模型的建構與使用在科學實務上扮演重要的角色。然而學校科學課程卻很少介紹模型、強調模型是理論建構的重要工具以及如何協助學生發展不同模型之間的轉化能力。本研究區分為兩大主題，四個部份。主題一包括部分1與部分2，主要著重在歷經與科學家相似真實性探究之8年級科展學生(N=5)，探討教師在科展建模歷程中提供模型與建模本質觀點的有無，對於學生所建構科學模型的類型、階層以及模型與建模認識觀點的異同表現。其中部份1以事後分析法進行個案研究(N=2)，探討無提供模型與建模鷹架時，科展學生建模的表現，而部分2以歷程分析法進行的個案研究(N=3)，嘗試將真實性探究的認知過程與模型與建模本質觀點納入科展建模歷程中，形成模型本位探究教學架構，探討在真實性建模的科展活動中提供模型與建模鷹架時，學生的建模表現。主題二嘗試以模型本位的探究教學架構應用在傳統課室教學(N=102)，形成本論文之部分3與部分4。其中部分3為立意取樣選取研究者任教之9年級班級，再隨機分配實驗組(模型本位探究教學組，N=37)與對照組(簡單探究教學組，N=32；與講述教學組，N=33)，教學過程中實驗組搭配建模文本而對照組搭配傳統文本，探討經過相同教師教學之後，學生對於模型與建模認識觀點、科學過程技能、概念內容、建模能力與後設建模能力之表現異同。而部份4主要藉由學生對已建模型進行證成，探討建模歷程中模型效化的概念融貫性，若學生能夠進行數據或是相關科學知識的證成，即可確認模型內部組成變因之間的關聯性，表示學生經過證成的模型之融貫程度相較於無法證成者為高。最後總結兩大主題的研究，包括部分1、2、3以及4，探討模型本位探究教學架構在真實性探究的科展歷程以及課室學習的歷程中，關於學生建模的表徵模型之間轉換的關係，亦表示如果強調實驗研究的科學建模，學生會先從巨觀現象建立起巨觀或是動作模型，接著經由資料收集與分析獲得「中觀」(meso-)的視覺以及語彙模型，最後才根據關係座標圖建立數學表徵模型，而如果只是黑板示範實驗，會忽略從「中觀模型」所能連結巨觀現象以及符號模型。研究結果顯示，彙整如下：
1.部分1為經由事後訪談之科展學生，S1與S2學生能夠在相同的探究階段建構相似的表徵模型類型，同時隨著探究時程的增加，兩位學生建構之外顯表徵模型階層逐漸提升至延伸抽象階段；而模型與建模認識觀點部份，S1與S2學生經歷師生共構之科展探究活動之後，對於模型的看法仍是屬於具體事物而非抽象思考模型，顯示只有經歷真實性探究的師生共構無法有效提升學生模型與建模認識觀點到最高階層。
2.部分2為補事後訪談研究法之缺失，透過歷程分析法探討個案學生在提供模型與建模鷹架時科展活動之表現。結果顯示在外顯表徵模型類型與階層面向，個案學生S3、S4以及S5與未提供模型與建模鷹架S1與S2建構相似表徵模型與階層。然而在模型與建模認識觀點面向上，S3經過科展建模歷程之後皆達最高層級，而S4與S5在「模型本質」以及「評價模型」皆達最高層級，而S4與S5在「模型功能與目的」以及「建模歷程」皆為階層2，主要原因為學生仍以解釋而非預測作為模型的主要功能取向。
3.對照部份1與部分2之結果顯示，經過長期真實性師生共構的科展學生皆能夠在不同的探究階段建構出相似的外顯表徵模型並且逐漸提升層級，然而無提供模型與建模觀點鷹架之學生，無法將想法視為抽象模型並透過內隱與外顯模型交互作用進行科學建模，而提供模型與建模鷹架之科展學生能夠將模型視為系統性思考的工具，並且運用在科展探究活動之中，獲得較高的階層。結果顯示模型與建模鷹架對於國中學生進行建模學習有其必要性。
4.經過不同教學模式教學之後，模型本位探究教學組學生在長時間內之「模型本質」-模型與建模認識觀點、「概念內容」-等加速度與牛頓第二運動定律以及「能力」-科學過程技能與建模能力之整體表現皆優於簡單探究以及講述教學組。然而模型本位探究教學在後設建模能力只有部份項目顯著優於其餘兩組，顯示以模型位探究進行學習活動仍然需要加強學生自我評估之表現，以及如何在過程中誘導學生原先已有的後設認知能力以促進建模學習是一項關鍵。綜合結果顯示以模型本位探究模式進行教學有助於學生不同面向的成長。
5.經過不同教學模式教學之後，學生透過情境適切的判斷證成已建模型合理性之證成融貫性表現，模型本位探究組除了在等加速度之後測未與簡單探究組達顯著差異之外，其餘部分皆顯著優於簡單探究組與講述教學組，而簡單探究組學生在等加速度延宕以及牛頓第二運動定律後測顯著優於講述教學組。結果顯示雖然進行簡單探究教學能夠讓學生獲得證成的能力，然而提供學生模型與建模鷹架並且外顯化建模歷程之教學活動更能有效提升學生證成融貫性。
6.模型本位探究教學策略能夠提供機會協助學生進行實驗活動形成巨觀模型，接著進行針對蒐集的數據繪製表格與關係圖並進行意義解釋形成中觀模型，再以建構之中觀模型與科學符號模型以及巨觀模型做比較，連結巨觀與科學符號模型。而教學歷程中額外提供學生鷹架，透過中觀模型連結巨觀現象與科學符號模型證成已建模型作為學生個人修改模型的依據。
雖然文獻說明模型與建模在科學學習扮演重要的角色，然而並未提供實徵的研究說明模型與建模鷹架為什麼是一項重要的學習要素。本研究認為提供模型與建模探究學習活動，需要提供學生模型與建模鷹架作為系統性思考的工具，透過實驗為主、證成合理性以及理論模型遷移的建模歷程讓學生接觸巨觀現象形成巨觀模型逐漸建構中觀模型，再以中觀模型連結巨觀模型與科學符號模型，發展學生的科學本質、概念內容以及相關能力以呼應科學學習的三大目標。

Although modeling is fundamental to human cognition and scientific inquiry, the design of secondary school curriculums rarely integrates inquiry and modeling, touches the epistemological view of models and modeling, and helps the student to develop the competences to make associations between models. This research which explores the implement of model-based inquiry teaching strategy in science fair and traditional classroom contexts in a secondary school covers two main subjects, which are divided into four parts. Subject 1 includes parts 1 and 2 (N = 5, 8th graders), and is targeted to explore whether the teacher providing the epistemological view of models and modeling during the process of science fair have any effect on the type, level, and cognition towards the concepts of model and modeling developed by the students. The intervention is made through integrating a model-based inquiry teaching framework based on the cognitive process of authentic inquiry and the concept of models and modeling into the process of science fair. Subject 2 (N = 102, 9th graders) is an attempt to implement the model-based inquiry teaching framework in traditional classroom context, which forms parts 3 and 4 of this research. The final consolidates the studies in the two main subjects, including parts 1, 2, 3, and 4, exploring model-based inquiry teaching strategy in the process of science fair and the traditional class learning in relation to the conversion between the representation models of student’s model construction process. The following is a consolidation of the results derived from the studies in this research:
1. Part 1 involves post hoc interviews with the students. S1 and S2 students are able to construct similar representation models in the same inquiry stage, and at the same time, the level of external representation models constructed by these two students gradually extends into the abstract stage along the inquiry process. In epistemological view of model and modeling, students S1 and S2 continue to see the concept of “model” as a concrete object, instead of an abstract entity, after they have experienced the explorative activities for the science fair with the teacher.
2. Part 2 is an attempt to remedy the shortcomings of the post hoc interviews. The results show that, in the dimensions of type and level of external representation models, students S3, S4, and S5 demonstrated the same level of representation models similar to the model constructed by S1 and S2 (not provided with the scaffolding of models and modeling).
3. Comparison between the results of Part 1 and 2 shows that students participating in the science fair who have been through a long-period of co-construction development with the teacher are able to construct similar external representation models in different stages and the levels elevates over the course of the process. However, students not provided with the scaffolding of models and modeling are unable to turn ideas into abstract models and conduct scientific modeling through alternating reference between the internal and expressed models. This indicates that co-construction activities involving only through authentic inquiry are not able to effectively elevate the students’ epistemological view of model to the highest level and that intervention of the scaffolding of model and modeling is necessary to the secondary school students when students are taught with the modeling instructional strategy.
4. Part 3 explores the 9th-gende students’ comprehension on the epistemological view of models and modeling, the scientific process skills, the content, modeling and metamodeling competences about acceleration and Newton's second law of motion before/after intervention of the model-based inquiry teaching. After teaching in differentiated teaching modes, students in the model-based inquiry teaching group generally have better performance than the simple inquiry and lecture teaching groups in the epistemological view of model and modeling, the understanding of acceleration and Newton’s second law of motion, and scientific process skills and modeling competences. However, students in the model-based inquiry teaching group only few perform better with significant difference than the students in the other two groups in the metamodeling competences.
5. After implementing the different teaching modes, the students verified the coherence and the reasonableness of the constructed models through appropriate judgment on the contexts. The model-based inquiry teaching group show better performance with significant difference than the simple inquiry group and the lecture teaching group in the posttest and delayed posttest of all aspects, except the unit of acceleration.
6. The model-based inquiry teaching strategy is able to provide the opportunity to help students form macroscopic models in the experiment activities, lead them to cross over to the stage of interpretation targeting on the data, tables, and correlation charts developed in the process and form the meso-models, and advance to the stage of linking macroscopic and scientific symbolic models through comparisons between the constructed meso-model and scientific symbolic model, as well as the macroscopic model.
Although the research literature described the significant role played by the concept of models and modeling in scientific research, they did not provide empirical evidence to stress the importance of models and modeling. This research believes that, when implementing model and modeling explorative learning activities, students should be provided with the conceptual framework of models and modeling as a tool for systematic thinking. The modeling process involving experiments, justification, and theoretic model deployment brings the students into contact with the macroscopic phenomena and induce formation of macroscopic model, which is then gradually developed into the meso-model. The meso-model is then linked with the macroscopic model and scientific symbolic model to develop students' comprehend on the nature of science, content of concepts, and the associated competences, that is, the three goals of science learning.

毛松霖、張菊秀 (1997)。”探究式教學法”與”講述式教學法”對於國中學生地球科學－氣象單元學習成效之比較。科學教育學刊 ，5(4)，461-497。
李國賓 (2008)。WebQuest教學策略對國小學童批判思考能力之影響—以國小六年級社會學習領域為例。國立台南大學數位學習科技學系教學碩士論文。未出版。
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)。
邱美虹、鐘建坪、鍾曉蘭、白勝安和陳建章(送審中)。建模文本對高中學生建模能力之促進效果。科學教育學刊。
吳明珠 (2008)。科學模型本質剖析：認識論面向初探。科學教育月刊，306，2-8。(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)。
周金城 (2002)。以孔恩的常態科學探究高中師生科學社群中科學探索活動的歷程—參與科學展覽活動之得獎個案分析。國立臺灣師範大學科學教育研究所博士論文，未出版。
周金城 (2008)。探究中學生對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)。
林正弘 (1988)。伽利略．波柏．科學說明。台北: 東大圖書公司。
林俊華(1985)。國中學生科學過程技能學習成就之調查研究。國立台灣師範大學物理研究所碩士論文。未出版。
林靜雯、邱美虹(2008)。從認知/方法論之向度初探高中學生模型及建模歷程之知識。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)。
洪振方 (2003)。探究式教學的歷史回顧與創造性探究模式之初探。高雄師大學報：自然科學與科技類，15，641-662。
洪振方、莊敏雄和宋國城 (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。
高慧蓮 (2006)。九年一貫課程提升學生科學本質能力指標表現可行教學模組之開發研究。科學教育學刊, 14(4), 1-25。
教育部 (2003)。國民中小學九年一貫課程綱要：自然與生活科技學習領域。台北: 作者。
陳瑞麟(2004)。科學理論版本的結構與發展。台北：台大出版中心。
郭章淵、戴文雄(2007)。問題導向學習對建築系學生學習成效之研究－以建築設備學教學為例。朝陽學報，12，293-310。
張志康、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。
張志康(2009)。從Chi與Vosniadou的綜合理論探究建模教學對學生力學概念改變之影響。國立台灣師範大學科學教育研究所博士論文。未出版。
楊秀停、王國華(2007)。實施引導式探究教學對於國小學童學習成效之影響。科學教育學刊，15(4)，439-459。
蔡執仲、段曉林(2005)。探究式實驗教學對國二學生理化學習動機之影響。科學教育學刊，13(3)，289-315。
謝州恩、吳心楷(2005)。探究情境中國小學童科學解釋能力成長之研究。師大學報：科學教育類，50(2)，55-84。
劉宏文(2001)。高中學生進行開放式科學探究活動之個案研究。國立彰化師範大學科學教育研究所博士論文，彰化市。
劉俊庚、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，59，23-54。
鐘建坪(2010)。引導式建模探究教學架構初探。科學教育月刊，328，2-19。
Achinstein, P. (1983). The nature of explanation. New York: Oxford University Press.
Abd-El-Khalick, F. S., & Akerson, V. L. (2004). Learning about nature of science as conceptual change: Factors that mediate the development of preservice elementary teachers’ views of nature of science. Science Education, 88(5),785-810.
Agassi, J. (1997). Thought, action and scientific technology. International Journal of Technology and Design Education, 7(1-2), 33-48.
Ainsworth, S. E., & van Labeke, N. (2004). Multiple forms of dynamic representation. Learning and Instruction, 14(3), 241–255.
Akerson, V. L., & Hanuscin, D. L. (2007). Teaching nature of science through inquiry: The results of a three-year professional development program. Journal of Research in Science Teaching, 44(5), 653–680.
Alonzo, A. C., & Steedle, J. T. (2009). Developing and assessing a force and motion learning progression. Science Education, 93(3), 389-421.
American Association for the Advancement of Science (1989). Science for all American. Washington, DC: American Association for the Advancement of Science.
American Association for the Advancement of Science (1993). Project 2061. Benchmarks for science literacy. Washington, DC: American Association for the Advancement of Science.
Anderson, J. R. (1983). The architecture of cognition. Cambridge, MA: Harvard University Press.
Anderson, R. D. (2002). Reforming science teaching: what research says about inquiry. Journal of Science Teacher Education, 13(1), 1–12.
Barrow, L. H. (2006). A brief history of inquiry: From Dewey to standards. Journal of Science Teacher Education, 17(3), 265-278.
Bamberger, Y., & Davis, E. A. (2013). Middle-school science students' scientific modeling performances across content areas and within a learning progression. International Journal of Science Education, 35(2), 213-238.
Banilower, E. R., Smith, P. S., Weiss, I. R., & Pasley, J. D. (2006). The status of K-12 science teaching in the United States: Results from a national observation study. In D. W. Sunal and E. L. Wright (Eds.) The Impact of State and National Standards on K-12 Science Teaching. Greenwich, CT: IAP.
Barab, S. A., Hay, K. E., Barnett, M. G., & Squire, K. (2001). Constructing virtual worlds: Tracing the historical development of learner practices/understandings. Cognition and Instruction, 19(1), 47-94.
Bell, R. L. (2006). Perusing Pandora’s Box: Exploring the what, when, and how of nature of science instruction. In L. B. Flick & N. G. Lederman (Eds.), Scientific inquiry and nature of science: Implications for teaching, learning, and teacher education (pp. 427-446). Dordrecht: Springer.
Bell, R. L., Matkins, J. J., & Gansneder, B. M. (2011). Impacts of contextual and explicit instruction on preservice elementary teachers’ understandings of the nature of science. Journal of Research in Science Teaching, 48(4), 414-436.
Bell, L. R., Smetana, L., & Binns, I. (2005). Simplifying inquiry instruction: assessing the inquiry level of classroom activities. The Science Teacher, 72 (7), 30-33.
Biggs, J. B., & Collis, K. F. (1982). Evaluating the quality of learning: The SOLO taxonomy. New York Academic Press.
Bloom, B. S. (1956). Taxonomy of educational objectives, the classification of educational goals Handbook I: Cognitive Domain. New York: McKay.
BonJour, L. (1985). The structure of empirical knowledge. Harvard University Press, Cambridge, Massachusetts.
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). Dordrecht, The Netherlands: Kluwer.
Britton, B. K., & Gulgoz, S. (1991). Using Kintsch's computational model to improve instructional text: Effects of repairing inference calls on recall and cognitive structures. Journal of Educational Psychology, 83(3), 329-404.
Brown, A. I. (1978). Knowing when, where, and how to remember: A problem of metacognition. In R. Glaser (Ed.), Advances in Instructional Psychology. New York: Halstead Press.
Brown, A. L., & DeLoache, J. S. (1978). Skills, plans, and self-regulation. In R. S. Siegel (Ed.), Children’s thinking: What develops? (pp. 3–35). Hillsdale, N.J.: Erlbaum.
Braaten, M., & Windschitl, M. (2011). Working toward a stronger conceptualization of scientific explanation for science education. Science Education, 95(4), 639-669.
Bruner, J. S. (1977). The process of education. Cambridge, MA: Harvard University Press.
Buckley, B. C., & Boulter, C. J. (2000). Investigating the role of representations and expressed models in building mental models. In J. K. Gilbert and C.J. Boulter (eds.), Developing Models in Science Education (pp.119-135.) Netherlands: Kluwer Academic Publishers.
Burns, J. C., Okey, J. R., & Wise, K. C. (1985). Development of an integrated process skill test: Tips II. Journal of Research in Science Teaching, 22(2), 169–177.
Bybee, R. W. (1997). Achieving scientific literacy: From purposes to practices. Portsmouth, NH: Heinemann.
Bybee, R. W. (2004). Science inquiry and science teaching. In L. B. Flick, & N. G. Lederman (Eds.) Scientific Inquiry and Nature of Science. Implications for Teaching, Learning, and Teacher Education (pp. 1–12). The Netherlands: Kluwer Academic Publishers.
Campbell, T., Zhang, D., & Neilson, D. (2011). Model based inquiry in the high school physics classroom: An exploratory study of implementation and outcomes. Journal of Science Education & Technology, 20(3), 258-269.
Carey, S. (1985). Conceptual change in childhood. The MIT press, Cambridge, Massachusetts.
Carey, S., Evans, R., Honda, M., Jay, E., & Unger, C. (1989). An experiment is when you try it and see if it works: A study of grade 7 students’ understanding of the construction of scientific knowledge. International Journal of Science Education, 11(5), 514–529.
Carey, S., & Smith, C. (1993). On understanding the nature of scientific knowledge. Educational Psychologist, 28(3), 235–251.
Champagne, A., Klopfer, L. E., & Anderson, J. H. (1980). Factors influencing the learning of classical mechanics. American Journal of Physics, 48(12), 1074 – 1079.
Chi, M. T. H. (1992). Conceptual change within and across ontological categories: Examples from learning and discovery in science. Cognitive Models of Science: Minnesota Studies in the Philosophy of Science, XV, 129-186.
Chi, M. T. H. (2005). Common sense conceptions of emergent processes. Journal of the Learning Science, 14(2), 161-199.
Chi, M. T. H., Feltovich, P. J., Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5(2), 121-152.
Chi, M. T. H. & Ohlsson, S. (2005). Complex declarative learning. In K. J. Holyak & R. G. Morrison (Eds.), The Cambridge handbook of thinking and reasoning (pp. 371-400). Cambridge: Cambridge University Press.
Chi, M. T. H., Roscoe, R., Slotta, J., Roy, M., & Chase, M. (2012). Misconceived causal explanations for emergent processes. Cognitive Science, 36(1), 1–61.
Chinn, C. A., & Hmelo-Silver, C. E. (2002). Authentic inquiry: Introduction to the special section. Science Education, 86(2), 171-174.
Chinn, C. A., & Malhotra, B. A. (2002). Epistemologically authentic inquiry in schools: A theoretical framework for evaluating inquiry tasks. Science Education, 86(2), 175–218.
Chinn, C. A., & Brewer, W. F. (1993). The role of anomalous data in knowledge acquisition: A theoretical framework and implications for science instruction. Review of Educational Research, 63(1), 1–49.
Chinn, C. A., & Brewer, W. F. (1998). An empirical test of a taxonomy of responses to anomalous data in science. Journal of Research in Science Teaching, 35(6), 623-654.
Chinn, C. A., & Brewer, W. F. (2001). Models of data: A theory of how people evaluate data. Cognition and Instruction, 19(3), 323-393.
Chinn, C. A., & Samarapungavan, A. (2009). Conceptual Change-Multiple Routes, Multiple Mechanisms: A Commentary on Ohlsson. Educational Psychologist, 44(1), 1-10.
Chiu, M. H., & Lin, J. W. (2005). Promoting fourth graders' conceptual change of their understanding of electric current via multiple analogies. Journal of Research in Science Teaching, 42(4), 424-468.
Clark, D. B. (2006). Longitudinal conceptual change in students' understanding of thermal equilibrium: An examination of the process of conceptual restructuring. Cognition and Instruction, 24(4), 467-563.
Clement, J. (1982). Students’ preconceptions in introductory mechanics. American Journal of Physics, 50(1), 66–71.
Clement, J. (1983). A conceptual model discussed by Galileo and used intuitively by physics students. In D. Gentner and A. L. Stevens (Eds.), Mental Models (pp. 325-340). Hillsdale, NJ: Lawrence Erlbaum Associates.
Clement, J. (1989). Learning via model construction and criticism: Protocol evidence on sources of creativity in science. In J. A. Glover, R. R. Ronning, and C. R. Reynolds (eds), Handbook of Creativity: Assessment, Theory and Research (pp. 341-381). New York: Plenum Press.
Clement, J. (1993). Using bridging analogies and anchoring intuitions to deal with students’ preconceptions in physics. Journal of Research in Science Teaching, 30(10), 1241-1257.
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. (2008). Six levels of organization for curriculum design and teaching. In: Clement J, Rea-Ramirez MA (eds.) Model Based Learning and Instruction in Science (pp. 255-272). New York, Springer.
College Board. (2009). Science: College Board standards for college success. New York: Author.
Confrey, J. (1990). A review of the research on student conceptions in mathematics, science and programming. In C. B. Cazdan (Ed). Review of Research in Education, 16, 3-56, Washington, DC: AERA.
Courtney, T. D. (1986). The significance of the SOLO taxonomy for learning and teaching in geography. Geographical Education, 5(2), 47–50.
Crawford, B. A. (1999). Is it realistic to expect a preservice teacher to create an inquiry-based classroom? Journal of Science Teacher Education, 10(3), 175 – 194.
Crawford, B. A., & Cullin, M. J. (2004). Supporting prospective teachers’ conceptions of modelling in Science, International Journal of Science Education, 26(11), 1379-1401.
Creedy, L. J. (1993). Student understandings of natural selection. Research in Science Education, 23(1), 34-41.
Daniel, L., Ortleb, E. P., & Biggs, A. (1995). Merrill life science. New York: McGraw-Hill.
Danusso, L., Testa, I. & Vicentini, M. (2010). Improving prospective teachers' knowledge about scientific models and modelling: Design and evaluation of a teacher education intervention. International Journal of Science Education, 32(7), 871-905.
DeBoer, G. E. (1991). A history of ideas in science education. New York: Teachers College.
Deboer, G. E. (2004). Historical perspectives on inquiry teaching in schools. In L.B. Flick, & N.G. Lederman (Eds.) Scientific Inquiry and Nature of Science. Implications for Teaching, Learning, and Teacher Education (pp. 17-35). The Netherlands: Kluwer Academic Publishers.
DeBoer, G. E., & Bybee, R. W. (1995). The goals of science curriculum. In R. W. Bybee and J. D. McInerney (Eds.), Redesigning the science curriculum, (pp. 77-74). Colorado Springs, CO: Biological Sciences Curriculum Study.
Devi, R., Tiberghien, A., Baker, M., & Brna, P. (1996). Modelling students’ construction of energy models in physics. Instructional Science, 24(4), 259–295.
diSessa, A. A. (1993). Towards an epistemology of physics. Cognition and Instruction, 10(2-3), 105-225.
diSessa, A. A. (2002). Why “conceptual ecology” is a good idea. In M. Limon & L. Mason (Eds.), Reconsidering conceptual change: Issues in theory and practice (pp. 29–60). Dortrecht: Kluwer.
diSessa, A. A., Gillespie, N. M., & Esterly, J. B. (2004). Coherence versus fragmentation in the development of the concept of force. Cognitive Science, 28(6), 843-900.
Doherty, E. J. S., & Evans, L. C. (1983). Primary independent study. Connecticut: Synergetics.
Doyle, J. K., & Ford, D. N. (1998). Mental models concepts for system dynamics research. System Dynamics Review, 14(1), 3-29.
Driver, R., Guesne, E., & Tiberghien, A. (1985). Children’s ideas in science. Milton Keynes, England: Open University Press.
Dunbar, K. (1995). How Scientists really reason: Scientific reasoning in real world laboratories. In R. J. Sternberg, & J. Davidson (Eds.) Mechanisms of insight(pp. 365-395). Cambridge: MA. MIT press.
Duit, R. (2009). Bibliography STCSE: Students’ and teachers’ conceptions and science education. Kiel, Germany: IPN—Leibniz Institute for Science Education (http://www.ipn.uni-kiel.de/aktuell/stcse/).
Duit, R., Roth, W., Komorek, M., & Wilbers, J. (2001). Fostering conceptual change by analogies - between Scylla and Charybdis. Learning and Instruction,11(4-5), 283-303.
Duschl, R., & Grandy, R. (Eds.). (2008). Teaching scientific inquiry: Recommendations for research and implementation. Rotterdam: Sense Publishers.
Dykstra, D. I., Boyle, C. F., & Monarch, I. A. (1992). Studying conceptual change in learning physics. Science Education, 76(6), 615-652.
Erduran, S. & Duschl, R. (2004). Interdisciplinary characterizations of models and the nature of chemical knowledge in the classroom. Studies in Science Education, 40(1), 105-138.
Eryilmaz, A. (2002). Effects of conceptual assignments and conceptual change discussions on students’ misconceptions and achievement regarding force and motion. Journal of Research in Science Teaching, 39(10), 1001–1015.
Etheredge, S. & Rudnitsky, A. (2003). Introducing students to scientific inquiry: How do we know what we know? Boston: Allyn & Bacon.
Flavell, J. (1979). Metacognition and cognitive monitoring: A new area of psychological inquiry. American Psychologist, 34(10), 906-911.
Flavell, J. H., Green, F. L., & Flavell, E. R. (1990). Developmental changes in children's knowledge about the mind. Cognitive Development, 5(1), 1-27.
Flick, L. B. (2000). Cognitive scaffolding that fosters scientific inquiry in middle level science. Journal of Science Teacher Education, 11(2), 109–129.
Francoeur, E. (1997). The forgotten tool: The design and use of molecular models. Social Studies of Science, 27(1), 7–40.
Fretz, E. B., Wu, H. K., Zhang, B., Davis, E. A., Krajcik, J. S., & Soloway, E. (2002). An investigation of software scaffolds supporting modeling practices. Research in Science Education, 32(4), 567–589.
Gabel, D. L. (1999). Improving teaching and learning through chemistry education research: A lock to the future, Journal of Chemical Education, 76(4), 548-554.
Gagné, E. D., Yekovich, C.W., & Yekovich, F. R. (1993). The cognitive psychology of school learning (2nd Edition). New York: Harper Collins.
Gardner, H. (1991). The Unschooled Mind: How Children Think and How Schools Should Teach. New York: Basic Books.
Garson, James. Connectionism, The Stanford Encyclopedia of Philosophy (Spring 2007 Edition), Edward N. Zalta (ed.), retrieved May 9, 2010 from http://plato.stanford.edu/archives/spr2007/entries/connectionism/
Germann, P. J., & Aram, R. J. (1998). Student performances on the science processes of recording data, analyzing data, drawing conclusions, and providing evidence. Journal of Research in Science Teaching, 33(7), 773-798.
Germann, P. J., Aram, R. J., & Burke, G. (1998). Identifying patterns and relationships among the responses of seventh-grade students to the science process skill of designing experiments. Journal of Research in Science Teaching, 33(1), 79-99.
Gibson, H. L., & Chase, C. (2002). Longitudinal impact of an inquiry-based science program on middle school students’ attitudes toward science. Science Education, 86(5), 693-705.
Giere, R. N. (1988). Explaining science: A cognitive approach. Chicago: University of Chicago Press.
Giere, R. N. (2010). An agent-based conception of models and scientific representation. Synthese, 172(2), 269-281.
Gilbert, J. K. (1995). The role of models and modelling in some narratives in science learning. Presented at the Annual Meeting of the American Educational Research Association, April 18-22. San Francisco, CA, USA.
Gilbert, J. K. (2004). Models and modeling : routes to more authentic science education. International Journal of Science and Mathematics Education, 2(2), 115-130.
Gilbert, J. K. (2005). Visualization: A metacognitive skill in science and science education. In J. K. Gilbert (Eds.), Visualization in science education (pp. 1-27). Dordrecht: Springer.
Gilbert, J. K. (2008). Visualization: An emergent field of practice and enquiry in science education. Visualization: Theory and practice in science education (pp.3-24). Dordrecht, Springer.
Gilbert, J. K. & Boulter, C. J. (1998). Learning science through models and modelling. In B. J. Fraser, & K. G. Tobin (Eds) International handbook of Science Education (pp.53-66). Dordrecht, Netherlands: Kluwer Academic Press.
Gilbert, J. K., Boulter, C., & Rutherford, M. (1998). Models in explanations, part 1: Horses for courses? International Journal of Science Education, 20(1), 83–97.
Gilbert, J. K., & Watts, D. M. (1983). Concepts, misconceptions and alternative conceptions: Changing perspectives in science education. Studies in Science Education, 10(1), 61–98.
Gilbert, S. W. (1991). Model building and a definition of science. Journal of Research in Science Teaching, 28(1), 73–80.
Gobert, J. D., & Buckley, B. C. (2000). Introduction to model-based teaching and learning in science education. International Journal of Science Education, 22(9), 891-894.
Gobert, J. D. & Discenna, J. (1997). The Relationship between Students' Epistemologies and Model-Based Reasoning. Kalamazoo, MI: Western Michigan University, Department of Science Studies. (ERIC Document Reproduction Service No.ED409164).
Gobert, J. D., & Pallant, A. (2004). Fostering students’ epistemologies of models via authentic model-based tasks. Journal of Science Education and Technology. 13(1), 7-22.
Gobert, J. Slotta, J., & Pallant, A. (2002). Inquiry Learning Through Students’ East-West Coast Collaboration. Presented at the National Association for Research in Science Teaching, April 7-11, New Orleans, LA.
Gobert, J., Snyder, J., & Houghton, C. (2002). The influence of students' understanding of models on model-based reasoning. Presented at the Annual 24 Meeting of the American Educational Research Association, April 1-5, New Orleans, LA.
Gobert, J. D., O’Dwyer, L., Horwitz, P., Buckley, B. C., Levy, S. T., Wilensky, U. (2011). Examining the relationship between students’ understanding of the nature of models and conceptual learning in biology, physics, and chemistry. International Journal of Science Education, 33(5), 653-684.
Goldberg, F. M., & Anderson, J. H. (1989). Student difficulties with gaphical representations of negative values of velocity. The Physics Teacher, 27(4), 254–260.
Goldstone, R. L., & Wilensky, U. (2008). Promoting transfer through complex systems principles. Journal of the Learning Sciences, 26(1), 465-516.
Greeno, J. G., & van de Sande, C. (2007). Perspectival understanding of conceptions and conceptual growth in interaction. Educational Psychologist, 42(1), 9–23.
Grosslight, L., Unger, C., Jay, E., & Smith, C. L. (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.
Gunstone, R. F. (1987). Student understanding in mechanics: A large population survey. American Journal of Physics, 55(8), 691–696.
Guth, J., & Pegg, J. (1994). First-year tertiary students’ understandings of iron flung patterns around a magnet. Research in Science Education, 24(1), 137-146.
Haack, S. (1993). Evidence and Inquiry: Towards Reconstruction in Epistemology, Oxford; Wiley-Blackwell.
Hacking, I. (1983). Representing and intervening. Cambridge, England: Cambridge University Press.
Halloun, I. A. (1996). Schematic modeling for meaningful learning of physics. Journal of Research in Science Teaching, 33(9), 1019-1041.
Halloun, I. A. (2004). Modeling Theory in Science Education. Kluwer Academic Publishers, Boston.
Halloun, I. A., & Hestenes, D. (1985). Common sense concepts about motion. American Journal of Physics, 53(11), 1056 – 1065.
Hanuscin, D. L., Akerson, V. L., & Phillipson, T. (2006). Integrating nature of science instruction into a physical science content course for preservice elementary teachers: NOS views of teaching assistants. Science Education, 90(5),912-935.
Harms, N. C. (1977). Project Synthesis: An interpretative consolidation of research identifying needs in natural science education (a proposal prepared for the National Science Foundation). Boulder: University of Colorado.
Harms, N. C., & Yager, R. E. (Eds.). (1981). What research says to the science teacher, Vol. 3. Washington, DC: National Science Teachers Association.
Harrison, A. G., & Treagust, D. F. (2000). A typology of school science models. International Journal of Science Education, 22(9), 1011–1026.
Haury, D. L. (1993) Teaching science through inquiry. Eric Document Reproduction Service No. ED 359048.
Helm, H., & Novak, J. D. (Eds.) (1983). Proceedings of the international seminar on misconceptions in science and mathematics. Department of Education, Cornell University, Ithaca, NY.
Hempel, C. G. (1965). Aspects of scientific explanation and other essays in the philosophy of science. New York, NY: Free Press.
Hempel, C. G., & Oppenheim, P. (1948). Studies in the logic of explanation. Philosophy of Science, 15(2), 135–175.
Hess, M. (1966). Models and Analogies in Science. Notre Dame University Press.
Hestenes, D. (1987). Toward a modeling theory of physical education. American Journal of Physics., 55(5), 440-454.
Hestenes, D. (1992). Modeling games in the Newtonian World. American Journal of Physics, 60(8), 732–748.
Hestenes, D. (1995). Modeling software for learning and doing physics. In C. Bernardini, C., Tarsitani,, & M. Vincentini (Eds.), Thinking physics for teaching (pp. 25-66). New York: Plenum.
Hestenes, D. (2006). Notes on modeling theory, Proceedings of the 2006 GIREP conference: Modelling in Physics and Physics Education.
Hestenes, D. (2010). Modeling theory for math and science education. In R. Lesh, P. L. Galbraith, C. R. Haines , & A. Hurford (Eds.), Modeling students’ mathematical modeling competencies (pp. 13-41). New York, Springer.
Hestenes, D., Wells, M. & Swackhamer, G. (1992). Force concept inventory. The Physics Teacher, 30(3), 141–158.
Milner-Bolotin, M. (2012). Increasing interactivity and authenticity of chemistry instruction through data acquisition systems and other technologies. Journal of Chemical Education, 89(4), 477–481.
Hinton, M. E. & Nakhleh, M. B. (1999). Students’ microscopic, macroscopic, and symbolic representations of chemical reactions, Chem. Educator, 4(5), 158-167.
Hodson, D. (1992). In search of a meaningful relationship: an exploration of some issues relating to integration in science and science education. International Journal of Science Education, 14 (5), 541–562.
Hofer, B. K. (2001). Personal epistemology research: Implications for learning and teaching. Educational Psychology Review, 13(4), 353–383.
Hofer, B. K., & Pintrich, P. R. (1997). The development of epistemological theories: Beliefs about knowledge and knowing and their relation to learning. Review of Educational Research, 67(1), 88–140.
Hofstein, A. & Lunetta, V. N. (2004). The laboratory in science education: Foundations for the twenty-first century. Science Education, 88(1), 28-54.
Hogan, K. & Thomas, D. (2001). Cognitive comparisons of students’ systems modelling in ecology. Journal of Science Education and Technology, 10(4), 319-345.
Holyoak, K. J. (1985). The pragmatics of analogical transfer. In G. H. Bower (Ed.), The Psychology of Learning and Motivation, Vol.19 (pp. 59-86). New. York: Academic Press.
Holyoak, K. J. (2005). Analogy. In K. J. Holyoak and R. G. Morison (Eds.), The Cambridge handbook of thinking and reasoning (pp. 117-142). Cambridge, UK: Cambridge University Press.
Ioannides, C., & Vosniadou, S. (2002). The changing meanings of force. Cognitive Science Quarterly, 2(1), 5-62.
Isaacson, W. (2007). Einstein: His life and universe. New York: Simon and Schuster.
Jakob. G., Per-Olof. W., & Sven-Olof. H. (2010). Teachers' Language on Scientific Inquiry: Methods of teaching or methods of inquiry? International Journal of Science Education, 32(9), 1151-1172.
Jeong, H., Songer, N. B., & Lee, S.-Y. (2007). Evidentiary competence: Sixth graders' understanding for gathering and interpreting evidence in scientific investigations. Research in Science Education, 37(1), 75-97.
Jimoyiannis, A., & Komis, V. (2001). Computer simulations in teaching and learning physics: A case study concerning students’ understanding of trajectory motion. Computers & Education, 36(2), 183-204.
Johnstone, A. H., (1991). Why is science difficult to learn? Things are seldom what they seem. Journal of Computer Assisted Learning, 7(2), 75-83.
Jonassen, D. (2008). Model Building for Conceptual Change. In S. Vosniadou (Ed.), International handbook of research on conceptual change (pp. 676-693). New York: Taylor & Francis - Routledge.
Jonassen, D., Strobel, J., & Gottdenker, J. (2005). Model building for conceptual change. Interactive Learning Environments, 13(1–2), 15–37.
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.
Kaberman, Z., & Dori, Y. J. (2009). Question posing, inquiry, and modeling skills of chemistry students in the case-based computerized laboratory environment. International Journal of Science and Mathematics Education, 7(3), 597-625.
Kaminski, J. A., Sloutsky, V. M., & Heckler, A. F. (2008). The advantage of abstract examples in learning math. Science, 320, 454–455.
Karagiannis, D., & Kühn, H. (2002) Metamodelling Platforms. In K. Bauknecht, A. Min Tjoa and G. Quirchmayer (eds.), Proceedings of the 3rd International Conference EC-Web 2002 - Dexa 2002, Aix-en-Provence, France, volume 2455 of Lecture Notes in Computer Science (pp. 182–196), Springer-Verlag.
Karplus, R. (1977). Science teaching and the development of reasoning. Journal of Research in Science Teaching, 14(2), 169-175.
Kempa, R. F., & Diaz, M. (1990). Students’ motivational traits and preferences for different instructional modes in science education: Part 2. International Journal of Science Education, 12(2), 205-216.
Keselman, A. (2003). Supporting inquiry learning by promoting normative understanding of multivariable causality. Journal of Research in Science Teaching, 40(9), 898-921.
Keys, C., & Bryan, L. A. (2001). Co-constructing inquiry-based science with teachers: Essential research for lasting reform. Journal of Research in Science Teaching, 38(6), 631–645.
Khan, S. (2007). Model-based inquiries in chemistry. Science Education, 91(6), 877–905.
Khishfe, R. (2008). The development of seventh graders' views of nature of science. Journal of Research in Science Teaching, 45(4), 470-.
Khishfe, R., & Abd-El-Khalick, F. (2002). Influence of explicit and reflective versus implicit inquiry-oriented instruction on sixth graders’ views of nature of science. Journal of Research in Science Teaching, 39(7), 551–578.
Kikas, E. (2003). University students’ conceptions of different physical phenomena. Journal of Adult Development,10(3), 139-150.
Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. Educational Psychologist, 41(2), 75–86.
Koponen, I. T., (2007). Models and modelling in physics education: A critical reanalysis of philosophical underpinnings and suggestions for revisions. Science & Education, 16(7-8), 751-773.
Kozma, R. & Russell, J. (1997). Multimedia and understanding: Expert and novice responses to different representations of chemical phenomena. Journal of Research in Science Teaching, 34(9), 949–968.
Kozma, R., & Russell, J. (2005). Modelling students becoming chemists: Developing representational competence. In J. K. Gilbert (Ed.), Visualization in science education (pp. 121–146). Dordrecht: Springer.
Krajcik, J. S., Blumenfeld, P. C., Marx, R. W., Bass, K. M., Fredricks, J., & Soloway, E. (1998). Inquiry in project-based science classrooms: initial attempts by middle school students. Journal of the Learning Sciences, 7(3&4), 313–350.
Krajcik, J. S., Czerniak, C., & Berger, C. (1998). Teaching children science in elementary and middle school classrooms: A project-based approach. New York: McGraw-Hill.
Kuhn, D. (2007). Reasoning about multiple variables: Control of variables is not the only challenge. Science Education, 91(5), 710–716.
Kuhn, T. S. (1970). The structure of scientific revolution. 2nd ed. Chicago: University of Chicago Press.
Kuhn, D., Black, J., Keselman, A., & Kaplan, D. (2000). The development of cognitive skills to support inquiry learning. Cognition and Instruction, 18(4), 495–523.
Kuhn, D. & Dean, D. (2005). Is developing scientific thinking all about learning to control variables? Psychological Science, 16(11), 866–870.
Kuhn, D., Pease, M., & Wirkala, C. (2009). Coordinating effects of multiple variables: A skill fundamental to causal and scientific reasoning. Journal of Experimental Child Psychology, 103(3), 268–284.
Kvanvig, J. (2007). Coherentist theories of epistemic justification. In: Zalta EN (ed.) Stanford Encyclopedia of Philosophy. Stanford, CA: Stanford University. Available at http://plato.stanford.edu/archives/fall2007/entries/justep-coherence/.
Larkin, J. (1983). The role of problem representation in physics. In D. Gentner & A. Stevens (Eds.), Mental models (pp. 75–98). Hillsdale, NJ: Erlbaum.
Larkin, J., McDermott, J., Simon, D., & Simon, H. (1980). Expert and novice performance in solving physics problems. Science, 208, 1335–1342.
Latour, B. (1987). Science in action: How to follow scientists and engineers through society. Cambridge, MA: Harvard University Press
Latour, B., & Woolgar, S. (1986). Laboratory life: The construction of scientific fact (2nd ed.). Princeton, NJ: Princeton University Press.
Lawson, A. E. (1995). Science teaching and the development of thinking. Belmont, CA: Wadsworth.
Leach, J., Hind, A., & Ryder, J. (2003). Designing and evaluating short teaching interventions about the epistemology of science in high school classrooms. Science Education, 87(6), 831–848.
Lederman, N. G. (1992). Students’ and teachers’ conceptions about the nature of science: A review of the research. Journal of Research in Science Teaching, 29(4), 331–359.
Lederman, N. G. (2004). Syntax of nature of science with inquiry and science instruction. In L. B. Flick & N. G. Lederman (Eds.), Scientific inquiry and nature of science (pp. 301 – 317). Dordrecht, The Netherlands: Kluwer.
Lederman, N. G., & Abd-El-Khalick, F. (1998). Avoiding de-natured science: Activities that promote understandings of the nature of science. In W. McComas (Ed.), The nature of science in science education: Rationales and strategies (pp. 83–126). Dordrecht, the Netherlands: Kluwer.
Lederman, N.G., Wade, P.D., & Bell, R.L. (1998). Assessing understanding of the nature of science: A historical perspective. In McComas, W. (Ed.), The nature of science in science education: Rationales and strategies (pp. 331–350). Dordrecht, The Netherlands: Kluwer Academic.
Lehrer, R., & Schauble, L. (2003). Origins and evolution of model-based reasoning in mathematics and science. In R. Lesh & H. M. Doerr (Eds.), Beyond constructivism: A models and modeling perspective on mathematics problem-solving, learning, and teaching (pp. 59-70). Mahwah, NJ: Lawrence Erlbaum Associates.
Lehrer, R., & Schauble, L. (2006). Cultivating model-based reasoning in science education. In K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 371–388). New York: Cambridge University Press.
Lehrer, R., and L. Schauble. (2010). What kind of explanation is a model? In M. K. Stein & L. Kucan (Eds.) Instructional explanations in the disciplines (pp. 9-22). New York, Springer.
Levins, L. (1992). Students' understanding of concepts related to evaporation. Research in Science Education, 22(1), 263-272.
Levy, S. T., & Wilensky, U. (2009). Students’ learning with the connected chemistry (CC1) curriculum: Navigating the complexities of the particulate world. Journal of Science Education and Technology, 18(3), 243-254.
Liang, L. L., Fulmer, G. W., Majerich, D. M., Clevenstine, R., & Howanski, R. (2012) The effects of a model-based physics curriculum program with a physics first approach: a causal-comparative study. Journal of Science Education and Technology, 21(1), 114–124.
Linderholm, T., Gaddy, M., van den Broek, P., Mischinski, M., Crittenden, A., & Samuels, S. J. (2000). Effects of causal text revisions on more- and less-skilled readers’ comprehension of easy and difficult texts. Cognition and Instruction, 18(4), 525–556.
Linn, M. C., & Muilenburg, L. (1996). Creating lifelong science learners: What models form a firm foundation? Educational Researcher, 25(5), 18-24.
Liu, S., & Lederman, N. G. (2002). Taiwanese gifted students’ views of nature of science. School Science and Mathematics, 102(3), 114–123.
Louca, L. T., Zacharia, Z. C. (2012). Modeling-based learning in science education: cognitive, metacognitive, social, material and epistemological contributions. Education review, 64(4), 471-492.
Louca, L. T., Zacharia, Z. C., & Constantinou, C. P. (2011). In quest of productive modeling-based learning discourse in elementary school science. Journal of Research in Science Teaching, 48(8), 919-951.
Maskiewicz, A. C., & Winters,V. C. (2012). Understanding the co-construction of inquiry practices: A case study of a responsive teaching environment. Journal of Research in Science Teaching, 49(4), 429-464.
McCloskey, M. (1983). Naïve theories of motion. In D. Gentner & A. Stevens (Eds.), Mental models (pp.299-324). Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.
McComas, W. F. (2004). Keys to teaching the nature of science. The Science Teacher, 71(9), 24-27.
McDermott, L. C., Rosenquist, M. L., & van Zee, E. H. (1987). Student difficulties in connecting graphs and physics: Examples from kinematics. American Journal of Physics, 55(6), 503–513.
McNeill, K. L. (2009). Teachers’ use of curriculum to support students in writing scientific arguments to explain phenomena. Science Education, 93(2), 233 – 268.
McNeill, K. L., Lizotte, D. J., Krajcik, J. S., & Marx, R. W. (2006). Supporting students’ construction of scientific explanations by fading scaffolds in instructional materials. Journal of the Learning Sciences, 15(2), 153–191.
McNeill, K. L., & Krajcik, J. (2012). Claim, evidence and reasoning: Supporting grade 5-8 students in constructing scientific explanations. New York: Pearson Allyn & Bacon.
Mestre, J. P. (1991). Learning and instruction in pre-college physical science. Physics Today, 44(9), 56-62.
Metz, K. E. (1991). Development of explanation: Incremental and fundamental change in children’s physics knowledge. Journal of Reserach in Science Teaching, 28(9), 785-797.
Michalsky, T. (2010). Shaping self-regulation in science teachers' professional growth: Inquiry skills. Science Education, 96(6), 1106-1133.
Minogue, J., & Jones, M. G. (2009). Measuring the impact of haptic feedback using the SOLO taxonomy. International Journal of Science Education. 31(10), 1359-1378.
Minstrell, J. (1982). Explaining the “at-rest” condition of an object. The Physics Teacher, 20(1), 10–14.
Minstrell, J. (n.d.). Facets of students’ thinking. Retrieved October 27, 2006, from http://depts.washington.edu/huntlab/diagnoser/facetcode.html.
Niss, M. (2009). Metamodelling messages conveyed in five statistical mechanical textbooks from 1936 to 2001, International Journal of Science Education, 35 (5), 697-719.
Morrison, J. A., Raab, F., & Ingram, D. (2009). Factors influencing elementary and secondary teachers’ views on the nature of science. Journal of Research on Science Teaching, 46(4), 384–403.
Mortimer, E. F. (1995). Conceptual change or conceptual profile change? Science and Education, 4(3), 267-285.
Nagel, E. (1961). The Structure of Science. Problems in the Logic of Scientific Explanation. New York: Harcourt, Brace and World.
National Research Council. (1996). National Science Education Standards. Washington, DC: National Academic Press.
National Research Council. (2000). Inquiry and the National Science Education Standards. Washington, DC: National Academic Press.
National Society for the Study of Education. (NSSE). (1960). Rethinking science education: Fifty-ninth yearbook of the NSSE, Part I. Chicago: University of Chicago Press.
Nersessian, N. (1992), How do scientists think? Capturing the dynamics of conceptual change in science, In R. Giere (Ed.), Cognitive models of science Minnesota studies in the philosophy of science, Vol. 15, Minneapolis: University of Minnesota Press.
Nersessian, N. J. (2002). The cognitive basis of model-based reasoning in science. In Carruthers, P., Stich, S. & Siegal, M. (eds.) The Cognitive Basis of Science, (pp. 133－153) Cambridge University Press.
Nersessian, N . J ., & Resnick, L. B. (1989). Comparing historical and intuitive explanations of motion : Does "naive physics" have a structure? In Proceedings of the Eleventh Annual Conference of the Cognitive Science Society (pp. 412-420). Hillsdale, NJ : Lawrence Erlbaum Associates, Inc .
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.
Novak, J. D. (2002). Meaningful learning: The essential factor for conceptual change in limited or inappropriate propositional hierarchies leading to empowerment of learners. Science Education, 86(4), 548-571.
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.
Osborne, J. F., & Patterson, A. (2011). Scientific argument and explanation: A necessary distinction? Science Education, 95(4), 627–638.

Otero, J., Campanario, J. M., & Hopkins, K. D. (1992). The relationship between academic achievement and metacognitive comprehension monitoring ability of Spanish secondary school students. Educational and Psychological Measurement, 52(2), 419–430.
Palmer, D. H. & Flanagan, R. B. (1997). Readiness to change the conception that “Motion- Implies-Force”: A comparison of 12-year-old and 16-year-old students. Science Education, 81(3), 317–331.
Papaevripidou, M., Constantinou, C. P., & Zacharia, Z. C. (2007). Modeling complex marine ecosystems: Using Stagecast CreatorTM to foster fifth graders’ development of modeling skills. Journal of Computer Assisted Learning, 23(2), 145–157.
Passmore, C. Stewart, J. & Cartier, J. (2009). Model-based inquiry and school science: creating connections. School Science and Mathematics, 109(7), 394-402.
Perkins, D. N. (1986). Knowledge as Design. Hillsdale, NJ: Lawrence Erlbaum Assoc. Publ.
Penner, D. E. (2001). Cognition, computers, and synthetic science: Building knowledge and meaning through modelling. Review of Research in Education, 25, 1-36.
Penner, D, Giles, N, Lehrer, R, & Schauble, L. (1997). Building functional models: designing an elbow. Journal of Research in Science Teaching, 34(2), 125–143.
Piaget, J. (1969). The mechanisms of perception. London: Routledge & Kegan Paul.
Pluta, J. W., Chinn, A. C., & Duncan, G. R. (2011). Learners’ epistemic criteria for good scientific models. Journal of Research in Science Teaching, 48(5), 486–511.
Posner, G. J., Strike, K. A., Hewson, P. W., & Gertzog, W. A. (1982). Accommodation of a scientific conception: Toward a theory of conceptual change. Science Education, 66(2), 211-277.
Prins, G. T., Bulte, A. M. W., & Pilot, A. (2011). Evaluation of a design principle for fostering students’ epistemological views on models and modelling using authentic practices as contexts for learning in chemistry education. International Journal of Science Education, 33(11), 1539–1569.
Raghavan, R., Sartoris, M. L., & Glaser, R. (1998). Interconnecting science and mathematics concepts. In R. Lehrer & D. Chazan (Eds.). Designing Learning Environments for Developing Understanding of Geometry and Space. Mahwah, N.J.: Lawrence Erlbaum Associates.
Rea-Ramirez, M. A., Clement, J., & Nunez-Oviedo, M. C. (2008). An instructional model derived from model construction and criticism theory. In J. J. Clement & M. A. Rea-Ramirez (eds.), Model based learning and instruction in science (pp. 23-43). Netherlands: Springer.
Reiner, M., Slotta, J. D., Chi, M. T. H., & Resnick, L. B. (2000). Naive physics reasoning: A commitment to substance-based conceptions. Cognition and Instruction, 18(1), 1 – 34.
Rezba, R. J., Sprague, C., Fiel, R. L., Funk, H. J., Okey, J. R., & Jaus, H. H. (1995). Learning and Assessing Science Process Skills. Dubuque: Kendall & Hunt Publishers.
Rosenquist, M. L., & McDermott, L. C. (1987). A conceptual approach to teaching kinematics. American Journal of Physics, 55(5), 407-415.
Rouse, W. B., & Morris, N. M. (1986). On looking into the black box: Prospects and limits in the search for mental models. Psychological Bulletin, 100(3), 349–363.
Roth, W. -M. (1996). The co-evolution of situated language and physics knowing. Journal of Science Education and Technology, 5(3), 171-191.
Roth, K., & Garnier, H. (2007). What science teaching looks like: An international perspective. Educational Leadership, 64(4), 16 – 23.
Roth, W. M., & Roychoudhury, A. (1993). The development of science process skills in authentic contexts. Journal of Research in Science Teaching, 30(2), 127–152.
Rubin, R, L., & Norman, J, T. (1992). Systematic modeling versus the learning cycle: Comparative effects on integrated science process skill achievement. Journal of Research in Science Teaching, 29 (7), 715-727.
Rutherford, F. J. (1964). The role of inquiry in science teaching. Journal of Research in Science Teaching, 2(2), 80–84.
Salmon, W. C. (1989). Four decades of scientific explanation. Minneapolis: University of Minnesota Press.
Sandoval, W. A. & Çam, A. (2011). Elementary children's judgments of causal justifications. Science Education. 95(3), 383-408.
Sandoval, W. A., & Reiser, B. J. (2004). Explanation-driven inquiry: integrating conceptual and epistemic supports for science inquiry. Science Education, 88(3), 345-372.
Scharmann, L. C., Smith, M. U., James, J. C., & Jensen, M. (2005). Explicit reflective nature of science instruction: Evolution, intelligent design & umbrellaology. Journal of Science Teacher Education, 16(1), 27-41.
Scheiter, K., Gerjets, P., & Catrambone, R. (2006). Making the abstract concrete: Visualizing mathematical solution procedures. Computers in Human Behavior, 22(1), 9–25.
Schroeder, C. M., Scott, T. P., Tolson, H., Huang, T.-Y., & Lee, Y.-H. (2007). A meta-analysis of national research: Effects of teaching strategies on student achievement in science in the United States. Journal of Research in Science Teaching, 44(10), 1436–1460.
Schwab, J. J. (1962). The teaching of science as enquiry. In J. J. Schwab & P. F. Brandwein (Eds.), The Teaching of Science (pp. 1-103). Cambridge, MA: Harvard University Press.
Schwartz, R. S., Lederman, N. G., & Crawford, B. (2004). Developing views of nature of science in an authentic context: An explicit approach to bridging the gap between nature of science and scientific inquiry. Science Education, 88(4), 610–645.
Schwarz, C. V. (2002). Is there a connection? The role of meta-modeling knowledge in learning with models. In the Proceedings of the International Conference of the Learning Sciences. Seattle, WA.
Schwarz, C. V., & Gwekwerere, Y. N. (2007). Using a guided inquiry and modeling instructional framework (EIMA) to support preservice K-8 science teaching. Science Education, 91(1), 158–187.
Schwarz, C. V., Reiser, B. J., Davis, E. A., Kenyon, L, Acher, A., Fortus, D., … Krajcik, J. (2009). Designing a learning progression for scientific modeling: Making scientific modeling accessible and meaningful for learners. Journal for Research in Science Teaching, 46(6), 632-654.
Schwarz, C. V., & White, B. Y. (2005). Metamodeling knowledge: Developing students’ understanding of scientific modeling. Cognition and Instruction, 23(2), 165–205.
Sensevy, G., Tiberghien, A., Santini, J., Laube, S., & Griggs, P. (2008). An epistemological approach to modeling: Cases studies and implications for science teaching. Science Education, 92(3), 424–447.
Shrigley, R. L. (1990). Attitude and behavior correlates. Journal of Research in Science Teaching, 27(2), 97-113.
Shternberg, B., & Yerushalmy,M. (2003). Models of functions and models of situations: On the design of modeling-based learning environments. In R. Lesh & H.M. Doerr (Eds.), Beyond constructivism: Models and modeling perspectives on mathematics teaching, learning, and problem solving (pp. 479–498). Mahwah, NJ: Lawrence Erlbaum Associates, Inc.
Sins, P. H. M., Savelsbergh, E. R., & van Joolingen, W. R. (2005). The difficult process of scientific modeling: An analysis of novices’ reasoning during computer-based modeling. International Journal of Science Education, 27(14), 1695–1721.
Sins, P. H. M., Savelsbergh, E. R., van Joolingen, W. R., & van Hout-Wolters, B. H. A. M. (2009). The relation between students’ epistemological understanding of computer models and their cognitive processing on a modelling task. International Journal of Science Education, 31(9), 1205–1229.
Smith, C., Maclin, D., Houghton, C., & Hennessy, G. (2000). Sixth-grade students’ epistemologies of science: the impact of school science experiences on epistemological development. Cognition and Instruction, 18(3), 349-422.
Smith, C., Snir, J., & Grosslight, L. (1992). Using conceptual models to facilitate conceptual change: the case of weight-density differentiation, Cognition and Instruction, 9(3), 221-283.
Snir, J., Smith, C. L., and Raz, G. (2003). Linking phenomena with competing underlying models: A software tool for introducing students to the particulate model of matter. Science Education, 87(6), 794-830.
Snyder, J. L. (2000). An investigation of the knowledge structures of experts, intermediates and novices in physics. International Journal of Science Education 22(9), 979–992.
Songer, N. B., Kelcey, B., & Gotwals, A. W. (2009). How and when does complex reasoning occur? Empirically driven development of a learning progression focused on complex reasoning about biodiversity. Journal of Research in Science Teaching, 46(6), 606-609.
Stake, R. & Easley, J., (1978). Case Studies in Science Education. Urbana, IL: The University of Illinois.
Stewart, J., Cartier, J. L., & Passmore, C. M. (2005). Developing understanding through model-based inquiry. In M. S. Donovan & J. D. Bransford (Eds.), How students learn (pp. 515–565). Washington, DC: National Research Council.
Stewart, J., Hafner, R., Johnson, S., & Finkel, E. (1992). Science as model building: Computers and high-school genetics. Educational Psychologist, 27(3), 317–336.
Steyvers, M., Tenenbaum, J. B., Wagenmakers, E., & Blum, B. (2003). Inferring causal networks from observations and interventions. Cognitive Science, 27(3), 453-489.
Strike, K. A., & Posner, G. J. (1992). A revisionist theory of conceptual change. In Dushl & Hamilton (Eds.), Philosophy of Science, Cognitive Psychology, and Educational Theory and Practice. Albany, NY: State University of New York Press.
Suppe, F. (1977). The search for philosophic understanding of scientific theories. in F. Suppe (ed.). The Structure of Scientific Theories (2nd edition). Urbana: University of Illinois Press.
Suppes, P. (1961). A comparison of the meaning and use of models in mathematics and the empirical sciences. In H. Freudenthal (ed.) The Concept and the Role of the Model in Mathematics and Natural and Social Sciences (pp. 163-177), Dordrecht, The Netherlands: Reidel.
Tamir, P. (1983). Inquiry and the science teacher. Science Teacher Education, 67(5), 657-672.
Tamir, P., Stavy, R., & Ratner, N. (1998). Teaching science by inquiry: Assessment and learning. Journal of Biological Education. 33(1), 27-32.
Tang, X., Coffey, J., Elby, A., & Levin, D. (2010). The scientific method and scientific inquiry: Tensions in teaching and learning. Science Education, 94(1), 29–47.
Tao, P.-K. (2003). Eliciting and developing junior secondary students’ understanding of the nature of science through a peer collaboration instruction in science stories. International Journal of Science Education, 25(2), 147-171.
Thagard, P. (1992). Conceptual revolutions. Princeton, NJ: Princeton University Press.
Thomas, G. (2003). Conceptualisation, development and validation of an instrument for investigating the metacognitive orientations of science classroom learning environments: The Metacognitive Orientation Learning Environment Scale–Science (MOLES–S). Learning Environment Research, 6(2), 175–197.
Toth, E. E., Suthers, D. D., & Lesgold, A. (2002). “Mapping to know”: the effects of representational guidance and reflective assessment on scientific inquiry. Science Education, 86(2), 264-286.
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.
Trowbridge, D. E., & McDermott, L. C. (1980). Investigation of student understanding of the concept of velocity in one dimension. American Journal of Physics, 48(12), 1020-1028.
Trowbridge, D. E., & McDermott, L. C. (1981). Investigation of student understanding of the concept of acceleration in one dimension. American Journal of Physics, 49(3), 242-253.
Trumper, R., & Gorsky, P. (1996). A cross-college age study about physics students’ conceptions of force in pre-service training for high school teachers. Physics Education, 31(4), 227 – 235.
Tyson, L. M., Venville, G., Harrison, A. G., & Treagust, D. F. (1997). A multidimensional framework for interpreting conceptual change events in the classroom. Science Education, 81(4), 387-404.
van Driel, J. & Verloop, N. (1999). Teachers' knowledge of models and modelling in science. International Journal of Science Teaching, 21(11), 1141-1153.
van Joolingen, W. R. (2004). A tool for the support of qualitative inquiry modeling. In Kinsuk, C.K. Looi, E. Sutinen, D. Sampson, I. Aedo, L. Uden, & E. Kähkönen (Eds.), Proceedings of the 4th IEEE conference on advanced learning.
van Kraayenoord C. E., & Schneider W. E. (1999). Reading achievement; metacognition, reading self-concept and interest: a study of German students in grades 3 and 4. European Journal of Psychology of Education, 14(3), 305-324.
van Zele, E., Lenaerts, J. & Wieme, W. (2004). Improving the usefulness of concept maps as a research tool for science education. International Journal of Science Education, 26(9), 1043-1064.
Veenman, M. V. J., & Beishuizen, J. J. (2004). Intellectual and metacognitive skills of novices while studying texts under conditions of text difficulty and time constraint. Learning and Instruction, 14(6), 619–638.
Veenman, M. V. J., Van Hout-Wolters, B. H. A. M., & Afflerbach, P. (2006). Metacognition and learning: Conceptual and methodological considerations. Metacognition and Learning, 1(1), 3–15.
Vionnet, L. (1979). Spontaneous reasoning in elementary dynamics. European Journal of Science Education, 1(2), 205.
Vosniadou, S. (1994). Capturing and modeling the process of conceptual change. Learning and instruction, 4(1), 45–69.
Vosniadou, S., & Brewer, W. F. (1992). Mental meanings of the earth: A study of conceptual change in childhood. Cognitive Psychology, 24(4), 535-585.
Vosniadou, S., Skopeliti, I., & Ikospentaki, K. (2004). Modes of knowing and ways of reasoning in elementary astronomy. Cognitive Development, 19(2), 203-222.
Wang, C. Y. & Barrow, L. H. (2011). Characteristics and levels of sophistication: An analysis of chemistry students’ ability to think with mental models. Research in science education, 41(4), 561-586.
Weaver, G. C. (1998). Strategies in K-12 science instruction to promote conceptual change. Science Education, 82(4), 455–472.
Weiss, I. R. (1978). Report of the 1977 national survey of science, mathematics, and social studies education: Center for educational research and evaluation. Washington, DC: U.S. Government Printing Office.
Welch, W. W., Klopfer, L., Aikenhead, G., & Robinson, J. (1981). The role of inquiry in science education: Analysis and recommendations. Science Education, 65(1), 33–50.
Wells, M., Hestenes, D., & Swackhamer, G. (1995). A modeling method for high school physics instruction. American Journal of Physics, 63(7), 606–620.
West, L. H. T., & Pines, A. L. (1985). Cognitive structure and conceptual change. New York: Academic Press.
White, B. (1993). Tinker tool: Causal models, conceptual change, and science education. Cognition and Instructions, 1(1), 69-108.
White, B. Y., Collins, A., & Frederiksen, J. R. (2011). The nature of scientific meta-knowledge. In M. S. Khine & I. Saleh (Eds.) Models and modeling: Cognitive tools for scientific enquiry (pp. 41–76). London, UK: Springer.
White, B., & Frederiksen, J. (1998). Inquiry, modeling, and metacognition: Making science accessible to all students. Cognition and Instruction, 16(1), 3-118.
Windschitl, M. (2004). Folk theories of “inquiry”: How preservice teachers reproduce the discourse and practices of an atheoretical scientific method. Journal of Research in Science Teaching, 41(5), 481–512.
Windschitl, M., & André, T. (1998). Using computer simulations to enhance conceptual: The roles of constructivist instruction and student epistemological beliefs. Journal of Research in Science Teaching, 35(2), 145–160.
Windschitl, M., Thompson, J., & Braaten, M. (2008). Beyond the scientific method: model-based inquiry as a new paradigm of preference for school science investigations. Science Education, 92(5), 941–967.
Wise, K. C. & Okey, J. R. (1983). A meta-analysis of the effects of various science teaching strategies on achievement. Journal of Research in Science Teaching, 20(5), 419-435.
Wu, H. -K. (2010). Modeling a complex system: Using novice-expert analysis for developing an effective technology-enhanced learning environment. International Journal of Science Education, 32(2), 195-219.
Yacoubian, H.A., & BouJaoude, S. (2010). The effect of reflective discussions following inquiry based laboratory activities on students’ views of nature of science. Journal of Research in Science Teaching, 47(10), 1229–1252.
Zhang, L. J. (2001). Awareness in reading: EFL students’ metacognitive knowledge of reading strategies in an acquisition-poor environment. Language Awareness, 10(4), 268–288.
Zimmerman, C. (2007). The development of scientific thinking skills in elementary and middle school. Developmental Review, 27(2), 172–223.
Zion, M., Cohen, S., & Amir, R. (2007). The spectrum of dynamic inquiry teaching practices. Research in Science Education, 37(4), 423–447.
Zion, M., Michalsky, T., & Mevarech, Z. R. (2005). The effects of metacognitive instruction embedded within an asynchronous learning network on scientific inquiry skills. International Journal of Science Education, 27(8), 957–983.
Zohar, A., & Ben, D. A. (2008). Explicit teaching of meta-strategic knowledge in authentic Classroom situations , Metacognition and Learning, 3 (1), 59-82.
Zohar, A., & Peled, B. (2008). The effects of explicit teaching of metastrategic knowledge on low- and high-achieving students. Learning and Instruction, 18(4), 337–353.
Zuckerman, M., Kieffer, S. C., & Knee, C. R. (1998). Consequences of self-handicapping: Effects on coping, academic performance and adjustment. Journal of Personality and Social Psychology, 74(6), 1619-1628.