研究生: |
李敏瑄 Lee, Min-Hsuan |
---|---|
論文名稱: |
基於建模之科學運算課程設計與評估 The Design and Implementation of Computational Science Instruction Based on Modelling |
指導教授: |
林育慈
Lin, Yu-Tzu |
口試委員: | 吳正己 張凌倩 林育慈 |
口試日期: | 2021/07/26 |
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: | 2021 |
畢業學年度: | 109 |
語文別: | 中文 |
論文頁數: | 75 |
中文關鍵詞: | STEM 、科學運算 、建模教學 、程式設計教學 |
英文關鍵詞: | STEM, Computational Science, Modeling-based Instruction, Programming Instruction |
研究方法: | 實驗設計法 、 準實驗設計法 、 半結構式訪談法 |
DOI URL: | http://doi.org/10.6345/NTNU202101147 |
論文種類: | 學術論文 |
相關次數: | 點閱:128 下載:15 |
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本研究的目的是探討基於建模的科學運算課程是否能提升學生的科學問題解決能力、建模品質與學習態度之成效。因此開發了科學與程式設計教材。另外,為了探討基於建模的教學與傳統講述式教學的對學生的影響,所以開發科學程式設計建模輔助平台,在這個平台中有相對應的程式設計建模歷程(現象描述、資料建模、流程建模、程式化以及觀察與除錯)之功能。實驗組學生在利用程式解決科學問題前,必須到此平台回答與科學問題相關的引導題目後才能開始編寫程式進行解題。本研究的實驗結果發現:
一、 實驗組之低先備能力組學生在歷經基於建模的科學算課程後,其科學問題解決能力,與控制組低先備能力組學生相比有較大的進步幅度。這代表了建模教學有助於課前科學問題解決能力與程式能力較差的學生提升他們的科學問題解決能力。
二、 實驗組學生在經歷基於建模的科學算課程後,其建模品質之進步幅度與控制組學生相比有較大的進步。另外,實驗組之低先備能力組學生的建模品質之進步幅度與控制組低先備能力組學生相比是有顯著差異的。但實驗組高先備能力組學生與控制組高先備能力組學生之建模品質並無差異。這表示建模教學有助於學生提升他們的建模品質。
三、 在學習態度上,實驗組與控制組學生的進步幅度並無差異。但在態度問卷後測中的開放是問答題和半結構式訪談中實驗組學生對於課程較高的評價。
本次研究可以給與後續欲發展科學運算課程與建模教學的研究者做為參考。
This study aims to develop a modelling-based programming instruction for science majors and explore its effectiveness on learning. A modelling-based learning platform was also developed to assist students in solving scientific problems by programming. Students were guided by five steps: phenomenon description, data modelling, algorithmic modelling, coding, and debugging. An empirical study was conducted to examine the effectiveness of the modelling-based programming instruction by comparing students’ learning outcomes (including computational science problem solving abilities, modelling qualities, and learning attitude) when in the proposed methodology with the traditional methodology. The research findings are as follows:
1. The students with lower performance in the pre-test of science and programing in the experimental group had greater improvement in computational science problem solving abilities, than those in the control group.
2. The experimental group had greater improvement in data modelling and algorithmic modelling than the control group. And the students with lower performance in the pre-test of science and programing the experimental group had greater improvement in phenomenon description, data modelling, and algorithmic modelling than those in the control group. But there was no difference in modelling qualities between the students with higher performance in the pre-test of science and programing in the experimental group and those in the control group.
3. Regarding learning attitude, there was no difference between the experimental group and the control group based on the statistical results. However, the experimental group showed more positive attitude toward the instruction in the questionnaire and interview results.
英文部分
Abdullah, S., & Shariff, A. (2008). The effects of inquiry-based computer simulation with cooperative learning on scientific thinking and conceptual understanding of gas laws. Eurasia Journal of Mathematics, Science and Technology Education, 4(4), 387-398.
Abelson, H., Sussman, G. J., & Sussman, J. (1996). Structure and interpretation of computer programs. Justin Kelly.
Angell, C., Kind, P. M.,Henriksen, E. K., & Guttersrud, Ø . (2008). An empirical-mathematical modelling approach to upper secondary physics. Physics Education, 43(3), 256.
Areias, C., & Mendes, A. (2007, June). A tool to help students to develop programming skills. In Proceedings of the 2007 international conference on Computer systems and technologies (pp. 1-7).
Agarwal, K. K., & Agarwal, A. (2005). Python for CS1, CS2 and beyond. Journal of Computing Sciences in Colleges, 20(4), 262–270.
Brewe, E. (2008). Modeling theory applied: Modeling Instruction in introductory physics. American Journal of physics, 76(12), 1155-1160.
Brewe, E., & Sawtelle, V. (2018). Modelling instruction for university physics: examining the theory in practice. European Journal of Physics, 39(5), 054001.
Barr, V. & Stephenson, C., “Bringing Computational Thinking to K-12: What is Involved and What is the Role of the Computer Science Education Community?, ACM Inroads, Vol. 2(1), 48-54, 2011.
Chang, K.-E., Chen, Y.-L., Lin, H.-Y., & Sung, Y.-T. (2008). Effects of learning support in simulation-based physics learning. Computers & Education, 51(4), 1486–1498.
Chang, K.-E., Chen, Y.-L., Lin, H.-Y., & Sung, Y.-T. (2008). Effects of learning support in simulation-based physics learning. Computers & Education, 51(4), 1486–1498.
Chabay, R., & Sherwood, B. (2008). Computational physics in the introductory calculus-based course. American Journal of Physics, 76(4), 307-313.
Durak, H. Y., Yilmaz, F. G. K., & Yilmaz, R. (2019). Computational Thinking, Programming Self-Efficacy, Problem Solving and Experiences in the Programming Process Conducted with Robotic Activities. Contemporary Educational Technology, 10(2), 173-197.
English, L. D. (2017). Advancing elementary and middle school STEM education. International Journal of Science and Mathematics Education, 15(1), 5-24.
Esteves, M., Fonseca, B., Morgado, L., & Martins, P. (2011). Improving teaching and learning of computer programming through the use of the Second Life virtual world. British Journal of Educational Technology, 42(4), 624-637.
Einhorn, S., “Micro-Worlds, Computational Thinking, and 21st Century Learning”, Logo Computer Systems Inc, White Paper, 2012.
Forbes, C. T., Cisterna, D., Bhattacharya, D., & Roy, R. (2019). Modeling Elementary Students' Ideas about Heredity: A Comparison of Curricular Interventions. The American Biology Teacher, 81(9), 626-635.
Gilbert,J.K.(2004). Models and modelling: Routes to more authentic science education. International Journal of Science and Mathematics Education, 2(2), 115-130.
Greca, I. M., & Moreira, M. A. (2002). Mental, physical, and mathematical models in the teaching and learning of physics. Science Education, 86(1), 106–121.
Gilbert, J. K., Boulter, C. J., & Elmer, R. (2000). Positioning models in science education and in design and technology education. In Developing models in science education (pp. 3-17). Springer, Dordrecht.
García-Peñalvo, F. J., & Mendes, A. J. (2018). Exploring the computational thinking effects in pre-university education.
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., & Pea, R. (2018). Computational thinking: A competency whose time has come (p. 19). Computer Science Education: Perspectives on Teaching and Learning in School.
Harrison,A.G.,& Treagust,D.F. (1996). Secondary students’ mental models of atoms and molecules: Implications for teaching chemistry. Science Education, 80(5), 509-534
Huppert, J., Lomask, S. M., & Lazarowitz, R. (2002). Computer simulations in the high school: Students' cognitive stages, science process skills and academic achievement in microbiology. International Journal of Science Education, 24(8), 803-821.
Hestenes, D. (1987). Towards a modeling theory of physics instruction. American Journal of Physics, 55(5), 440-454
Hestenes, D. (1997, March). Modeling methodology for physics teachers. In AIP conference proceedings (Vol. 399, No. 1, pp. 935-958). AIP.
Halloun, I. A., & Hestenes, D. (1987). Modeling instruction in mechanics. American Journal of Physics, 55(5), 455-462.
Hutchins, N. M., Biswas, G., Maróti, M., Lédeczi, Á., Grover, S., Wolf, R., ... & McElhaney, K. (2020). C2STEM: A system for synergistic learning of physics and computational thinking. Journal of Science Education and Technology, 29(1), 83-100.
Ismail, M. N., Ngah, N. A., & Umar, I. N. (2010). Instructional strategy in the teaching of computer programming: A need assessment analyses. TOJET: The Turkish Online Journal of Educational Technology, 9(2).
Jimoyiannis, A., & Komis, V. (2001). Computer simulations in physics teaching and learning: a case study on students’ understanding of trajectory motion. Computers & Education, 36(2), 183–204.
Jenkins, T. (2002). On the difficulty of learning to program. In Proceedings of the 3rd Annual Conference of the LTSN Centre for Information and Computer Sciences (Vol. 4, pp. 53–58).
Johnson, F., McQuistin, S., & O'Donnell, J. (2020, January). Analysis of Student Misconceptions using Python as an Introductory ProgrammingLanguage. In Proceedings of the 4th Conference on Computing Education Practice 2020 (pp. 1-4).
Justi, R. S., & Gilbert, J. K. (2002). Modelling, teachers’ views on the nature of modelling, and implications for the education of modellers. International Journal of Science Education, 24(4), 369–387. https://doi.org/10.1080/09500690110110142
Lahtinen, E., Ala-Mutka, K., & Järvinen, H.-M. (2005). A study of the difficulties of novice programmers. In ACM SIGCSE Bulletin (Vol. 37, pp. 14–18). ACM
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.
Lesh, R., & English, L. D. (2005). Trends in the evolution of models & modeling perspectives on mathematical learning and problem solving. ZDM, 37(6), 487-489.
Marlino, M. R. (2001). Visualization in undergraduate geoscience science education: what do we really know?. Computers and Geosciences, 27, 497-498
Morrison, J. S. (2006). Attributes of STEM education: The students, the academy, the classroom. TIES STEM Education Monograph Series. Baltimore: Teaching Institute for Excellence in STEM. Retrieved from
Magana, A. J., Marepalli, P. & Clark, J. V., “Work in Progress – Integrating Computational and Engineering Thinking through Online Design and Simulation of Multidisciplinary Systems”, 41st ASEE/IEEE Frontiers in Education Conference, October 12-15, 2011.
Moursund, D. (2009). Computational Thinking. IAE-pedia. Available online at http://iaepedia.org/Computational_Thinking. Accesed August 8, 2010.
Neves, R. G., Neves, M. C., & Teodoro, V. D. (2013). Modellus: Interactive computational modelling to improve teaching of physics in the geosciences. Computers & Geosciences, 56, 119-126.
Oh, P. S., & Oh, S. J. (2011). What teachers of science need to know about models: An overview. International Journal of Science Education, 33(8), 1109-1130.
Ö zmen, B., & Altun, A. (2014). Undergraduate Students’ Experiences in Programming: Difficulties and Obstacles. Turkish Online Journal of Qualitative Inquiry, 5(3), 1–27.
Prosser, M. (1983). Relationship Between the Cognitive Abilities of a Group of Tertiary Physics Students and the Cognitive Requirements of their Textbook. Science Education, 67(1), 75-83.
Psycharis, S. (2013). Examining the effect of the computational models on learning performance, scientific reasoning, epistemic beliefs and argumentation: An implication for the STEM agenda. Computers & Education, 68, 253-265.
Papert, S. (1991). Situating constructionism. In I. Harel & S. Papert (Eds.), Constructionism (pp. 193–206). Westport, CT: Ablex Publishing.
Qin, H. (2009, March). Teaching computational thinking through bioinformatics to biology students. In Proceedings of the 40th ACM technical symposium on Computer science education (pp. 188-191).
Redish, E. F., & Wilson, J. M. (2000). Student programming in the introductory physics course: MUPPET.
Robins, A., Rountree, J., & Rountree, N. (2003). Learning and teaching programming: A review and discussion. Computer science education, 13(2), 137-172.
Román-González, M., Pérez-González, J. C., & Jiménez-Fernández, C. (2017). Which cognitive abilities underlie computational thinking? Criterion validity of the Computational Thinking Test. Computers in Human Behavior, 72, 678-691.
Redish, E. F., & Wilson, J. M. (1993). Student programming in the introductory physics course: MUPPET. American Journal of Physics, 61(3), 222–232.
Sins, P. H., Savelsbergh, E. R., & van Joolingen, W. R. (2005). The Difficult Process of Scientific Modelling: An analysis of novices' reasoning during computer‐based modelling. International Journal of Science Education, 27(14), 1695-1721.
Sanders, M. E. (2008). Stem, stem education, stemmania. Retrieved from
Svoboda, J., & Passmore, C. (2013). The strategies of modeling in biology education. Science & Education, 22(1), 119-142.
Shen, J., Lei, J., Chang, H. Y., & Namdar, B. (2014). Technology-enhanced, modeling-based instruction (TMBI) in science education. In M. Spector, M. D. Merrill, J. Elen, & M. J. Bishop (Eds.)
Selby, C., & Woollard, J. (2013). Computational thinking: the developing definition. Retrieved from http://eprints.soton.ac.uk/356481
Sanford, J. F., & Naidu, J. T. (2017). Mathematical modeling and computational thinking. Contemporary Issues in Education Research (CIER), 10(2), 158-168.
Sarjoughian, H. S., & Zeigler, B. P. (1996, December). Abstraction mechanisms in discrete-event inductive modeling. In Proceedings Winter Simulation Conference (pp. 748-755). IEEE.
Soloway, E. (1993). Should we teach students to program? Communications of the ACM, 36(10), 21–25.
Taub, R., Armoni, M., Bagno, E., & Ben-Ari, M. M. (2015). The effect of computer science on physics learning in a computational science environment. Computers & Education, 87, 10-23.
van Joolingen, W. (2004, August). Roles of modeling in inquiry learning. In IEEE International Conference on Advanced Learning Technologies, 2004. Proceedings. (pp. 1096-1097). IEEE.
Voskoglou, M. G., & Buckley, S. (2012). Problem solving and computational thinking in a learning environment. arXiv preprint arXiv:1212.0750.
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 of the Royal Society A: Mathematical, Physical and Engineering Sciences, 366(1881), 3717-3725.
Xinogalos, S. (2012). An evaluation of knowledge transfer from microworld programming to conventional programming. Journal of Educational Computing Research, 47(3), 251-277.
Zaini, Z. H., Arshad, N. I., Singh, B. S., Aszemi, N. M., Anggoro, S., & Hawanti, S. (2020). A Study on Student Attitudes in Learning Programming using Physical Computing. Dinamika Jurnal Ilmiah Pendidikan Dasar, 12(1), 25-32
Zawojewski, J. (2013). Problem solving versus modeling. In Modeling students' mathematical modeling competencies (pp. 237-243). Springer, Dordrecht.
中文部分
洪振方, 莊敏雄, & 宋國城. (2011). 建模教學對國小學童的模型認知及地質概念理解之影響. 科學教育學刊, 19(4), 309-333.