學生應用視覺化建模與程式工具進行計算建模之分析

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賴柏翰(Po-Han Lai) 查詢紙本館藏

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網路學習科技研究所

論文名稱

學生應用視覺化建模與程式工具進行計算建模之分析
(An Analysis of Computational Modeling in Science with Visual Modeling and Programming Tool)

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摘要(中)

計算思維為現代所有人都應具備的基礎能力，而計算建模能夠幫助學生學習科學與計算思維相關的概念知識。因此，本研究基於建造理論，設計一個具備低門檻與高天花板且使用視覺化程式設計介面的科學計算建模系統與計算思維引導之課程活動，蒐集了28名國三學生於建模活動中所建立的科學計算模型與建模行為，使用質性與量化分析，並加入行為序列分析方式，探討科學計算建模對於學生科學與計算思維的影響，以及分析建模行為和學習成效與建模表現的相關性。研究結果顯示，學生是否能夠建立出計算模型的關鍵步驟為發展問題相關的表徵內容，這一步驟亦影響建模過程中出錯的頻率。此外，學生能否建立較為完整的計算模型則與對於隨時間變化之動態系統的描述、數學與工程概念的轉換、與對於事件識別能力有關。最後，本研究亦發現解構能力較差的學生會傾向依賴參考案例來完成建模。另外，本研究根據研究結果提出未來研究中能改進的部分，像是科學與計算思維試題都應重新設計、教師於課程中可以先帶領學生系統性地分析問題並依照學生程度給予鷹架輔助以及系統上可以加入更多防止錯誤機制與錯誤回復機制。

摘要(英)

Computational thinking is one of the contemporary skills that all modern people should possess, and computational modeling can help students learn scientific and computational thinking concepts. One aim of this study was to understand the effect of scientific computational modeling activities on students’ scientific and computational thinking concepts. The other aim of this study was to clarify the relations between modeling behaviors, learning outcomes and modeling performance. In this study, we designed a scientific computational modeling system with the feature of low-threshold and high-ceiling effect based on constructionism. The system provided a visual-based programming interface along with learning activities guided by computational thinking. A number of twenty-eight 9th graders participated in the learning activity. Students’ scientific computational models and their modeling behaviors during the learning activities were collected. The quantitative analysis, qualitative analysis, and behavior sequence analysis were conducted to answer research questions. The result showed that developing abstract features for the questions is the key step to successfully build models. This step also affects the frequency of errors in the modeling process. Furthermore, students’ abilities to develop more comprehensive computational models were correlated with the abilities to describe a time-varying dynamic system, the conversion between mathematics and engineering concepts, and the abilities to recognize event patterns. Finally, the result also showed that students with poor decomposition abilities tended to rely on references to create the model. Suggestions were provided for future studies. First, both science and computational thinking assessments should be redesigned. Second, teachers can lead students to analyze problems systematically and provide scaffoldings for students with different levels in class. Third, more error prevention and correction mechanisms should be integrated into the modeling system.

關鍵字(中)

★ 計算思維
★ 建造理論
★ 綜效學習
★ 電腦模擬
★ 計算建模
★ 滯後序列分析

關鍵字(英)

★ computational thinking
★ constructionism
★ synergistic learning
★ computer simulation
★ computational modeling
★ lag sequential analysis

論文目次

摘要 i
Abstract ii
致謝 iv
目錄 vi
圖目錄 ix
表目錄 xi
壹、緒論 1
1.1 研究背景與動機 1
1.2 研究目的與問題 2
1.3 名詞解釋 2
1.3.1 計算建模（Computational modeling） 2
1.3.2 電腦模擬（Computer simulation） 3
1.3.3 滯後序列分析（Lag Sequential Analysis, LSA） 3
1.3.4 計算思維（Computational thinking） 3
1.4 論文架構 3
貳、文獻探討 4
2.1 計算思維 4
2.2 計算思維與STEM的綜效學習 5
2.3 計算建模 6
參、系統設計 9
3.1 系統架構與設計理念 9
3.2 系統介紹 10
肆、研究方法 20
4.1 研究流程 20
4.2 教學活動設計 21
4.3 實驗設計 25
4.3.1 填寫實驗同意書 26
4.3.2 前測試題測驗 26
4.3.3 學習活動 26
4.3.4 最終實作測驗 26
4.3.5 後測試題測驗 26
4.4 研究對象 26
4.5 研究工具 27
4.5.1 CoSci平台 27
4.5.2 前後測紙筆測驗 27
4.5.3 系統操作Log檔 28
4.6 資料蒐集與分析 29
4.6.1 科學計算模型 30
4.6.2 活動任務表現 30
4.6.3 活動行為 30
4.6.4 學習成效 33
伍、研究結果與討論 34
5.1 科學概念與CT概念的學習成效 34
5.2 學習活動任務表現 35
5.2.1 任務表現與任務等級分組 35
5.2.2 任務等級質性分析 36
5.3 任務表現等級分析 41
5.3.1 活動行為與任務表現分析 41
5.3.2 學習成效與任務表現分析 49
5.4 活動行為分析 51
5.4.1 活動行為與任務表現相關性 51
5.4.2 活動行為與科學概念相關性 51
5.4.3 活動行為與CT概念相關性 52
5.4.4 監督式活動行為序列分析 54
陸、結論與未來工作 60
6.1 結論 60
6.2 未來工作 62
參考文獻 64
附錄A 科學概念試題 70
附錄B CT概念試題 73

參考文獻

教育部（2018）。運算思維推動計畫。 Retrieved from http://compthinking.csie.n tnu.edu.tw/index.php/intro

Aho, A. V. (2011). Ubiquity symposium: Computation and computational thinking. Ubiquity, 2001(January). Available at http://ubiquity.acm.org/article.cfm?id= 1922682.

Anderson, N. D. (2016). A Call for Computational Thinking in Undergraduate Psychology. Psychology Learning & Teaching, 15(3), 226-234.

Aiken, J. M., Caballero, M. D., Douglas, S. S., Burk, J. B., Scanlon, E. M., Thoms, B. D., & Schatz, M. F. (2013). Understanding student computational thinking with computational modeling. AIP Conference Proceedings, 1513(1), 46.

Bakeman, R., & Gottman, J. M. (1997). Observing Interaction: An Introduction to Sequential Analysis (2nd ed.). Cambridge University Press.

Barr, D., Harrison, J., & Conery, L. (2011). Computational thinking: A digital age skill for everyone. Learning & Leading with Technology, 38(6), 20-23.

Basu, S., Biswas, G., Sengupta, P., Dickes, A., Kinnebrew, J. S., & Clark, D. (2016). Identifying middle school students’ challenges in computational thinking-based science learning. Research and Practice in Technology Enhanced Learning, 11(1), 1-35.

Basu, S., Biswas, G., & Kinnebrew, J. S. (2017). Learner modeling for adaptive scaffolding in a Computational Thinking-based science learning environment. User Modeling and User-Adapted Interaction, 27(1), 5-53.

Basu, S., Biswas, G., & Kinnebrew, J. S. (2017). Learner modeling for adaptive scaffolding in a Computational Thinking-based science learning environment [image]. User Modeling and User-Adapted Interaction, 27(1), 5-53.

Biswas, G., McElhaney, K., Ledeczi, A., Schwartz, D., Conlin, L., Grover, S., Basu, S., Chin, D., Wolf, R., Blari, K. P., Broll, B. (2018). C2STEM. https://www.c2stem.org/

Chang, C. J., Liu, C. C., Wu, Y. T., Chang, M. H., Chiang, S. F., Chiu, B. C., ... & Wu, S. W. (2016). Students′ perceptions on problem solving with collaborative computer simulation. In 24th International Conference on Computers in Education, ICCE 2016 (pp. 166-168). Asia-Pacific Society for Computers in Education.

Csizmadia, A., Curzon, P., Dorling, M., Humphreys, S., Ng, T., Selby, C., & Woollard, J. (2015). Computational thinking: A guide for teachers.

diSessa, A. A. (2000). Changing minds: Computers, learning, and literacy. Cambridge, MA: MIT Press.

Esquembre, F. (2004). Easy Java Simulations: A software tool to create scientific simulations in Java. Computer Physics Communications, 156(2), 199-204.

Fennell, H. W., Lyon, J. A., Magana, A. J., Rebello, S., Rebello, C. M., & Peidrahita, Y. B. (2019). Designing hybrid physics labs: combining simulation and experiment for teaching computational thinking in first-year engineering. 2019 IEEE Frontiers in Education Conference (FIE), 1-8.

Grover, S., & Pea, R. (2013). Computational Thinking in K–12. Educational Researcher, 42(1), 38-43.

Grover, S., Pea, R. (2018). Computational thinking: A competency whose time has come. In: Sentance, S., Barendsen, E., Schulte, C. (Eds.), Computer Science Education: Perspectives on teaching and learning in school (pp. 19-37). London: Bloomsbury Academic

Harel, I., & Papert, S. (1990). Software Design as a Learning Environment. Interactive Learning Environments, 1(1), 1-32.

Harel, I., & Papert, S. (1991). Constructionism. Ablex Publishing Corporation .

Henderson, P. B., Cortina, T. J., Hazzan, O., and Wing, J. M. (2007). Computational thinking. In Proceedings of the 38th ACM SIGCSE Technical Symposium on Computer Science Education - SIGCSE ’07, 195-196.

Hill, L. L., Crosier, S. J., Smith, T. R., & Goodchild, M. (2001). A Content Standard for Computational Models. D-Lib Magazine, 7(6). Available at https://doi.org/ 10.1045/june2001-hill

Hou, H.-T. (2012). Exploring the behavioral patterns of learners in an educational massively multiple online role-playing game (MMORPG). Computers & Education, 58(4), 1225-1233.
Humphreys, P. (2004). Extending Ourselves. Computational Science, Empiricism, and Scientific Method. Oxford Univ. Press, Oxford

Hutchins, N. M., Biswas, G., Maróti, M., Lédeczi, Á., Grover, S., Wolf, R., Blair, K. P., Chin, D., Conlin, L., Basu, S., & McElhaney, K. (2019). C2STEM: a System for Synergistic Learning of Physics and Computational Thinking. Journal of Science Education and Technology, 29(1), 83-100.

Jona, K., Wilensky, U., Trouille, L., Horn, M. S., Orton, K., Weintrop, D., Beheshti, E. (2014). Embedding computational thinking in science, technology, engineering, and math (CT-STEM). Presented at the Future Directions in Computer Science Education Summit Meeting, Orlando

Kelleher, C., & Pausch, R. (2005). Lowering the barriers to programming. ACM Computing Surveys, 37(2), 83-137.

Li, Z.-Z., Cheng, Y.-B., & Liu, C.-C. (2012). A constructionism framework for designing game-like learning systems: Its effect on different learners. British Journal of Educational Technology, 44(2), 208-224.

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.

Michaelson, G. (2018). Microworlds, Objects First, Computational Thinking and Programming. Computational Thinking in the STEM Disciplines, 31-48.

Moors, L., & Sheehan, R. (2017). Aiding the Transition from Novice to Traditional Programming Environments. In Proceedings of the 2017 Conference on Interaction Design and Children, 509-514.

Musaeus, L. H., & Musaeus, P. (2019). Computational Thinking in the Danish High School: Learning Coding, Modeling, and Content Knowledge with NetLogo. In Proceedings of the 50th ACM Technical Symposium on Computer Science Education - SIGCSE ′19, 913-919.

National Research Council. (2010). Report of a Workshop on the Scope and Nature of Computational Thinking. Washington, DC: The National Academies Press.

National Research Council. (2011). Report of a Workshop of Pedagogical Aspects of Computational Thinking. Washington, DC: The National Academies Press.

National Research Council (2012). A framework for K-12 science education: practices, crosscutting concepts, and core ideas. National Academies Press, Washington, DC

Neves, R. G., Silva, J. C., & Teodoro, V. D. (2013). Integrating Computational Modelling in Science, Technology, Engineering and Mathematics Education. Educational interfaces between mathematics and industry, New ICMI Study Series, 16, 375-383.

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

Rodríguez-Martínez, J. A., González-Calero, J. A., & Sáez-López, J. M. (2019). Computational thinking and mathematics using Scratch: an experiment with sixth-grade students. Interactive Learning Environments, 28(3), 316-327.

Sáez-López, J.-M., Román-González, M., & Vázquez-Cano, E. (2016). Visual programming languages integrated across the curriculum in elementary school: A two year case study using “Scratch” in five schools. Computers & Education, 97, 129-141.

Selby, C., & Woollard, J. (2013). Computational Thinking: The Developing Definition. In Proceedings of the 45th ACM Technical Symposium on Computer Science Education - SIGCSE ’14

Shute, V. J., Sun, C., & Asbell-Clarke, J. (2017). Demystifying computational thinking. Educational Research Review, 22, 142-158.

Taber, C. S., & Timpone, R. J. (1996). Computational Modeling (Quantitative Applications in the Social Sciences) (1st ed.). SAGE Publications, Inc.

Tucker, A. (2003). A Model Curriculum for K-12 Computer Science: Final Report of the ACM K-12 Task Force Curriculum Committee. New York, NY: CSTA

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.

Weintrop, D., & Wilensky, U. (2015). To block or not to block, that is the question: Students′ perceptions of blocks-based programming. In Proceedings of the 14th International Conference on Interaction Design and Children - IDC 15, 199-208.

Weintrop, D., & Wilensky, U. (2017). Comparing Block-Based and Text-Based Programming in High School Computer Science Classrooms. ACM Transactions on Computing Education, 18(1), 1-25.

Wilensky, U., Brady, C. E., & Horn, M. S. (2014). Fostering computational literacy in science classrooms. Communications of the ACM, 57(8), 24-28.

Wilensky, U., & Reisman, K. (2006). Thinking Like a Wolf, a Sheep, or a Firefly: Learning Biology Through Constructing and Testing Computational Theories—An Embodied Modeling Approach. Cognition and Instruction, 24(2), 171-209.

Wilkerson-Jerde, M. H. (2013). Construction, categorization, and consensus: student generated computational artifacts as a context for disciplinary reflection. Educational Technology Research and Development, 62(1), 99-121.

Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33-3.

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. Available at http://socialissues.cs.toronto.edu/201 4/01/computational-thinking/

Winsberg, E. (2013). Computer Simulations in Science. Retrieved from https://plato.stanford.edu/entries/simulations-science/?utm_source=feedly

Zhang, N., & Biswas, G. (2018). Understanding Students’ Problem-Solving Strategies in a Synergistic Learning-by-Modeling Environment. Lecture Notes in Computer Science, 405-410.

指導教授

劉晨鐘(Chen-Chung Liu)

審核日期

2021-1-18

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