簡易檢索 / 詳目顯示

研究生: 謝亞錚
Ya-Jeng Shieh
論文名稱: 機器人輔助程式設計學習之學習成效與學生心智模型探討
The effects and mental models of using robots to learn programming
指導教授: 吳正己
Wu, Cheng-Chih
學位類別: 碩士
Master
系所名稱: 資訊教育研究所
Graduate Institute of Information and Computer Education
論文出版年: 2009
畢業學年度: 97
語文別: 中文
論文頁數: 100
中文關鍵詞: 程式設計機器人心智模型
英文關鍵詞: Programming, Robots, Mental models
論文種類: 學術論文
相關次數: 點閱:204下載:12
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  • 本研究目的在探討使用機器人學習程式設計之成效及其可能形成之心智模型。研究採準實驗設計,參與者為台北某高中的四班高一學生共144人,依使用工具之不同將學生分為實體機器人、模擬軟體、實體加模擬、及傳統等四組。實驗分二階段進行,第一階段目的在探討三種不同機器人輔程式設計學習之成效,第二階段目的在比較使用機器人與傳統方式學習程式設計之成效及可能產生的心智模型。研究結果發現:(1)實體機器人與模擬軟體搭配使用能產生不錯的學習成果,(2)學生對使用機器人學習程式設計持正向態度,(3)學生使用機器人與使用傳統方式學習程式設計傾向於產生不同的心智模型。建議未來研究可探討同時使用機器人和傳統方法學習程式設計之成效及心智模型。

    This study investigated the effects of using robots in teaching high school students programming and possible mental models which students might form from such a strategy. A quasi-experiment design was implemented in this study. Four classes of high school students, totaling 144 students, participated in this study. Participants were assigned into four groups according to the tools they used: physical robot group, robot simulator group, robot-and-simulator group, and traditional group. The experiment was conducted in two stages, the first stage aimed to investigate the effects of using different kinds of robotic tools in teaching programming: physical robot, robot simulator, and robot-and-simulator. The second stage was to compare the effects of using robots and traditional methods, and the possible mental models formed by students. The findings of the study were: (1) Using robots and simulators together would enhance students’ performance. (2) Students had positive attitudes toward using robotic tools learning programming. (3) Students using different tools would result in different mental models. We suggested future study to investigate the effects of combining robots with traditional method to help students learning programming.

    目 錄 I 圖 次 II 表 次 III 第一章 緒論 1 第一節 背景與動機 1 第二節 研究目的 4 第三節 研究範圍與限制 4 第二章 文獻探討 5 第一節 機器人與程式設計教學 5 第二節 程式設計的心智模型 12 第三章 研究方法 19 第一節 研究設計 19 第二節 研究參與者 20 第三節 教學規劃 20 第四節 研究工具 23 第五節 實施步驟 30 第四章 結果與討論 33 第一節 機器人程式設計 33 第二節 傳統程式設計 39 第三節 心智模型 51 第五章 結論與建議 58 第一節 結論 58 第二節 建議 60 參考文獻 61 附錄一 活動問卷 65 附錄二 課堂觀察記錄表 69 附錄三 第一階段講義 70 附錄四 第二階段講義 80 附錄五 第一階段成就測驗 91 附錄六 第二階段成就測驗 98

    曾義智(2007):應用機器人於程式設計教學─實體機器人與模擬軟體使用成效比較。未出版碩士論文,國立臺灣師範大學資料教育研究所,台北市。

    Astrachan, O. (1998). Concrete teaching: Hooks and props in teaching programming, Proceedings of ITiCSE 1998, 21-24.

    Ben-Ari, M. (2001). Constructivism in computer science education. Journal of Computers in Mathematics and Science Teaching, 20(1), 45-73.

    Cañas J. J., Bajo M. T., Gonzalov P. (1994). Mental models and computer programming. International Journal of Human-Computer Studies, 40(5), 795-811.

    Costelloe, E. (2004). Teaching Programming The State of the Art. CRITE Technical Report.

    Corritore, C. L. & Wiedenbeck, S. (1999). Mental representation of expert procedural and object-oriented programmers in a software maintenance task. International Journal of Human-Computer Studies, 50(1), 61-83.

    Dehnadi, S. (2006). Testing Programming Aptitude. Proceegings of the 18th Annual Woekshop of the Psychology of Programming Interest Group, 22-37. Brighton, UK.

    Dehnadi, S., & Bornat, R. (2006). The camel has two humps, www.cs.mdx.ac.uk/research/PhDArea/saeed/paper1.pdf.

    Duke, R., Salzman, E., Burmeister, J., Poon, J., & Murray, L. (2000) Teaching programming to beginners – choosing the language is just the first step. Proceedings of the Australasian conference on Computing education, 79-86. Melbourne, Australia.

    Er, M. C. (1995). Process frame: a cognitive device for recursion comprehension. Computers and Education, 24(1), 31-36.

    Fagin, B. S., Merkle, L. D., & Eggers, T. W. (2001). Teaching computer science with robotics using Ada/Mindstorms 2.0. ACM SIGAda Ada Letters, XXI(4),73-78.

    Fagin, B., Merkle, L. (2003). Mesuring the effectiveness of robots in teaching computer science. ACM SIGCSE Bulletin, 35(1), 307-311.

    Galloway, J. P. (1999). The value of programming in beginning educational computing. Proceedings of Society for Information Technology and Teacher Education International Conference, 376-381. February 28-March 4, 1999, San Antonio, TX, USA.

    Goldweber, M., Congdon, C., Fagin, B., Hwang. D., & Klassner, F. (2001). The use of robots in the undergraduate curriculum, ACM SIGCSE Bulletin, 33(1), 404-405.

    Götschi, T., Sanders, I., & Galpin, V. (2003). Mental models of recrusion. ACM SIGCSE Bulletin, 35(1), 346-350.

    Haberman, B. (2004). How learning logic programming affects recursion comprehension. Computer Science Education, 14(1), 37-53.

    Jacobsen, C. L. & Jadud, M. C. (2005). Towards concrete concurrency: occam-pi on the LEGO Mindstorms. ACM SIGCSE Bulletin, 37(1), 431-435.

    Johnson-Laird, P. N. (1983). Mental Models. Cambrige University Press, Cambridge London.

    Kahney, H. (1983). What do novice programmers know about recursion. Proceedings of the SIGCHI conference on Human Factors in Computing Systems, 235-239. Boston, MA, USA.

    Klassner, F. (2002). A case study of LEGO Mindstorms'™ suitability for artificial intelligence and robotics courses at the college level, Technical Symposium on Computer Science Education Proceedings of the 33rd SIGCSE technical symposium on Computer science education, 8-12.

    Linn, M. C., & Clancy, M. J. (1992). The case for case studies of programming problems. Communications of the ACM, 35(3). 121-132.
    Lui, A. K., Kwan, R., Poon, M., Cheung, Y. H. Y. (2004). Saving weak programming student: applying constructivism in a first programming course. ACM SIGCSE Bulletin, 36(2), 72-76.

    Ma, L., Ferguson, J., Roper, M., & Wood, M. (2006). Investigating novice programmers' mental models. Technical report, the EFoCS Group, University of Strathclyde, http://www.cis.strath.ac.uk/~linxiao/TechReport2006.doc.

    Ma, L., Ferguson, J., Roper, M., & Wood, M. (2007). Investigating the viability of mental models held by novice programmers. ACM SIGCSE Bulletin, 39(1), 499-503.

    Mannila, L., Peltomaki, M., & Salakoski, T. (2006) What about a simple language? Analyzing the difficulties in learning to program. Computer science education, 16(3), 211-227.

    McNerney, T. S. (2004). From turtles to tangible programming bricks: explorations in physical language design. Personal and Ubiquitous Computing, 8(5), 326-337.

    Myers, B. A. (1986). Visual programming, programming by example, and program visualization a taxonomy. Proceedings of the SIGCHI conference on Human factors in computing systems, 59-66. Boston, Massachusetts, United States

    Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. New York: Basic Books.

    Pennington, N., (1987). Stimulus structures and mental representations in expert comprehension of compuer programs. Cognitive psychology, 19(3), 295-341.

    Ramalingam, V., LaBelle, D.,& Wiedenbeck, S. (2004). Self-efficacy and mental models in learning to program. ACM SIGCSE Bulletin, 36(3), 171-175.

    Soloway, E. (1986). Learning to program = Learning to construct mechanisms and explanations. Communications of the ACM, 29(9), 850-858.

    Sanders, I., Galpin, V., & Götschi, T. (2006). Mental models of recursion. Proceedings of the 11th annual SIGCSE congerence on Innovation and technology in computer science education, June 26-28, 2006, Bologna, Italy.

    Wulf, T. (2005). Constructivist approaches for teaching computer programming. Proceedings of the 6th conference on Information technology education, 245-248. Newark, NJ, USA.

    Wiedenbeck, S. & Ramalingam, V. (1999). Novice comprehension of small programs written in the procedural and object-oriented styles. International Journal of Human-Computer Studies, 51(1), 71-87.

    Zaidman, M. (2004). Teaching defensive programming in JAVA. Journal of Computing Sciences in Colleges, 19(3), 33-43.

    Zmuda, M. & Hatch, M. (2007). Scheduling topics for improved student comprehension of recursion. Computers and Education, 48(2), 318-328.

    下載圖示
    QR CODE