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姓名	温采婷(Cai-Ting Wen)  查詢紙本館藏	畢業系所	網路學習科技研究所
論文名稱	系統思維框架下的計算建模學習與教學法 (The learning of computational modeling based on systems thinking pedagogical framework)
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摘要(中)	科學建模已被視為科學教育與實踐不可分割的基本學習目標。將計算工具融入科學實踐提供學生與科學模型互動、探索並建構科學現象的機會，使學習者能有效參與科學建模並對科學現象有更深的理解。然而，由於計算建模過程的複雜性涉及對特定領域知識的理解以及應用計算工具實踐的能力，鷹架引導與教學法的介入不可或缺。因此，本研究提出了一個基於系統思維理論之教學法框架，藉由結構化地分析與辨識複雜系統中的重要組成元素以及元素間的交互動態與靜態關係，引導學生運用計算工具建立複雜場景的科學模型。本研究並結合演示法與提問法兩種教學鷹架引導學生藉由系統思維建立模型，並觀察不同鷹架輔助是否影響學生在建模活動中的計算建模表現和行為模式。受試者為兩班10 年級學生參加為期九週的計算建模課程，兩班學生被隨機分配到觀察提示組(N=29)和演示組(N=36)。本研究蒐集學生紙本測驗的回答和於計算建模平台實作建模測驗之成品與行為，並進一步評估計算建模表現、行為策略。結果顯示計算建模框架是引導學生學習如何建構科學模型並進一步轉化為計算形式的有效方法。然而，研究結果也發現學生在兩種教學法下展現不同程度之模型品質和建模策略。此外，我們發現先備知識對學生的建模策略和建模表現也有不同程度的影響。
摘要(英)	Scientific modeling has been advocated as an essential learning objective of science education. Integrating computational tools with science practice opens up a chance for learners to engage in effective scientific modeling as it allows students to interactively manipulate the scientific models and explore the obscure mechanisms behind the scientific phenomena. However, due to the complexity of the model construction process, scaffolding and effective intervention are needed to provide students with guidance in computational modeling activities. This study thus proposed a systems thinking pedagogical framework based on the theory to provide a guideline for educators and learners to learn how to construct a computational model. The framework was composed of five modules aiming to raise students’ awareness of identifying critical elements in the system and the identification of the interconnections of the elements in the systems during modeling. Demonstration and question prompt scaffolding were applied to guide students to construct computational models under the computational modeling framework to reveal whether the pedagogical scaffolding impacted their computational modeling performance and action during modeling activities. Two intact classes of 10th grade students participating in the 9-week computational modeling program were randomly assigned to the question-prompt group (n = 29) and demonstration group (n = 36). The computational modeling performance was collected through paper-based tests and a hands-on modeling test which asked students to construct a computational model on the platform. Students’ answers, product of computational modeling, and their actions were then evaluated and used for further analysis. The results showed that implementing such a framework is an effective approach which guides students to learn how to construct scientific models and further transfer into computational forms. However, this study also found that the students who learnt under the two types of scaffolding demonstrated different learning outcomes in the hands-on test and modeling strategies. Besides, we found that prior knowledge also had a different level of impact on the students’ modeling strategies and modeling performance.
關鍵字(中)	★ 計算建模 ★ 科學建模 ★ 系統思維	關鍵字(英)	★ Computational modeling ★ Scientific modeling ★ Systems thinking
論文目次	ACKNOWLEDGEMENTS I 摘要 II ABSTRACT III TABLE OF CONTENTS V LIST OF FIGURES VII LIST OF TABLES IX Chapter 1. Introduction 1 1.1 Background and Motivation 1 1.2 Research questions 4 1.3 Thesis organization 5 Chapter 2. Literature Review 6 2.1 Scientific modeling 6 2.2 Computational modeling 8 2.3 Scaffolding consideration of computational modeling with systems thinking framework 10 2.4 Learning analytics in scientific modeling 13 Chapter 3. Systems thinking framework 15 Chapter 4. Methods 21 4.1 Participants 21 4.2 Procedural 21 4.3 The CoSci computational modeling platform 23 4.4 Computational modeling framework and scaffolding 27 4.4.1 Demonstration 29 4.4.2 Question prompt approach 30 4.4.3 Summary of the two approaches 32 4.5 Data Collection 33 4.5.1 Paper-based modeling test 33 4.5.2 Hands-on modeling test 40 4.5.3 Computational modeling behavior 43 4.6 Data analysis 45 Chapter 5. Findings 47 5.1 Computational modeling under the systems thinking framework 47 5.2 Computational modeling performance under question prompt and demonstration scaffolding 49 5.3 Computational modeling actions under question prompt and demonstration scaffolding 51 5.4 Computational modeling action pattern under question prompt and demonstration scaffolding 54 5.5 Considering the impact of prior knowledge on computational modeling learning moderated by scaffolding 63 5.5.1 How does the prior knowledge influence the relations between scaffolding and modeling performance? 63 5.5.2 Summary of the factors impacting students’ learning performance 71 Chapter 6. Discussion 72 6.1 Computational modeling learning under the systems thinking framework 72 6.2 Computational modeling learning under question prompt and demonstration scaffolding 73 6.3 Computational modeling actions and patterns 75 6.4 The impact of prior knowledge on computational modeling learning 78 Chapter 7. Conclusion 80 7.1 Computational modeling learning with the systems thinking framework 80 7.2 Limitations and future works 83 Reference 84 Appendix A – Paper-based test 99 Appendix B – Hands-on test 102
參考文獻	Arnold, R. D., & Wade, J. P. (2015). A definition of systems thinking: A systems approach. Procedia computer science, 44, 669-678. Assaraf, O. B. Z., & Orion, N. (2005). Development of system thinking skills in the context of earth system education. Journal of Research in Science Teaching: The Official Journal of the National Association for Research in Science Teaching, 42(5), 518-560. Atkeson, C. G., & Schaal, S. (1997, July). Robot learning from demonstration. In ICML (Vol. 97, pp. 12-20). Aydogan Yenmez, A., Erbas, A. K., Cakiroglu, E., Cetinkaya, B., & Alacaci, C. (2018). Mathematics teachers’ knowledge and skills about questioning in the context of modeling activities. Teacher Development, 22(4), 497-518. Bain, C., Anton, G., Horn, M., & Wilensky, U. (2020). Back to computational transparency: Co-designing with teachers to integrate computational thinking in science classrooms. In Proceedings of the 14th International Conference of the Learning Sciences, Nashville, TN. ISLS. Bakeman, R., & Gottman, J. M. (1997). Observing interaction: An introduction to sequential analysis. Cambridge university press. Bamberger, Y. M., & Davis, E. A. (2013). Middle-school science students’ scientific modelling performances across content areas and within a learning progression. International Journal of science education, 35(2), 213-238. 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., Kinnebrew, J. S., & Biswas, G. (2014). Assessing student performance in a computational-thinking based science learning environment. In International conference on intelligent tutoring systems (pp. 476-481). Springer, Cham. Baum, L. E., & Petrie, T. (1966). Statistical inference for probabilistic functions of finite state Markov chains. The annals of mathematical statistics, 37(6), 1554-1563. Becker, M., McElvany, N., & Kortenbruck, M. (2010). Intrinsic and extrinsic reading motivation as predictors of reading literacy: A longitudinal study. Journal of Educational psychology, 102(4), 773. Bertalanffy, L. V. (1968). General system theory: Foundations, development, applications. G. Braziller. Bielik, T., Finnie, K., Peek-Brown, D., Klager, C., Touitou, I., Schneider, B., & Krajcik, J. (2022). High School Teachers’ Perspectives on Shifting towards Teaching NGSS-Aligned Project Based Learning Curricular Units. Journal of Science Teacher Education, 33(4), 413-434. Bienkowski, M., Snow, E., Rutstein, D., & Grover, S. (2015). Assessment design patterns for computational thinking practices in secondary computer science. Menlo Park: SRI. Retrieved February, 2, 2021. Billard, A., Calinon, S., Dillmann, R., & Schaal, S. (2008). Robot programming by demonstration. In Springer handbook of robotics (pp. 1371-1394). Springer, Berlin, Heidelberg. Blikstein, P., & Wilensky, U. (2004). MaterialSim: An agent-based simulation toolkit for learning materials science. In International Conference on Engineering Education, Gainesville, Florida, USA. Blikstein, P., & Wilensky, U. (2009). An atom is known by the company it keeps: A constructionist learning environment for materials science using agent-based modeling. International Journal of Computers for Mathematical Learning, 14(2), 81-119. Bloom, B. S. (1956). Taxonomy of educational objectives: the classification of educational goals. London. Brennan, K., & Resnick, M. (2012). New frameworks for studying and assessing the development of computational thinking. In Proceedings of the 2012 annual meeting of the American educational research association, Vancouver, Canada (Vol. 1, p. 25). Brown, J. S., Collins, A., & Duguid, P. (1989). Situated cognition and the culture of learning. Educational researcher, 18(1), 32-42. Campbell, T., Oh, P. S., Maughn, M., Kiriazis, N., & Zuwallack, R. (2015). A review of modeling pedagogies: Pedagogical functions, discursive acts, and technology in modeling instruction. Eurasia Journal of Mathematics, Science and Technology Education, 11(1), 159-176. Chang, C. J., Chang, M. H., Liu, C. C., Chiu, B. C., Fan Chiang, S. H., Wen, C. T., ... & Chai, C. S. (2017). An analysis of collaborative problem‐solving activities mediated by individual‐based and collaborative computer simulations. Journal of Computer Assisted Learning, 33(6), 649-662. Chang, H. Y. (2022). Science teachers’ and students’ metavisualization in scientific modeling. Science Education, 106(2), 448-475. Chin, C. (2006). Classroom interaction in science: Teacher questioning and feedback to students’ responses. International journal of science education, 28(11), 1315-1346. Chiu, M. H., & Lin, J. W. (2019). Modeling competence in science education. Disciplinary and Interdisciplinary Science Education Research, 1(1), 1-11. Cobb, P., Boufi, A., McClain, K., & Whitenack, J. (1997). Reflective discourse and collective reflection. Journal for research in mathematics education, 28(3), 258-277. Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2013). Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. Routledge. Collins, A., Brown, J. S., & Holum, A. (1991). Cognitive apprenticeship: Making thinking visible. American educator, 15(3), 6-11. Collins, A., Brown, J. S., & Newman, S. E. (1988). Cognitive Apprenticeship: Teaching the Craft of Reading, Writing and Mathematics. Thinking: The Journal of Philosophy for Children, 8(1), 2-10. Conlin, L., Hutchins, N., Grover, S., & Biswas, G. (2020). “Doing Physics” And “Doing Code”: Students’ Framing During Computational Modeling in Physics. Dabholkar, S., Anton, G., & Wilensky, U. (2018). GenEvo-An emergent systems microworld for model-based scientific inquiry in the context of genetics and evolution. International Society of the Learning Sciences, Inc.[ISLS]. Dabholkar, S., Irgens, G. A., Horn, M., & Wilensky, U. (2020, June). Students’ epistemic connections between science inquiry practices and disciplinary ideas in a computational science unit. In International Conference of the Learning Sciences (No. Jun-2020). De Jong, T., & Van Joolingen, W. R. (1998). Scientific discovery learning with computer simulations of conceptual domains. Review of educational research, 68(2), 179-201. De Jong, T., Sotiriou, S., & Gillet, D. (2014). Innovations in STEM education: the Go-Lab federation of online labs. Smart Learning Environments, 1(1), 1-16. Deepak, S., & Ameer, P. M. (2019). Brain tumor classification using deep CNN features via transfer learning. Computers in biology and medicine, 111, 103345. Dennen, V. P., & Hao, S. (2014). Intentionally mobile pedagogy: The M-COPE framework for mobile learning in higher education. Technology, Pedagogy and Education, 23(3), 397-419. Develaki, M. (2016). Key-aspects of scientific modeling exemplified by school science models: some units for teaching contextualized scientific methodology. Interchange, 47(3), 297-327. Durak, H. Y., & Saritepeci, M. (2018). Analysis of the relation between computational thinking skills and various variables with the structural equation model. Computers & Education, 116, 191-202. Ellis, A. K., Denton, D. W., & Bond, J. B. (2014). An analysis of research on metacognitive teaching strategies. Procedia-Social and Behavioral Sciences, 116, 4015-4024. Engeström, Y. (2009). Expansive learning: Toward an activity-theoretical reconceptualization. In Contemporary theories of learning (pp. 61-81). Routledge. Esquembre, F. (2004). Easy Java Simulations: A software tool to create scientific simulations in Java. Computer physics communications, 156(2), 199-204. Fiorella, L., & Mayer, R. E. (2016). Eight ways to promote generative learning. Educational Psychology Review, 28(4), 717-741. Fiorella, L., & Zhang, Q. (2018). Drawing boundary conditions for learning by drawing. Educational Psychology Review, 30(3), 1115-1137. Fisher, D. M. (2018). Reflections on teaching system dynamics modeling to secondary school students for over 20 years. Systems, 6(2), 12. Fraser, N. (2015, October). Ten things we′ve learned from Blockly. In 2015 IEEE Blocks and Beyond Workshop (Blocks and Beyond) (pp. 49-50). IEEE. Gilbert, J. K., & Justi, R. (2016). Modelling-based teaching in science education (Vol. 9). Basel, Switzerland: Springer international publishing. Graesser, A., & McNamara, D. (2010). Self-regulated learning in learning environments with pedagogical agents that interact in natural language. Educational Psychologist, 45(4), 234-244. Gravemeijer, K., & Doorman, M. (1999). Context problems in realistic mathematics education: A calculus course as an example. Educational studies in mathematics, 39(1), 111-129. Grover, S., & Basu, S. (2017, March). Measuring student learning in introductory block-based programming: Examining misconceptions of loops, variables, and boolean logic. In Proceedings of the 2017 ACM SIGCSE technical symposium on computer science education (pp. 267-272). Grover, S., & Pea, R. (2013). Computational thinking in K–12: A review of the state of the field. Educational researcher, 42(1), 38-43. Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Harvey, B., Garcia, D. D., Barnes, T., Titterton, N., Armendariz, D., Segars, L., ... & Paley, J. (2013). Snap!(build your own blocks). In Proceeding of the 44th ACM technical symposium on Computer science education (pp. 759-759). Hayes, A. F. (2017). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. Guilford publications. Heitzmann, N., Stadler, M., Richters, C., Radkowitsch, A., Schmidmaier, R., Weidenbusch, M., & Fischer, M. R. (2022). Learners’ adjustment strategies following impasses in simulations-Effects of prior knowledge. Learning and Instruction, 101632. Hewage, P., Trovati, M., Pereira, E., & Behera, A. (2021). Deep learning-based effective fine-grained weather forecasting model. Pattern Analysis and Applications, 24(1), 343-366. Hmelo, C. E., Nagarajan, A., & Day, R. S. (2000). Effects of high and low prior knowledge on construction of a joint problem space. The Journal of Experimental Education, 69(1), 36-56. Hogan, K., & Thomas, D. (2001). Cognitive comparisons of students′ systems modeling in ecology. Journal of Science Education and Technology, 10(4), 319-345. Hou, H. T. (2015). Integrating cluster and sequential analysis to explore learners’ flow and behavioral patterns in a simulation game with situated-learning context for science courses: A video-based process exploration. Computers in human behavior, 48, 424-435. Hulme, P. E. (2014). Bridging the knowing–doing gap: know‐who, know‐what, know‐why, know‐how and know‐when. Journal of Applied Ecology, 51(5), 1131-1136. Hussein, A., Gaber, M. M., Elyan, E., & Jayne, C. (2017). Imitation learning: A survey of learning methods. ACM Computing Surveys (CSUR), 50(2), 1-35. Hutchins, N., Wolf, R., Chin, D., Blair, K.P. and Grover, S. (2020). Computational thinking in support of learning and transfer. In Proceedings of the 14th International Conference of the Learning Sciences, Nashville, TN. ISLS. Jackson, S. L., Stratford, S. J., Krajcik, J., & Soloway, E. (1994). Making dynamic modeling accessible to precollege science students. Interactive learning environments, 4(3), 233-257. Kalyuga, S. (2007). Expertise reversal effect and its implications for learner-tailored instruction. Educational psychology review, 19(4), 509-539. Krajcik, J. S., & Sutherland, L. M. (2010). Supporting students in developing literacy in science. science, 328(5977), 456-459. Krajcik, J., & Merritt, J. (2012). Engaging students in scientific practices: What does constructing and revising models look like in the science classroom?. The Science Teacher, 79(3), 38. Krell, M., Reinisch, B., & Krüger, D. (2015). Analyzing students’ understanding of models and modeling referring to the disciplines biology, chemistry, and physics. Research in Science Education, 45(3), 367-393. Lee, I., Grover, S., Martin, F., Pillai, S., & Malyn-Smith, J. (2020). Computational thinking from a disciplinary perspective: Integrating computational thinking in K-12 science, technology, engineering, and mathematics education. Journal of Science Education and Technology, 29(1), 1-8. Legare, C. H., & Nielsen, M. (2015). Imitation and innovation: The dual engines of cultural learning. Trends in cognitive sciences, 19(11), 688-699. Lehrer, R., & Schauble, L. (2006). Cultivating model-based reasoning in science education. Cambridge University Press. Lemke, J. L. (1990). Talking science: Language, learning, and values. Ablex Publishing Corporation, 355 Chestnut Street, Norwood, NJ 07648 (hardback: ISBN-0-89391-565-3; paperback: ISBN-0-89391-566-1).. Lin, J. M. C., Wang, P. Y., & Lin, I. C. (2012). Pedagogy* technology: A two‐dimensional model for teachers′ ICT integration. British Journal of Educational Technology, 43(1), 97-108. Linn, M. C., Clark, D., & Slotta, J. D. (2003). WISE design for knowledge integration. Science education, 87(4), 517-538. Liu, C. C., Wen, C. T., Chang, H. Y., Chang, M. H., Lai, P. H., Fan Chiang, S. H., ... & Hwang, F. K. (2022). Augmenting the effect of virtual labs with" teacher demonstration" and" student critique" instructional designs to scaffold the development of scientific literacy. Instructional Science, 50(2), 303-333. Liu, C. Y., Wu, C. J., Wong, W. K., Lien, Y. W., & Chao, T. K. (2017). Scientific modeling with mobile devices in high school physics labs. Computers & Education, 105, 44-56. Liu, H. C., Andre, T., & Greenbowe, T. (2008). The impact of learner’s prior knowledge on their use of chemistry computer simulations: A case study. Journal of Science Education and Technology, 17(5), 466-482. Louca, L. T. (2022). Modeling-based learning in pre-school science affordances of different types of children constructed models. In Chinn, C., Tan, E., Chan, C., & Kali, Y. (Eds.). Proceedings of the 16th International Conference of the Learning Sciences. Hiroshima, Japan: International Society of the Learning Sciences. Louca, L. T., & Zacharia, Z. C. (2012). Modeling-based learning in science education: cognitive, metacognitive, social, material and epistemological contributions. Educational Review, 64(4), 471-492. Lynch, M. J., & Zenchak, J. J. (2002). Use of Scientific Inquiry To Explain Counterintuitive Observations. Mahaffy, P. G., Matlin, S. A., Holme, T. A., & MacKellar, J. (2019). Systems thinking for education about the molecular basis of sustainability. Nature Sustainability, 2(5), 362-370. Manlove, S., Lazonder, A. W., & de Jong, T. (2009). Trends and issues of regulative support use during inquiry learning: Patterns from three studies. Computers in Human Behavior, 25(4), 795-803. Martin, A. J. (2016). Using Load Reduction Instruction (LRI) to boost motivation and engagement. Leicester: British Psychological Society. Mathews, L. G., Jones, A., Szostak, R., & Repko, A. (2008). Using systems thinking to improve interdisciplinary learning outcomes: Reflections on a pilot study in land economics. Issues in Interdisciplinary Studies. Matsumoto, P. S., & Cao, J. (2017). The development of computational thinking in a high school chemistry course. Journal of Chemical Education, 94(9), 1217-1224. McLain, M. (2018). Emerging perspectives on the demonstration as a signature pedagogy in design and technology education. International Journal of Technology and Design Education, 28(4), 985-1000. Milne, C., & Otieno, T. (2007). Understanding engagement: Science demonstrations and emotional energy. Science Education, 91(4), 523-553. Namdar, B., & Shen, J. (2013). Knowledge organization with multiple external representations in an argumentation based computer supported collaborative learning environment. National Research Council. (2011). A framework for K-12 Science Education: Practices, Crosscutting Concepts, and Core Ideas. Washington: The National Academies Press. NGSS Lead States (2013). Next Generation Science Standards: For States, by States. Achieve, Inc. on Behalf of the Twenty-Six States and Partners That Collaborated on the NGSS, 1–103. doi: 10.17226/18930 Nguyen, H., & Santagata, R. (2021). Impact of computer modeling on learning and teaching systems thinking. Journal of Research in Science Teaching, 58(5), 661-688. Nicolescu, M., & Mataric, M. J. (2005). Task learning through imitation and human-robot interaction. Models and mechanisms of imitation and social learning in robots, humans and animals: behavioural, social and communicative dimensions, 407-424. Oliveira, A., Feyzi Behnagh, R., Ni, L., Mohsinah, A. A., Burgess, K. J., & Guo, L. (2019). Emerging technologies as pedagogical tools for teaching and learning science: A literature review. Human Behavior and Emerging Technologies, 1(2), 149-160. Ong, K. K. A., Hart, C. E., & Chen, P. K. (2016). Promoting higher-order thinking through teacher questioning: A case study of a Singapore science classroom. New Waves-Educational Research and Development Journal, 19(1), 1-19. Orsolini, M., & Pontecorvo, C. (1992). Children′s talk in classroom discussions. Cognition and instruction, 9(2), 113-136. Osborne, R. J., & Wittrock, M. C. (1983). Learning science: A generative process. Science education, 67(4), 489-508. Papert, S. (1980). Mindstorms, children, computers, and powerful ideas. New York, NY: Basic Books. Papert, S. (1990). Perestroika and epistemological politics. Epistemology and Learning Group, MIT Media Laboratory. Park, M., Yi, M., Flores, R., & Nguyen, B. (2020). Informal Formative Assessment Conversations in Mathematics: Focusing on Preservice Teachers′ Initiation, Response and Follow-Up Sequences in the Classroom. Eurasia Journal of Mathematics, Science and Technology Education, 16(10). Peng, R. D. (2011). Reproducible research in computational science. Science, 334(6060), 1226-1227. Perkins, D. (1998). What is understanding. Teaching for understanding: Linking research with practice, 39-57. Prabhu, P., Kim, H., Oh, T., Jablin, T. B., Johnson, N. P., Zoufaly, M., ... & Beard, S. (2011, November). A survey of the practice of computational science. In SC′11: Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis (pp. 1-12). IEEE. Price, T. W., & Barnes, T. (2015, August). Comparing textual and block interfaces in a novice programming environment. In Proceedings of the eleventh annual international conference on international computing education research (pp. 91-99). Psycharis, S., Kalovrektis, K., & Xenakis, A. (2020). A Conceptual Framework for Computational Pedagogy in STEAM education: Determinants and perspectives. Hellenic Journal of STEM Education, 1(1), 17-32. Quintana, C., Reiser, B. J., Davis, E. A., Krajcik, J., Fretz, E., Duncan, R. G., ... & Soloway, E. (2018). A scaffolding design framework for software to support science inquiry. In The journal of the learning sciences (pp. 337-386). Psychology Press. Raza, S., Haider, S., & Williams, M. A. (2012, December). Teaching coordinated strategies to soccer robots via imitation. In 2012 IEEE International Conference on Robotics and Biomimetics (ROBIO) (pp. 1434-1439). IEEE. Rich, K. M., Strickland, C., Binkowski, T. A., & Franklin, D. (2019, February). A K-8 debugging learning trajectory derived from research literature. In Proceedings of the 50th ACM technical symposium on computer science education (pp. 745-751). Rijke, W. J., Bollen, L., Eysink, T. H., & Tolboom, J. L. (2018). Computational thinking in primary school: An examination of abstraction and decomposition in different age groups. Informatics in education, 17(1), 77-92. Roth, W. M., & Lucas, K. B. (1997). From “truth” to “invented reality”: A discourse analysis of high school physics students′ talk about scientific knowledge. Journal of Research in Science Teaching: The Official Journal of the National Association for Research in Science Teaching, 34(2), 145-179. Sánchez Tapia, I. (2020). Introduction: A broad look at contextualization of science education across national contexts. In International perspectives on the contextualization of science education (pp. 1-12). Springer, Cham. Sauro, H. M., Hucka, M., Finney, A., Wellock, C., Bolouri, H., Doyle, J., & Kitano, H. (2003). Next generation simulation tools: the Systems Biology Workbench and BioSPICE integration. Omics A Journal of Integrative Biology, 7(4), 355-372. Schaal, S. (1996). Learning from demonstration. Advances in neural information processing systems, 9. Schwarz, C. V., & White, B. Y. (2005). Metamodeling knowledge: Developing students′ understanding of scientific modeling. Cognition and instruction, 23(2), 165-205. Schwarz, C. V., Reiser, B. J., Davis, E. A., Kenyon, L., Achér, A., Fortus, D., ... & Krajcik, J. (2009). Developing a learning progression for scientific modeling: Making scientific modeling accessible and meaningful for learners. Journal of Research in Science Teaching: The Official Journal of the National Association for Research in Science Teaching, 46(6), 632-654. Senge, P. M. (1990). The art and practice of the learning organization (Vol. 1). New York: Doubleday. Sengupta, P., Kinnebrew, J. S., Basu, S., Biswas, G., & Clark, D. (2013). Integrating computational thinking with K-12 science education using agent-based computation: A theoretical framework. Education and Information Technologies, 18(2), 351-380. Shen, J., & Confrey, J. (2010). Justifying Alternative Models in Learning Astronomy: A study of K–8 science teachers’ understanding of frames of reference. International Journal of Science Education, 32(1), 1-29. Shen, J., Lei, J., Chang, H. Y., & Namdar, B. (2014). Technology-enhanced, modeling-based instruction (TMBI) in science education. In Handbook of research on educational communications and technology (pp. 529-540). Springer, New York, NY. Shin, N., Bowers, J., Roderick, S., McIntyre, C., Stephens, A. L., Eidin, E., ... & Damelin, D. (2022). A framework for supporting systems thinking and computational thinking through constructing models. Instructional Science, 1-28. 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. Svoboda, J., & Passmore, C. (2013). The strategies of modeling in biology education. Science & Education, 22(1), 119-142. Sweeney, L. B. (2005). How is this similar to that? The skill of recognizing parallel dynamic structures on center stage. Creative Learning Exchange: Documents. Tehrani, J. J., & Riede, F. (2008). Towards an archaeology of pedagogy: learning, teaching and the generation of material culture traditions. World Archaeology, 40(3), 316-331. Tripto, J., Ben-Zvi Assaraf, O., Snapir, Z., & Amit, M. (2016). The ‘What is a system’reflection interview as a knowledge integration activity for high school students’ understanding of complex systems in human biology. International Journal of Science Education, 38(4), 564-595. Tropper, N., Leiss, D., & Hänze, M. (2015). Teachers’ temporary support and worked-out examples as elements of scaffolding in mathematical modeling. ZDM, 47(7), 1225-1240. van Riesen, S. A., Gijlers, H., Anjewierden, A., & de Jong, T. (2018). The influence of prior knowledge on experiment design guidance in a science inquiry context. International journal of science education, 40(11), 1327-1344. Veerasamy, A. K., Larsson, P., Apiola, M. V., D’Souza, D., & Laakso, M. J. (2021). Pedagogical Approaches in Computational Thinking-Integrated STEAM Learning Settings: A Literature Review. Intelligent Computing, 369-389. Verhoeff, R. P., Knippels, M. C. P., Gilissen, M. G., & Boersma, K. T. (2018, June). The theoretical nature of systems thinking. Perspectives on systems thinking in biology education. In Frontiers in Education (Vol. 3, p. 40). Frontiers Media SA. Vygotsky, L. S. (1978). Mind in society. Cambridge: Harvard University Press. Wang, J., Guo, D., & Jou, M. (2015). A study on the effects of model-based inquiry pedagogy on students’ inquiry skills in a virtual physics lab. Computers in Human Behavior, 49, 658-669. Weintrop, D. (2019). Block-based programming in computer science education. Communications of the ACM, 62(8), 22-25. Weintrop, D., & Wilensky, U. (2017). Comparing block-based and text-based programming in high school computer science classrooms. ACM Transactions on Computing Education (TOCE), 18(1), 1-25. 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., Holbert, N., Horn, M. S., & Wilensky, U. (2016). Computational thinking in constructionist video games. International Journal of Game-Based Learning (IJGBL), 6(1), 1-17. Wen, C. T., Chang, C. J., Chang, M. H., Fan Chiang, S. H., Liu, C. C., Hwang, F. K., & Tsai, C. C. (2018). The learning analytics of model-based learning facilitated by a problem-solving simulation game. Instructional Science, 46(6), 847-867. Wen, C. T., Liu, C. C., Chang, H. Y., Chang, C. J., Chang, M. H., Chiang, S. H. F., ... & Hwang, F. K. (2020). Students’ guided inquiry with simulation and its relation to school science achievement and scientific literacy. Computers & Education, 149, 103830. Whiten, A., Flynn, E., Brown, K., & Lee, T. (2006). Imitation of hierarchical action structure by young children. Developmental science, 9(6), 574-582. Wilensky, U. (1999). GasLab—An extensible modeling toolkit for connecting micro-and macro-properties of gases. In Modeling and simulation in science and mathematics education (pp. 151-178). Springer, New York, NY. Wilensky, U., & Papert, S. (2010). Restructurations: Reformulations of knowledge disciplines through new representational forms. Constructionism, 17, 1-15. Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35. Wing, J. M. (2014). Computational thinking benefits society. 40th anniversary blog of social issues in computing, 2014, 26. Xu, Z., Ritzhaupt, A. D., Tian, F., & Umapathy, K. (2019). Block-based versus text-based programming environments on novice student learning outcomes: a meta-analysis study. Computer Science Education, 1–28. doi:10.1080/08993408.2019.1565233 Xun, G. E., & Land, S. M. (2004). A conceptual framework for scaffolding III-structured problem-solving processes using question prompts and peer interactions. Educational technology research and development, 52(2), 5-22. Yip, D. Y. (2004). Questioning skills for conceptual change in science instruction. Journal of Biological Education, 38(2), 76-83.
指導教授	劉晨鐘(Chen-Chung Liu)	審核日期	2022-12-20
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