研究生: |
謝欣玲 Hsieh, Hsin-Ling |
---|---|
論文名稱: |
基於科學運算之運算思維導向程式設計教學 Teaching Programming to Science Majors by Modelling |
指導教授: |
林育慈
Lin, Yu-Tzu |
口試委員: | 吳正己 張凌倩 |
口試日期: | 2020/07/27 |
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: | 97 |
中文關鍵詞: | 運算思維 、科學運算 、程式設計教學 、STEM |
英文關鍵詞: | Computational Thinking, Scientific Computing, Programming Instruction, STEM |
DOI URL: | http://doi.org/10.6345/NTNU202100416 |
論文種類: | 學術論文 |
相關次數: | 點閱:192 下載:0 |
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 |
Armoni, M., Meerbaum-Salant, O., &Ben-Ari, M. (2015). From scratch to “Real” programming. ACM Transactions on Computinig Education, 14(4). https://doi.org/10.1145/2677087
Aydin, G. (2020). Prerequisites for elementary school teachers before practicing STEM education with students: A case study. Eurasian Journal of Educational Research, 2020(88), 1–40. https://doi.org/10.14689/ejer.2020.88.1
Barr, V., &Stephenson, C. (2011). Bringing computational thinking to K-12: What is involved and what is the role of the computer science education community? ACM Inroads, 2(1), 48–54. https://doi.org/10.1145/1929887.1929905
Bennedsen, J., &Caspersen, M. E. (2008). Abstraction Ability as an Indicator of Success for Learning Computing Science ? 15–25.
Bennedsen, J., &Caspersen, M. E. (2019). Failure Rates in Introductory Programming-12 Years Later (Vol. 10).
Brewe, E. (2008). Modeling theory applied: Modeling Instruction in introductory physics. American Journal of Physics, 76(12), 1155–1160. https://doi.org/10.1119/1.2983148
Burrows, A. C., Breiner, J. M., Keiner, J., &Behm, C. (2014). Biodiesel and integrated STEM: Vertical alignment of high school biology/biochemistry and chemistry. Journal of Chemical Education, 91(9), 1379–1389. https://doi.org/10.1021/ed500029t
Bybee, B. R. W. (2010). Advancing_STEM_Education_A_20. (September 2010), 30–36.
Chonacky, N., &Winch, D. (2008). Integrating computation into the undergraduate curriculum: A vision and guidelines for future developments. American Journal of Physics, 76(4), 327–333. https://doi.org/10.1119/1.2837811
Chonacky, N., Winch, D., &Winch, D. (2017). Integrating computation into the undergraduate curriculum : A vision and guidelines for future developments Integrating computation into the undergraduate curriculum : 327(2008). https://doi.org/10.1119/1.2837811
Computing, S. (2007). Python for Scientific Computing. 10–20.
DeJong, T., &VanJoolingen, W. R. (1998). Scientific discovery learning with computer simulations of conceptual domains. Review of Educational Research, 68(2), 179–201. https://doi.org/10.3102/00346543068002179
English, L. D. (2017). Advancing Elementary and Middle School STEM Education. International Journal of Science and Mathematics Education, 15, 5–24. https://doi.org/10.1007/s10763-017-9802-x
Fan, S. C., &Yu, K. C. (2017). How an integrative STEM curriculum can benefit students in engineering design practices. International Journal of Technology and Design Education, 27(1), 107–129. https://doi.org/10.1007/s10798-015-9328-x
Gilbert, J. K., Boulter, C. J., &Elmer, R. (2000). Positioning Models in Science Education and in Design and Technology Education. Developing Models in Science Education, (1982), 3–17. https://doi.org/10.1007/978-94-010-0876-1_1
Gomes, A., &Mendes, A. J. (2007). Learning to program - difficulties and solutions | Academic Conference Paper. (May). Retrieved from https://www.researchgate.net/publication/228328491_Learning_to_program_-_difficulties_and_solutions
Halloun, I. A. (2007). Mediated modeling in science education. In Science and Education (Vol. 16). https://doi.org/10.1007/s11191-006-9004-3
Harrison, A. G., &Treagust, D. F. (2000). A typology of school science models. International Journal of Science Education, 22(9), 1011–1026. https://doi.org/10.1080/095006900416884
Herbert, B. E. (2003). 2003_The role of scaffolding student metacognition in developing mental models of complex , Earth and environmental systems. Learning, 1–7.
Jenkins, T. (2002). O d l p. 53–58.
Kalelioʇlu, F. (2015). A new way of teaching programming skills to K-12 students: Code.org. Computers in Human Behavior, 52, 200–210. https://doi.org/10.1016/j.chb.2015.05.047
Lahtinen, E., Ala-Mutka, K., &Järvinen, H.-M. (2005). A study of the difficulties of novice programmers. ACM SIGCSE Bulletin, 37(3), 14. https://doi.org/10.1145/1151954.1067453
Li, Y. (2016). Teaching programming based on Computational Thinking. Proceedings - Frontiers in Education Conference, FIE, 2016-November. https://doi.org/10.1109/FIE.2016.7757408
Lijnse, P. (2014). Models of / for Teaching Modeling. (November).
Linn, M. C., &Dalbey, J. (2010). Cognitive consequences of Programming Instruction : Instruction , Access , and Ability Cognitive Consequences of Programming Instruction : Instruction , Access , and Ability. 1520(November 2014), 37–41. https://doi.org/10.1207/s15326985ep2004
Mannila, L., Dagiene, V., Demo, B., Grgurina, N., Mirolo, C., Rolandsson, L., &Settle, A. (2014). Computational thinking in K-9 education. ITiCSE-WGR 2014 - Working Group Reports of the 2014 Innovation and Technology in Computer Science Education Conference, 1–29. https://doi.org/10.1145/2713609.2713610
McPadden, D., &Brewe, E. (2017). Impact of the second semester University Modeling Instruction course on students’ representation choices. Physical Review Physics Education Research, 13(2), 1–15. https://doi.org/10.1103/PhysRevPhysEducRes.13.020129
Morrison, J. S. (2006). STEM_Articles.pdf (p. 20). p. 20.
Özmen, B., &Altun, A. (2014). Undergraduate Students’ Experiences in Programming: Difficulties and Obstacles. Turkish Online Journal of Qualitative Inquiry, 5(3). https://doi.org/10.17569/tojqi.20328
Pérez-Marín, D., Hijón-Neira, R., Bacelo, A., &Pizarro, C. (2020). Can computational thinking be improved by using a methodology based on metaphors and scratch to teach computer programming to children? Computers in Human Behavior, 105(December 2018), 105849. https://doi.org/10.1016/j.chb.2018.12.027
Pettini, F., Mastromarco, P., &Pettini, P. (2006). [Antibacterial activity of endodontic medications]. Minerva Stomatologica, 47(7–8), 309–314. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/9793365
Prinsley, R., &Baranyai, K. (2015). STEM Skills in the Workforce : What Do Employers Want ? Office of the Chief Scientist, Government of Australia, (9), 1–4. https://doi.org/10.13140/RG.2.2.12120.60167
Psycharis, S., Kalovrektis, K., Sakellaridi, E., &Korres, K. (2018). Unfolding the Curriculum : Physical Computing , Computational Thinking and Computational Experiment in STEM ’ s Transdisciplinary Approach. 19–24.
Report of a Workshop of Pedagogical Aspects of Computational Thinking Committee for the Workshops on Computational Thinking ; National. (2011).
Román-González, M., Pérez-González, J. C., Moreno-León, J., &Robles, G. (2018). Can computational talent be detected? Predictive validity of the Computational Thinking Test. International Journal of Child-Computer Interaction, 18, 47–58. https://doi.org/10.1016/j.ijcci.2018.06.004
Romero, M., Lepage, A., &Lille, B. (2017). Computational thinking development through creative programming in higher education. International Journal of Educational Technology in Higher Education, 14(1). https://doi.org/10.1186/s41239-017-0080-z
Sanders, B. M. (2009). I 20 i 20. Integrative STEM Education: Primer, 2, 20–26.
Selby, C. C., &Woollard, J. (2010). Computational Thinking : The Developing Definition.
Seymour Papert. (1980). Papert_Mindstorms.Pdf.
Sinsa, P. H. M., Savelsbergh, E. R., &VanJoolingen, 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. https://doi.org/10.1080/09500690500206408
Svoboda, J., &Passmore, C. (2013). The Strategies of Modeling in Biology Education. Science and Education, 22(1), 119–142. https://doi.org/10.1007/s11191-011-9425-5
Swaid, S. I. (2015). Bringing computational thinking to STEM education. Procedia Manufacturing, 3(Ahfe), 3657–3662. https://doi.org/10.1016/j.promfg.2015.07.761
Ting, C., Chia, W., Chang, J., Hua, M., Shih, C., &Fan, H. (2018). The learning analytics of model ‑ based learning facilitated by a problem ‑ solving simulation game. Instructional Science, (0123456789). https://doi.org/10.1007/s11251-018-9461-5
Tsai, C. (2019). Computers in Human Behavior Improving students ’ understanding of basic programming concepts through visual programming language : The role of self-e ffi cacy. Computers in Human Behavior, 95(October 2018), 224–232. https://doi.org/10.1016/j.chb.2018.11.038
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. https://doi.org/10.1007/s10956-015-9581-5
Weintrop, D., &Wilensky, U. (2019). Computers & Education Transitioning from introductory block-based and text-based environments to professional programming languages in high school computer science classrooms. Computers & Education, 142(July), 103646. https://doi.org/10.1016/j.compedu.2019.103646
White, K. P., &Ingalls, R. G. (2015). We might divide applications of simulation broadly into two categories . The first includes so- called man-in-the-loop simulations used for training and / or entertainment . Many professionals hone their skills and learn emergency procedures in simulated . 1741–1755.
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. https://doi.org/10.1098/rsta.2008.0118
Wing, Jeannette M. (2006). Computational Thinking. 49(3), 33–35.