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研究生: 陳思如
Chen, Si-Ru
論文名稱: 基於資料探勘之程式設計迷思概念診斷
The Diagnosis of Programming Misconceptions Based on Data Mining
指導教授: 林育慈
Lin, Yu-Tzu
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: 2019
畢業學年度: 107
語文別: 中文
論文頁數: 104
中文關鍵詞: 迷思概念診斷程式設計教學資料探勘
英文關鍵詞: Misconception diagnosis, Programming teaching, Data exploration
DOI URL: http://doi.org/10.6345/NTNU201900349
論文種類: 學術論文
相關次數: 點閱:149下載:0
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  • 摘要 I 誌謝 III 目錄 IV 表目錄 V 圖目錄 VI 第一章 緒論 1 第一節 研究背景與動機 1 第二節 研究目的 3 第三節 名詞釋義 4 第二章 文獻探討 6 第一節 迷思概念 6 第二節 程式設計迷思概念 10 第三節 資料探勘 16 第三章 研究方法 18 第一節 研究設計與架構 18 第二節 研究實驗參與者 20 第三節 研究程序 21 第四節 研究工具 22 第五節 迷思概念診斷之程式設計學習平台 26 第六節 迷思概念程式碼症狀探勘 36 第七節 迷思概念診斷 41 第四章 分析結果與討論 42 第一節 分析結果 42 第二節 討論 57 第五章 結論與建議 60 第一節 結論 60 第二節 建議 62 參考文獻 63 附錄一 程式設計紙本測驗試卷 (Python) 69 附錄二 程式設計紙本測驗試卷 (C++) 74 附錄三 程式設計紙本測驗試卷 (C語言) 80 附錄四 程式設計紙本測驗試卷 (Java) 88 附錄五 程式設計紙本測驗試卷 (JavaScript) 96

    Altadmri, A., &Brown, N. C. C. (2015). 37 Million Compilations. In Proceedings of the 46th ACM Technical Symposium on Computer Science Education - SIGCSE ’15 (pp. 522–527). New York, New York, USA: ACM Press. https://doi.org/10.1145/2676723.2677258
    Bayman, P., &Mayer, R. E. (1983). A diagnosis of beginning programmers’ misconceptions of BASIC programming statements. Communications of the ACM, 26(9), 677–679. https://doi.org/10.1145/358172.358408
    Bonar, J., &Soloway, E. (1985). Preprogramming Knowledge: A Major Source of Misconceptions in Novice Programmers. Human–Computer Interaction, 1(2), 133–161. https://doi.org/10.1207/s15327051hci0102_3
    Britos, P., Rey, E. J., Rodriguez, D., &Garcia-Martinez, R. (2008). Work in progress - programming misunderstandings discovering process based on intelligent data mining tools. In 2008 38th Annual Frontiers in Education Conference (p. F4H–1–F4H–2). IEEE. https://doi.org/10.1109/FIE.2008.4720499
    Chakrabarti, S., Ester, M., Fayyad, U., Gehrke, J., Han, J., Morishita, S., …Wang, W. (2006). Data Mining Curriculum: A Proposal (Version 1.0) Intensive Working Group of ACM SIGKDD Curriculum Committee. Retrieved from http://www.kdd.org/exploration_files/CURMay06.pdf
    Clancy, M. (2004). Misconceptions and Attitudes that Interfere with Learning to Program. (Sally Fincher,Marian Petre, Ed.). Computer Science Education Research. Retrieved from http://lib.myilibrary.com/Open.aspx?id=21787
    Clement, J. (1993). Using bridging analogies and anchoring intuitions to deal with students’ preconceptions in physics. Journal of Research in Science Teaching, 30(10), 1241–1257. https://doi.org/10.1002/tea.3660301007
    DuBoulay, B. (1986a). Some Difficulties of Learning to Program. Journal of Educational Computing Research, 2(1), 57–73. https://doi.org/10.2190/3LFX-9RRF-67T8-UVK9
    DuBoulay, B. (1986b). Some Difficulties of Learning to Program. Journal of Educational Computing Research, 2(1), 57–73. https://doi.org/10.2190/3LFX-9RRF-67T8-UVK9
    Durkin, K., &Rittle-Johnson, B. (2015). Diagnosing misconceptions: Revealing changing decimal fraction knowledge. Learning and Instruction, 37, 21–29. https://doi.org/10.1016/j.learninstruc.2014.08.003
    Eryilmaz, A. (2002). Effects of conceptual assignments and conceptual change discussions on students’ misconceptions and achievement regarding force and motion. Journal of Research in Science Teaching, 39(10), 1001–1015. https://doi.org/10.1002/tea.10054
    Fleury, A. E. (1991). Parameter passing: the rules the students construct. In Proceedings of the twenty-second SIGCSE technical symposium on Computer science education - SIGCSE ’91 (Vol. 23, pp. 283–286). New York, New York, USA: ACM Press. https://doi.org/10.1145/107004.107066
    Gilbert, J. K., &Watts, D. M. (1983). Concepts, Misconceptions and Alternative Conceptions: Changing Perspectives in Science Education. Studies in Science Education, 10(1), 61–98. https://doi.org/10.1080/03057268308559905
    Green, T. R. G. (1977). Conditional program statements and their comprehensibility to professional programmers. Journal of Occupational Psychology, 50(2), 93–109. https://doi.org/10.1111/j.2044-8325.1977.tb00363.x
    Grether, W. F. (1962). SOME IMPLICATIONS OF TESTING PROCEDURES FOR AUTO-INSTRUCTIONAL PROGRAMMING. ETS Research Bulletin Series, 1962(1), i-74. https://doi.org/10.1002/j.2333-8504.1962.tb00114.x
    Hand, D. J. (2007). Principles of Data Mining. Drug Safety, 30(7), 621–622. https://doi.org/10.2165/00002018-200730070-00010
    Kaczmarczyk, L. C., Petrick, E. R., East, J. P., &Herman, G. L. (2010). Identifying student misconceptions of programming. In Proceedings of the 41st ACM technical symposium on Computer science education - SIGCSE ’10 (p. 107). New York, New York, USA: ACM Press. https://doi.org/10.1145/1734263.1734299
    Klopfer, L. E., Champagne, A. B., &Gunstone, R. F. (1983). Naive Knowledge and Science Learning. Research in Science & Technological Education, 1(2), 173–183. https://doi.org/10.1080/0263514830010205
    Kohn, T. (2017). Variable Evaluation: an Exploration of Novice Programmers’ Understanding and Common Misconceptions. Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education (SIGCSE ’17) (Seattle, Washington, USA - March 08 - 11, 2017). https://doi.org/10.1145/3017680.3017724
    Kurvinen, E., Hellgren, N., Kaila, E., Laakso, M.-J., &Salakoski, T. (2016). Programming Misconceptions in an Introductory Level Programming Course Exam. Proceedings of the 2016 ACM Conference on Innovation and Technology in Computer Science Education, 308–313. https://doi.org/10.1145/2899415.2899447
    Lahtinen, E., Ala-Mutka, K., &Järvinen, H.-M. (2005). A study of the difficulties of novice programmers. In Proceedings of the 10th annual SIGCSE conference on Innovation and technology in computer science education - ITiCSE ’05 (Vol. 37, pp. 14–18). New York, New York, USA: ACM Press. https://doi.org/10.1145/1067445.1067453
    Ma, L. (2007a). Investigating and Improving Novice Programmers’ Mental Models of Programming Concepts. Retrieved from https://pdfs.semanticscholar.org/3c8e/fb0c95325ac2f6bb38bd3d56fdbe900e4892.pdf
    Ma, L. (2007b). Investigating and Improving Novice Programmers’ Mental Models of Programming Concepts.
    Mannila, L., Dagiene, V., Demo, B., Grgurina, N., Mirolo, C., Rolandsson, L., &Settle, A. (2014). Computational Thinking in K-9 Education. In Proceedings of the working group reports of the 2014 on innovation & technology in computer science education conference (pp. 1–29). ACM. https://doi.org/10.1145/2713609.2713610
    Ming-Syan Chen, Jiawei Han, &Yu, P. S. (1996). Data mining: an overview from a database perspective. IEEE Transactions on Knowledge and Data Engineering, 8(6), 866–883. https://doi.org/10.1109/69.553155
    National Research Council. (1997). Science teaching reconsidered: A handbook. Chapter 4: Misconceptions as Barriers to Understanding Science. National Academies Press. https://doi.org/10.17226/5287
    Pea, R. D. (1986a). Language-Independent Conceptual “Bugs” in Novice Programming. Journal of Educational Computing Research, 2(1), 25–36. https://doi.org/10.2190/689T-1R2A-X4W4-29J2
    Pea, R. D. (1986b). Language-Independent Conceptual “Bugs” in Novice Programming. Journal of Educational Computing Research, 2(1), 25–36. https://doi.org/10.2190/689T-1R2A-X4W4-29J2
    Pea, R. D., &Kurland, D. M. (1984). On the cognitive effects of learning computer programming. New Ideas in Psychology, 2(2), 137–168. https://doi.org/10.1016/0732-118X(84)90018-7
    Perkins, D., &Martin, F. (1985). Fragile knowledge and neglected strategies in novice programmers. First Workshop on Empirical Studies of Programmers on Empirical Studies of Programmers, 213–229.
    Plass, D. (2015). Identifying and addressing common programming misconceptions with variables (part 1). Retrieved from http://essay.utwente.nl/70455/
    Putnam, R. T., Sleeman, D., Baxter, J. A., &Kuspa, L. K. (1986). A Summary of Misconceptions of High School Basic Programmers. Journal of Educational Computing Research, 2(4), 459–472. https://doi.org/10.2190/FGN9-DJ2F-86V8-3FAU
    Qian, Y., &Lehman, J. (2017a). Students’ Misconceptions and Other Difficulties in Introductory Programming. ACM Transactions on Computing Education, 18(1), 1–24. https://doi.org/10.1145/3077618
    Qian, Y., &Lehman, J. (2017b). Students’ Misconceptions and Other Difficulties in Introductory Programming. ACM Transactions on Computing Education, 18(1), 1–24. https://doi.org/10.1145/3077618
    Ragonis, N., &Ben-Ari, M. (2005a). A long-term investigation of the comprehension of OOP concepts by novices. Computer Science Education, 15(3), 203–221. https://doi.org/10.1080/08993400500224310
    Ragonis, N., &Ben-Ari, M. (2005b). A long-term investigation of the comprehension of OOP concepts by novices. Computer Science Education, 15(3), 203–221. https://doi.org/10.1080/08993400500224310
    Robins, A. (2010). Learning edge momentum: a new account of outcomes in CS1. Computer Science Education, 20(1), 37–71. https://doi.org/10.1080/08993401003612167
    Sadler, P. M., &Sonnert, G. (2016). understanding misconceptions teaching and learning in middle school physical science. American Educator, 40(1), 26–32. Retrieved from https://eric.ed.gov/?id=EJ1094278
    Sadler, P. M., Sonnert, G., Coyle, H. P., Cook-Smith, N., &Miller, J. L. (2013a). The Influence of Teachers’ Knowledge on Student Learning in Middle School Physical Science Classrooms. American Educational Research Journal, 50(5), 1020–1049. https://doi.org/10.3102/0002831213477680
    Sadler, P. M., Sonnert, G., Coyle, H. P., Cook-Smith, N., &Miller, J. L. (2013b). The Influence of Teachers’ Knowledge on Student Learning in Middle School Physical Science Classrooms. American Educational Research Journal, 50(5), 1020–1049. https://doi.org/10.3102/0002831213477680
    Sekiya, T., &Yamaguchi, K. (2013). Tracing quiz set to identify novices’ programming misconceptions. In Proceedings of the 13th Koli Calling International Conference on Computing Education Research - Koli Calling ’13 (pp. 87–95). New York, New York, USA: ACM Press. https://doi.org/10.1145/2526968.2526978
    Shah, P., Berges, M., &Hubwieser, P. (2017). Qualitative Content Analysis of Programming Errors. In Proceedings of the 5th International Conference on Information and Education Technology - ICIET ’17 (pp. 161–166). New York, New York, USA: ACM Press. https://doi.org/10.1145/3029387.3029399
    Simon. (2011a). Assignment and sequence: why some students can’t recognise a simple swap. In Proceedings of the 11th Koli Calling International Conference on Computing Education Research - Koli Calling ’11 (p. 10). New York, New York, USA: ACM Press. https://doi.org/10.1145/2094131.2094134
    Simon. (2011b). Assignment and sequence: why some students can’t recognise a simple swap. In Proceedings of the 11th Koli Calling International Conference on Computing Education Research - Koli Calling ’11 (p. 10). New York, New York, USA: ACM Press. https://doi.org/10.1145/2094131.2094134
    Sirkiä, T., &Sorva, J. (2012). Exploring programming misconceptions. In Proceedings of the 12th Koli Calling International Conference on Computing Education Research - Koli Calling ’12 (pp. 19–28). New York, New York, USA: ACM Press. https://doi.org/10.1145/2401796.2401799
    Sleeman, D., Putnam, R. T., Baxter, J., &Kuspa, L. (1986). Pascal and High School Students: A Study of Errors. Journal of Educational Computing Research, 2(1), 5–23. https://doi.org/10.2190/2XPP-LTYH-98NQ-BU77
    Sorva, J. (2013). Notional machines and introductory programming education. ACM Transactions on Computing Education, 13(2), 1–31. https://doi.org/10.1145/2483710.2483713
    Steven, F. (2014). Predictive Analytics, Data Mining and Big Data. Retrieved from https://link.springer.com/content/pdf/10.1057%2F9781137379283.pdf
    Taber, K. S. (2014). Alternative Conceptions/Frameworks/Misconceptions. In Encyclopedia of Science Education (pp. 1–5). Dordrecht: Springer Netherlands. https://doi.org/10.1007/978-94-007-6165-0_88-2
    Vamvakoussi, X., &Vosniadou, S. (2010). How Many Decimals Are There Between Two Fractions ? Aspects of Secondary School Students’ Understanding of Rational Numbers and Their Notation. Cognition and Instruction, 28(2), 181–209. https://doi.org/10.1080/07370001003676603
    Veerasamy, A. K., D ’souza, D., &Laakso, M.-J. (2016). Identifying Novice Student Programming Misconceptions and Errors From Summative Assessments. Journal of Educational Technology Systems, 45(1), 50–73. https://doi.org/10.1177/0047239515627263
    Wu, X., Kumar, V., Ross Quinlan, J., Ghosh, J., Yang, Q., Motoda, H., …Steinberg, D. (2008). Top 10 algorithms in data mining. Knowledge and Information Systems, 14(1), 1–37. https://doi.org/10.1007/s10115-007-0114-2
    Yang, Q., Wu, X., Domingos, P., Elkan, C., Gehrke, J., Han, J., …Wah, B. W. (2006). 10 CHALLENGING PROBLEMS IN DATA MINING RESEARCH. International Journal of Information Technology & Decision Making, 5(4), 597–604. Retrieved from http://cs.uvm.edu/~icdm/10Problems/10Problems-06.pdf
    Zehetmeier, D., Böttcher, A., Brüggemann-Klein, A., &Thurner, V. (2015). Development of a classification scheme for errors observed in the process of computer programming education. Advances in Higher, 127. Retrieved from https://www.researchgate.net/profile/Josep_Domenech4/publication/305991453_Advances_in_Higher_Education/links/57a88b7008aef20758cbd726.pdf#page=139

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