficacy. Encyclopedia of human behavior, 4, 71-81.
    Bernard, M., and Bachu, E. (2015). Enhancing the metacognitive skill of novice programmers through collaborative learning. Metacognition: Fundaments, Applications, and Trends, 76, 277-298.
    Bocconi, S., Chioccariello, A., Dettori, G., Ferrari, A., & Engelhardt, K. (2016). Developing computational thinking in compulsory education-implications for policy and practice. Joint Research Centre (Seville site), 3-68.
    Bhardwaj, J. (2017). In search of self-efficacy: Development of a new instrument for first year Computer Science students. Computer Science Education, 27(2), 79-99. 
    Brown, N.C. & Wilson, G. (2018). Ten quick tips for teaching programming. PLoS computational biology, 14(4), 1-8.
    BBC Bitesize. (2019). Computational thinking-KS3 Computing. Retrieved from https://www.bbc.co.uk/bitesize/topics/z7tp34j
    CSTA & ISTE (2011). Operational definition of computational thinking for K-12 education. Retrieved from https://www.iste.org
    Chen, K., Chan, A. H. S. (2011). A review of technology acceptance by older adults. Gerontechnology, 10(1), 1-12.
    Chung, I. L., Chou, C. M., Hsu, C. P., & Li, D. K. (2016). A programming learning diagnostic system using case-based reasoning method. Proceedings of the IEEE International Conference on System Science and Engineering (ICSSE) (pp. 1-4), Puli, Taiwan.
    Cabo, C. (2018). Effectiveness of Flowcharting as a Scaffolding Tool to Learn Python. Proceedings of the 2018 IEEE Frontiers in Education Conference (FIE) (pp. 1-7), San Jose, USA.
    Cheah, C. S. (2020). Factors Contributing to the Difficulties in Teaching and Learning of Computer Programming: A Literature Review. Contemporary Educational Technology, 12(2), 272.
    Denny, P., Prather, B., Becker, A., Albrecht, Z., Loksa, D., & Pettit, R. (2019). A Closer Look at Metacognitive Scaffolding: Solving Test Cases Before Programming. Proceedings of the 19th Koli Calling International Conference on Computing Education Research (pp. 1-10), Finland, Koli.
    Doo, M. Y., Bonk, C., & Heo, H. (2020). A meta-analysis of scaffolding effects in online learning in higher education. The International Review of Research in Open and Distributed Learning, 21(3), 60-80.
    Dawar, D., Murphy, M. (2020). An assignment a day: Scaffolded learning approach for teaching introductory computer programming. Information Systems Education Journal, 18(4), 59-73. 
    Google (2015). Exploring computational thinking. Retrieved from https://edu.google.com/resources/programs/exploring-computational-thinking/
    Gedeon, S.A. and Valliere, D. (2018), “Closing the loop: measuring entrepreneurial self-efficacy to assess student learning outcomes”. Entrepreneurship Education & Pedagogy,1(4), 272-303.
    Georgiou, K., Angeli, C. (2019). Developing Preschool Children’s Computational Thinking with Educational Robotics: The Role of Cognitive Differences and scaffolding. Proceedings of the 16th International Conference on Cognition and Exploratory Learning in Digital Age (PP. 101-108), Cagliari, Italy: IADIS Press.
    Gorson, J., O'Rourke, E. (2020). Why do CS1 Students Think They're Bad at Programming? Investigating Self-efficacy and Self-assessments at Three Universities. Proceedings of the 2020 ACM Conference on International Computing Education Research (pp. 170-181), Virtual Event, New Zealand.
    Hill, J., & Hannafin, M. (1997). Cognitive strategies and learning from the World Wide Web. Educational Technology Research and Development, 45(4), 37-64.
    Hannafin, M., Land, S., & Oliver, K. (1999). Open learning environments: Foundations, methods, and models. Instructional-design theories and models: A new paradigm of instructional theory, 2, 115-140. Mahwah, NJ: Lawrence Erlbaum Associates.
    Islam, N., Sheikh, G. S., Fatima, R., & Alvi, F. (2019). A Study of Difficulties of Students in Learning Programming. Journal of Education & Social Sciences, 7(2), 38-46.
    Jin, Y., Sun, J., Ma, H., & Wang, X. (2021). The impact of different types of scaffolding in project-based learning on girls' computational thinking skills and self-efficacy. Proceedings of the 2021 Tenth International Conference of Educational Innovation through Technology (EITT) (pp. 1-6), Chongqing, China.
    Kong, S. C. (2017). Development and validation of a programming self-efficacy scale for senior primary school learners. In S. C. Kong, J. Sheldon, & K. Y. Li (Eds.), Proceedings of the International Conference on Computational Thinking Education (pp. 97-102), The Education University of Hong Kong.
    Kim, K., Cho, Y. K., & Kim, K. (2018). “BIM-Based Decision-Making Framework for Scaffolding Planning.” Journal of Management in Engineering, 34(6), 04018046-1-04018046-17.
    Kanaparan, G., Cullen, R., & Mason, D. (2019). Effect of self-efficacy and emotional engagement on introductory programming students. Australasian Journal of Information Systems, 23, 1-24.
    Kukul, V., Gokcearslan, S., & Gunbatar, M. S. (2017). Computer programming self-efficacy scale (CPSES) for secondary school students: Development, validation and reliability. Educational Technology Theory and Practice, 7(1), 158-179.
    Kukul, V., & Karatas, S. (2019). Computational thinking self-efficacy scale: Development, validity and reliability. Informatics in Education, 18(1), 151-164.
    Kılıç, S., Gökoğlu, S., & Öztürk, M. (2020). A Valid and Reliable Scale for Developing Programming-Oriented Computational Thinking. Journal of Educational Computing Research, 59(2), 257-286.
    Kong, S. C., Lai, M., & Sun, D. (2020). Teacher development in computational thinking: Design and learning outcomes of programming concepts, practices and pedagogy. Computers y Education, 151, 103872.
    Kadar, R., Mahlan, S. B., Shamsuddin, M., Othman, j., & Wahab, N. A. (2022). Analysis of Factors Contributing to the Difficulties in Learning Computer Programming among Non-Computer Science Students. Proceedings of the 2022 IEEE 12th Symposium on Computer Applications & Industrial Electronics (ISCAIE)(pp. 89-94), Penang Island, Malaysia.
    Lishinski, A., Yadav, A., Good, J., & Enbody, R. (2016). Learning to program: Gender differences and interactive effects of students' motivation, goals, and self‐efficacy on performance. Proceedings of the 2016 ACM Conference on International Computing Education Research (pp. 211-220), Melbourne, VIC, Australia: ACM.
    Lishinski, A., Yadav, A. (2021). Self-Evaluation Interventions: Impact on Self-Efficacy and Performance in Introductory Programming. ACM Transactions on Computing Education, 21(3), 23:1-23:28.
    Mohd Rum, S. N., & Ismail, M. A. (2017). Metocognitive Support Accelerates Computer Assisted Learning for Novice Programmers. Educational Technology & Society, 20(3), 170-181.   
    Ortiz, M., Chiluiza, K., & Valcke, M. (2017). Gamification in computer programming: Effects on learning, engagement, self-efficacy and intrinsic motivation. Proceedings of the 11th European Conference on Games Based Learning (ECGBL 2017) (pp.507-514), Graz, Austria.
    Özyurt, H., & Özyurt, Ö. (2018). Analyzing the effects of adapted flipped classroom approach on computer programming success, attitude toward programming, and programming self-efficacy. Computer Applications in Engineering Education, 26(6), 2036-2046.
    Papert, S. (1980). Children, computers, and powerful ideas. New York, NY: Basic Books.
    Psycharis, S., & Kallia, M. (2017). The effects of computer programming on high school students’ reasoning skills and mathematical self-efficacy and problem solving. Instructional Science, 45(5), 583-602.
    Prather, J., Pettit, R., McMurry, K., Peters, A., Homer, J., & Cohen, M. (2018). Metacognitive Difficulties Faced by Novice Programmers in Automated Assessment Tools. Proceedings of the 2018 ACM Conference on International Computing Education Research (pp. 41-50), New York, USA.
    Prather, J., Pettit, R., Becker, B. A., Denny, P., Loksa, D., Peters, A., Albrecht, Z., & Masci, K. (2019). First Things First: Providing Metacognitive Scaffolding for Interpreting Problem Prompts. Proceedings of the 50th ACM Technical Symposium on Computer Science Education (pp. 531-537), New York, USA.
    Robins, A. (2019). Novice programmers and introductory programming. The Cambridge Handbook of Computing Education Research, Cambridge Handbooks in Psychology, 327-376.
    Sysło, M. M., & Kwiatkowska, A. B. (2013). Informatics for all high school students. Proceedings of the International conference on Informatics in Schools: Situation, Evolution, and Perspectives (pp. 43-56), Berlin, Heidelberg.
    Shen, J., Chen, G., Barth-Cohen,L., Jiang,S., & Eltoukhy, M. (2020). Connecting computational thinking in everyday reasoning and programming for elementary school students. Journal of Reasearch on Technology in Education, 1-21.
    Steinhorst, P., Petersen, A., & Vahrenhold, J. (2020). Revisiting self-efficacy in introductory programming. Proceedings of the 2020 ACM Conference on International Computing Education Research (ICER’20) (pp.158-169), Virtual Event, New Zealand.
    Silva, L., Mendes, A., Gomes, A., & Cavalcanti-De-Macedo, G. (2021). Regulation of Learning Interventions in Programming Education: A Systematic Literature Review and Guideline Proposition. Proceedings of the 52nd ACM Technical Symposium on Computer Science Education (pp. 13-20), Virtual Event, USA.
    Sun, L., Hu, Linlin., & Zhou, D. (2021). Improving 7th-graders’computational thinking skills through unplugged programing activities: A study on the influence of multiple factors. Thinking skills and creativity, 42, 2-15.
    Te Kete Ipurangi. (2017). Digital Technologies and the national curriculum. Retrieved from https://www.tki.org.nz
    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.
    Toharudin, U., Rahmat, A., & Kurniawan, I. S. (2019). The important of self-efficacy and self-regulation in learning: How should a student be? Journal of Physics: Conference Series, 1157(2), 1-6.
    Vygotsky, L. S. (1978). Mind in society : The development of higher psychological processes. Cambridge, MA: Harvard University Press.
    Wood, D. J., Bruner, J. S., & Ross, G. (1976). The role of tutoring in problem solving. Journal of Child Psychology and Psychiatry, 17, 89-100.
    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 on the Royal Society A: Mathematical, Physical and Engineering Sciences, 366(1881), 3717-3725.
    Wing, J. M. (2011). Research Notebook: Computational Thinking--What and Why? The Link Magazine. The magazine of the Carnegie Mellon University School of Computer Science. Retrieved from http://people.cs.vt.edu/~kafura/CS6604/Papers/CT-What-And-Why.pdf
    Wing, J. M. (2017). Computational thinking’s influence on research and education for all. Italian Journal of Educational Technology, 25(2), 7-14.
    Xinogalos, S. (2016). Designing and deploying programming courses: Strategies, tools, difficulties and pedagogy. Education and Information Technologies, 21(3), 559-588. 
    Yildiz Durak, H. (2018). Digital story design activities used for teaching programming effect on learning of programming concepts, programming self-efficacy, and participation and analysis of student experiences. Journal of Computer Assisted learning, 34, 740-752.
    Zakaria, I. N., Iksan, Z.H. (2020). Computational Thinking among High School Students. Universal Journal of Educational Research, 8(11A), 9-16.
    Zhou, P., Li, J., Chen, F., Zhou, H., Bao, S., & Li, M. (2021). Design of metacognitive scaffolding for k-12 programming education and its effects on students' problem solving ability and metacognition. Proceedings of the 2021 Tenth International Conference of Educational Innovation through Technology (EITT) (pp. 182-186), Chongqing, China.

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