中文部分
王嘉瑜(2016)。科學模型與建模:科學建模的教學方式。台灣化學教育電子期刊,2016,3(1)。
李建會(1995)。還原論、突現論與世界的統一性。科學技術與辯證法,12(5),5-8。
呂益準(2005)。以混成軌域之電腦多媒體教導學生判斷分子形狀(未出版碩士論文)。國立臺灣師範大學,臺北市。
邱美虹(2008)。模型與建模能力之理論架構。科學教育月刊,306,2-9。(轉載自論文發表於中華民國科學教育學術研討會,2007,高雄:國立高雄師範大學科學教育研究所)。
邱美虹(2015)。以系統化方式進行模型與建模能力之線上教學與評量系統-探討科學課程、概念發展路徑與建模能力之研究(未出版)。科技部計畫報告。
邱美虹(2016)。科學模型與建模:科學模型、科學建模與建模能力。台灣化學教育電子期刊,2016,3(1)。
邱美虹、林秀蓁(2004)。以 CHILDES 分析一對一科學教學活動中師生互動共建科學知識的行為表現。科學教育學刊,12(2),133-158。邱美虹、吳文龍、鍾曉蘭與李雪碧(2013)。以概念演化樹探討跨年級學生理想氣體心智模式之發展歷程。科學教育學刊,21(2), 135-162。邱美虹、傳化文(1993)。分子模型與立體化學的解題。科學教育學刊,1(2),161-188。邱美虹、廖焜熙(1996)。立體化學與空間能力。化學,52(2),145-151。
邱美虹和劉俊庚(2008)。從科學學習的觀點探討模型與建模能力。科學教育月刊,314,2-20。
林靜雯和邱美虹(2008)。從認知/方法論之向度初探高中學生模型與建模歷程之知識。科學教育月刊,307,9-14。(轉載自論文發表於中華民國科學教育學術研討會,2007,高雄:國立高雄師範大學科學教育研究所)。
吳明珠(2004)。從科學史中理論模型的發展暨認知學心智模式探討化學概念的理解-層析理論的模型化案例(未出版博士論文)。國立臺灣師範大學,臺北市。吳明珠(2008)。科學模型本體分析:認識論面向初探。科學教育月刊,307,2-8。(轉載自論文發表於中華民國科學教育學術研討會,2007,高雄:國立高雄師範大學科學教育研究所)。
周金城(2008)。探究中學生對於科學模型的分類與組成本質的理解。科學教育月刊,306,10-17。(轉載自論文發表於中華民國科學教育學術研討會,2007,高雄:國立高雄師範大學科學教育研究所)。
武杰、李宏芳 (2000)。非線性是自然界本質嗎?科學技術與辯證法,17(2),1-5。
洪朝欽(1999)。非線性統動力系統--秩序、混沌、複雜與自我組織。科學月刊,1999年2月號。引自科學月科全文資料庫:http://163.20.22.161/Science/。
范冬萍 (2005a)。突現論的類型及其理論訴求—複雜性科學與哲學的視野。科學技術與辯證法,22(4),49-53。
范冬萍 (2005b)。突現論性質的下向因果關係。哲學研究,7,108-114。
范冬萍、張華夏 (2005)。突現理論:歷史與前沿。自然辯證法研究,21(6),5-10。
陳瑞麟(2003)。科學與世界之間:科學哲學論文集。臺北市:學富文化事業有限公司。
陳瑞麟(2004)。科學理論版本的結構與發展。臺北市:臺大出版中心。陳盈吉(2004)。探究動態類比對於科學概念學習與概念改變歷程之研究-以國二學生學習氣體粒子為例(未出版碩士論文)。國立臺灣師範大學,臺北市。
陳婉茹(2004)。探討動態類別對於化學平衡概念學習之研究-八年級學生概念本體及心智模式之變化(未出版碩士論文)。國立臺灣師範大學,臺北市。
湯偉君和邱美虹(2007):複雜系統、突現及其對科學教育的啟示。科學教育月刊,301,17-25。
湯偉君(2008)。以解釋本質探討中學演化論之教科書內容與教學(未出版博士論文)。國立臺灣師範大學,臺北市。湯偉君和邱美虹(2010)。省思科學教學—由解釋、科學解釋類型的觀點。科學教育研究與發展季刊,59,1-22。
張又升(2009):突現的概念。政大研學論壇。2012/10/20引自http://blog.roodo.com/nccugsa/8253918e.pdf
張志康和邱美虹(2009)。建模能力分析指標的發展與應用—以電化學為例。科學教育學刊,17(4),319-342。
劉俊庚和邱美虹(2010)。從建模觀點分析高中化學教科書中原子理論之建模歷程及其意涵。科學教育研究與發展季刊,59,23-52。
劉俊庚(2011)。探討模型與建模對於學生原子概念學習之影響(未出版博士論文)。國立臺灣師範大學,臺北市。鍾曉蘭(2007)。以多重表徵的模型教學探究高二學生理想氣體心智模式的類型及演變的途徑(未出版碩士論文)。國立臺灣師範大學,臺北市。
鍾曉蘭和邱美虹(2012)。高二學生在理想氣體多重表徵教學前後心智模式的改變。教育科學研究期刊,57(4),73-101。
鍾曉蘭和謝進生(2008)。以科展進行高二學生氣體動力論之科學學習及概念改變─氣體粒子運動模型組vs電腦動畫組。96學年度教育部中小學科學教育專案結案報告(未出版)。國立三重高中,臺北縣。
鍾曉蘭和謝進生(2010)。設計建模與多重表徵的模型教學活動以增進高二學生的科學學習-以化學鍵、分子混成軌域、分子形狀與結構為例。98學年度教育部中小學科學教育專案結案報告(未出版)。國立三重高中,臺北縣。
Strauss, A. & Corbin, J.著,吳芝儀與廖梅花譯(2001)。紮根理論研究方法。臺北市:濤石。
英文部分
Andersen H .(2001). The history of reductionism versus holistic approaches to scientific research. Endeavour, 25(4), 153-156.
Ainsworth, S. (1999). The functions of multiple representations. Computers & Education, 33(2-3), 131-152.
Ainsworth, S. (2006). DeFT: A conceptual framework for considering learning with multiple representations. Learning and Instruction, 16(3), 183-198.
Boo, H. K.(1998). Students’ understandings of chemical bonds and the energetics of chemical reactions. Journal of Research in Science Teaching, 35(5), 569-581.
Boulter, C. J., & Buckley, B. C.(2000). Constructing a typology of models for science education. In J. K. Gilbert & C. J. Boulter (eds.), Developing models in Science Education, (pp.41-57). Netherlands: Kluwer academic Publisher.
Burr, V. (2003). Social Constructionism. New York: Routledge.
Chi, M. T. H. (1992). Conceptual change within and across ontological categories: Examples from learning and discovery in science. In R. Giere (Ed.) Cognitive Models of Science: Minnesota studies in the Philosophy of Science (pp.129-186). Minnesapolis, MN: University of Minnesota Press.
Chi, M.T.H. (2005). Common sense conceptions of emergent processes: Why some misconceptions are robust. Journal of the Learning Science, 14, 161-199.
Chi, M.T.H., & Roscoe, R.D. (2002). The process and challenges of conceptual change. In M. Limon & L. Mason (Eds.), Reconsidering Conceptual Change: Issues in Theory and Practice. (pp.3-27). Netherlands: Kluwer Academic Publishers.
Chi, M. T. H., Roscoe, R. D., Slotta, J. D., Roy, M. and Chase, C. C. (2012). Misconceived Causal Explanations for Emergent Processes. Cognitive Science, 36, 1–61.
Chi, M. T. H, Siler, S.A., & Jeong, H.(2004). Can tutors monitor students’ understanding accurately? Cognition and Instruction, 22(3), 363-387.
Chiu, M. H., & Chung, S. L. (2013).The use of multiple perspectives of conceptual change to investigate students’ mental models of gas particles. In G. Tsaparlis & H. Sevian (Ed.) Concepts of Matter in Science Education (pp.143-168). Netherlands: Springer
Chittleborough, G. D., Treagust, D. F., Mamiala, T. L., & Mocerino, M. (2005). Students’ perceptions of the role of models in the process of science and in the process of learning. Research in Science & Technological Education, 23(2), 195-212.
Clement, J. (2000). Model based learning as a key research area for science education. International Journal of Science Education, 22(9), 1041–1053.
Clement, J. J., & Rea-Ramirez, M. A. (Eds.) (2008). Model based learning and instruction in science. Dordrecht, NL: Springer.
Coll, R. K., & Taylor, N. (2002). Mental models in chemistry: senior chemistry students’ mental models of chemical bonding. Chemistry Education: Research and Practice in Europe, 3(2), 175-184.
de Posada, J. M. (1997). Conceptions of high school students concerning the internal structure of metals and their electric conduction: Structure and evolution. Science Education, 81(4), 445-467.
Gibbin, J. (2005). Deep Simplicity: Chaos, Complexity and the Emergence of Life. U.S: Penguin book.
Giere, R.N. (2004). How models are used to represent reality. Philosophy of Science, 71, 742–752.
Gilbert, J. K. (1993). Models and modeling in science education. Hatfield: The Association for Science Education.
Gilbert, J. K. (2005). Visualization: A metacognitive skill in science and science education. In J. K. Gilbert (ed.), Visualization in Science Education (pp. 9-27). Netherlands: Springer.
Gilbert, J. K., & Buckley, B. C. (2000). Introduction to model-based teaching and learning in science education. International Journal of Science Education, 22(9), 891-894.
Gilbert, J. K., & Boulter, C., J., & Elmer, R. (2000). Positioning models in science education and in design and technology education. In J. K. Gilbert and C. J. Boulter (eds.) Developing Models in Science Education (pp. 3-17). Dordrecht/Boston/London: Kluwer Academic Publishers.
Gilbert, J. K., & Justi, R. (2016). Modelling-based Teaching in Science Education. In J.K. Gilbert & R. Justi (Eds.), Models and Modeling in Science Education 9 (pp.81-96). Switzerland: Springer.
Gilbert, J. K., & Treagust, D. F. (2009). Introduction: macro, submicro and symbolic representations and the relationship between them: key models in chemical education. In J. K. Gilbert, & D. Treagust (eds.). Multiple representations in Chemical Education (pp.1-8). Dordrecht: Springer.
Goldstein, J. (1999). Emergence as a Construct: History and Issue. Emergence, 1(1) 49-72.
Goldstone, R. L. (2006). The complex systems see-change in education. The Journal of the Learning Sciences, 15(1), 35-43.
Gobert, J., Snyder, J. & Houghton, C. (2002, April). The Influence of Students’ Understanding of Models on Model-Based Reasoning. Paper presented at the Annual Meeting of the American Educational Research Association, New Orleans, LO.
Grosslight, L., Unger, C., Jay, E., & Smith, C. (1991). Understanding models and their use in science conceptions of middle and high school students and experts. Journal of Research in Science Teaching, 28(9), 799-822.
Halloun, I. A. (1996) Schematic modelling for meaningful learning of physics. Journal of Research in Science Teaching. 33, 1019-1041.
Halloun, I. A. (2006). Modeling Theory in Science Education. Netherlands: Springer.
Harrison, A. G., & Treagust. D. F. (2000a). A typology of school science models. International Journal of Science Education, 22(9), 1011-1026.
Harrison, A. G., & Treagust. D. F. (2000b). Learning about atoms, molecules, and chemical bonds:A case study of multiple-model use in grade 11 chemistry, Science Education, 84, 352-381.
Harrison A. G., & De Jong. O. (2005). Exploring the use of multiple analogical models when teaching and learning chemical equilibrium. Journal of Research in Science Teaching, 42(10), 1135–1159.
Hestenes, D. (1992). Modeling games in the newtonian world. Am. J. Phys., 60, 732-748.
Hmelo-Silve, C.E., & Azevedo, R. (2006). Understanding complex systems: Some core challenges. The Journal of the Learning Sciences, 15(1), 53-61.
Hmelo-Silve, C.E., & Pfeffer, M. G. (2004). Comparing expert and novice understanding of a complex system from the perspective of structures, behaviors, and functions. Cognitive Science, 1, 127-138.
Holland, J. H. (1995). Hidden Order: How Adaptation Builds Complex. MA: Addison-Wesley.
Holland, J. H. (1998), Emergence from Chaos to Order. New York: Oxford University Press.Harrison, A. G., & Treagust, D. (1996). Secondary students’ mental models of atoms and molecules: Implications for teaching chemistry. Science Education, 80(5), 509-534.
Jacobson, M. J., & Wilensky, U. (2006). Complex systems in education: Scientific and educational importance and implications for the learning sciences. The Journal of the Learning Sciences, 15(1), 11-34.
Johnson-Laird, P. N.(1983).Mental models. Towards a Cognitive Science of Language, Inference, and Consciousness. Cambridge, UK:Cambridge University Press.
Johnson-Laird, P. N. (1999). Formal rules versus mental models in reasoning. In R. J. Sternberg (Ed.), The nature of cognition (pp. 586-624). Cambridge, MA: MIT Press.
Johnstone, A. H. (1991). Why is science difficult to learn? Things are seldom what they seem. Journal of Computer Assisted Learning, 7, 75-83.
Johnstone, A. H. (1993). The development of chemistry teaching: A changing response to changingdemand. Journal of Chemical Education, 70(9), 701-705.
Johnstone, A. H. (2000). Chemical education research: Where from here? University Chemistry Education, 4(1), 34-38.
Justi, R. S. (2000). Teaching with Historical Models. In J. K. Gilbert & C. J. Boulter (eds.), Developing models in Science Education, (pp.209-226). Netherlands: Kluwer academic Publisher.
Justi, R. S., & Gilbert, J. K. (2002). Modeling, teachers’ views on the nature of modeling, and implications for the education of modelers. International Journal of Science Education, 24(4), 369-387.
Justi, R. S. & Gilbert, J. K. (2003), Teachers' views on the nature of models, International Journal of Science Education, 25(11), 1369-1386.
Kim, J. (1999). Making sense of emergency. Philosophical studies, 95, 3-36.
Kim, J. (2006). Emergence: Core ideas and issues. Synthese, 151, 547-559.
Koponen, I. T. (2007). Models and modelling in physics education: A critical re-analysis of philosophical underpinnings and suggestions for revisions. Science & Education, 16, 751–773.
Kozma, R. (2003). The material features of multiple representations and their cognitive and social affordances for science understanding. Learning and Instruction, 13(2), 205-226.
Kozma, R. B., & Russell, J. (1997). Multimedia and understanding: Expert and novice responses to different representations of chemical phenomena. Journal of Research in Science Teaching, 34, 949–968.
Kozma, R., & Russell, J. (2005). Students becoming chemists: Developing representational competence. In J. K. Gilbert (Ed.), Visualization in science education (pp. 121–146). Dordrecht, The Netherlands: Springer.
Maia, P. F., & Justi, R. (2009). Learning of chemical equilibrium through modelling-based teaching. International Journal of Science Education, 31(5), 603-630.
Nahum, T. L., Mamlok-Naaman, R., Hofstein, A., & Krajcik, J. (2007). Developing a new teaching approach for the chemical bonding concept aligned with current scientific and pedagogical knowledge. Science Education, 91, 579-603.
National Research Council (1996). National Science Education Standards. Washington, DC: National Academy.
National Research Council. (2007). Taking science to school: Learning and teaching science in grades K-8. Washington, DC: National Academies Press.
Nersessian, N. J. (1999). Model-based reasoning in conceptual change. In L. Magnani, N. J. Nerssian, & P. Thagard (eds.), Models are used to represent reality. New York: Kluwer Academic Publishers.
Nersessian, N. J. (2002). The cognitive basis of model-based reasoning in science. In P. Carruthers, S. Stich, & M. Siegal (Eds.), The cognitive basis of science (pp. 17–34). Cambridge: Cambridge University Press.
Nersessian, N. J., & Patton, C. (2009). Model-based reasoning in interdisciplinary engineering. In A. Mcijers (Ed.), Handbook of the philosophy of technology and engineering sciences (pp. 687 – 718). Amsterdam: Elsevier.
Nicoll, G. (2001). A report of undergraduates’ bonding misconceptions. International Journal of Science Education, 23(7), 707-730.
Niss, M. (2009). Metamodelling messages conveyed in five statistical mechanical textbooks from 1963 to 2001. International Journal of Science Education, 31(5), 697-719.
Ogborn, J. (1994). Overview: the nature of modelling. In H. Mellar, J. Bliss, R. Boohan, J. Ogborn, & C. Tompsett (eds.), Learning with artificial worlds: computer basedmodelling in the curriculum (pp.11-15). Hong Kong: Graphicraft
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.
Peterson, R. F., & Treagust, D. F. (1989). Development and application of a diagnostic instrument to evaluate grade-11 and –12 students’ concepts of covalent bonding and structure following a course of instruction. Journal of Research in Science Teaching, 26(4), 301-314.
Prigogine, I. & Stengers, I. (1984), Order Out of Chaos: Man’s New Dialogue With Nature, New York: Bantam Books.
Resnick, M. (1996). Beyond the centralized mindset. The Journal of the Learning Sciences, 5, 1-22.
Salmon, W. C. (1998). Causality and explanation. New York, NY: Oxford University.
Sterelny, K., & Griffiths, P. E. (1999). Sex and death: An introduction to philosophy of biology. Chicago, IL: The University of Chicago.
Schwarz, C., & White, B. (2005). Metamodeling knowledge: Developing students’ understanding of scientific modeling. Cognition and Instruction, 23(2), 165 – 205.
Tan, K-C. D., & Treagust, D. F. (1999). Evaluating students’ understanding of chemical bonding, School Science Review, 81(294), 75-81.
Taber, K. S. (1995). Development of Student Understanding: a case study of stability and lability in cognitive structure, Research in Science & Technological Education, 13(1), 89-99.
Taber, K. S. (2002). Compounding quanta: probing the frontiers of student understanding of molecular orbitals. Chemistry Education: Research and Practice in Europe, 3(2), 159-173.
Taber, K. S., & Coll, R. (2002). Bonding. In J. K. Gilbert, O. D. Jong, R. Justy, D. F. Treagust, & J. H. Van Driel (eds.), Chemical education: towards research-based practice (pp. 213-234). Dordrecht: Kluwer.
Talanquer, V. (2007). Explanations and Teleology inChemistry Education. International Journal of Science Education, 1-18. DOI: 10.1080/09500690601087632
Thagard, P. (1992). Conceptual revolution. Princeton: Princeton University Press.
Treagust, D. F., Chittleborough, G., & Mamiala, T. L. (2002). Students’ understanding of the role of scientific models in learning science. International Journal of Science Education, 24(4), 357-368.
Treagust, D. F., & Harrison, A. G. (2000). In search of explanatory frameworks: An analysis of Richard Feynman's lecture 'Atoms in motion'. International Journal of Science Education, 22(11), 1157-1170.
Vosniadou, S., Brewer, W. F. (1992). Mental models of the earth: A study of conceptual change in childhood. Cognitive Psychology, 24(4), 535-585.
Vosniadou, S.(1994). Capturing and modeling the process of conceptual change. Learning and Instruction, 4, 45-69.
Vosniadou, S.(2002). On the nature of naive physics. In M. Limon & L. Mason (Eds.), Reconsidering conceptual change: Issues in theory and practice (pp. 61-76). Dordrecht, the Netherlands: Kluwer Academic.
Vosniadou, S., Skopeliti, I., & Ikospentaki K. (2004). Modes of knowing and ways of reasoning in elementary astronomy. Cognitive Development, 19, 203-222.
Waldrop, M. M. (1992). Complexity: The emerging science at the edge of chaos and order. New York: Touchstone.
Wilensky, U. (1999). NetLogo [computer software]. Evanston, IL: Center for Connected Learning and Computer-Based Modeling, Northwestern University (http://ccl.northwestern.edu/netlogo)
Wilensky, U., & Resnick, M. (1999). Thinking in levels: A dynamic systems perspective to making sense of the world. Journal of Science Education and Technology, 8(1), 3-19.
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 & Instruction, 24(2), 171-209.
Zhang, B. H., Liu, X., & Krajcik, J. S. (2006). Expert Models and Modeling Processes Associated with a Computer Modeling Tool. Science Education, 90(4), 579-604.