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研究生: 詹婉約
Chan, Wan-Yueh
論文名稱: 中學生的視覺表徵能力與分子概念理解之探究
Investigating High School Students’ Visual Representational Ability and Molecular Conceptual Comprehension
指導教授: 邱美虹
Chiu, Mei-Hung
學位類別: 碩士
Master
系所名稱: 科學教育研究所
Graduate Institute of Science Education
論文出版年: 2010
畢業學年度: 98
語文別: 中文
論文頁數: 120
中文關鍵詞: 視覺化表徵分子結構
英文關鍵詞: visualization, representation, molecular structure
論文種類: 學術論文
相關次數: 點閱:226下載:10
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  • 本研究旨在探討學生對視覺表徵及分子概念的理解、以及視覺表徵及分子概念理解之間的關連。本研究發展視覺表徵能力指標(Visual representational ability index, VRAI),透過解構的方式瞭解視覺表徵能力,視覺表徵能力指標具有兩大向度,一為視覺表徵能力的四個成分,這些成分為化學中重要的視覺化技能,即知覺、詮釋、轉換和連結、及選擇和評估;二為視覺表徵能力層級,由層級一到層級五。本研究具有兩個測驗工具,一為視覺表徵能力測驗,一為分子概念理解測驗,預試對象為25位九年級學生,信度Cronbach’s Alpha分別為0.88及0.84,正式施測對象為343位國中學生。
    研究結果包含視覺化表徵能力、分子概念理解、及視覺表徵能力層級的部分。在視覺化表徵能力方面,第一,由成分一「知覺」的研究結果可知學生對表徵的理解可能與以下因素有關,(一)當表徵中呈現的是單一訊息時,較多重訊息的表徵理解為佳;(二)即便表徵所體現的同為巨觀層級的概念,可在日常經驗中觀察的概念較無法在日常經驗觀察的概念理解為佳;(三)即便只需要對表徵做表面性的描述,微觀表徵對學生而言還是概念負載的;(四)學生傾向於以化學式的表面特徵詮釋表徵。第二,由成分二「詮釋」的研究結果顯示,(一)概念負載有助於學生對表徵的語法規則描述;(二)學生偏向於以表徵所觀察到的表面特徵解釋表徵。第三,由成分三「轉換與連結」的研究結果顯示,大部分學生偏向於以表徵所觀察到的表面特性做表徵間的連結。第四,由成分四「選擇和評估」的研究結果歸納學生對表徵的理解可能有以下趨勢,(一)當表徵所體現的概念具有過程屬性時,較易被學生理解,當表徵所體現的概念牽涉分子本身的認識時,學生較不易理解;(二)學生無法由二維的表徵知覺三維的空間相對位置。
    在分子結構概念理解方面,由巨觀層級的研究結果可知,當概念牽涉物質的特性時,則學生的答題表現較那些可直接觀察的概念為低。由微觀層級的研究結果可知,(一)學生傾向於以物質粒子的觀點詮釋原子與分子;(二)學生對化學過程相關概念的理解較原子與分子本身的理解為佳。
    在視覺表徵能力層級方面,整合四個成分的整體結果進入「視覺表徵能力指標(VRAI)」後,發現以下幾點,(一)高表現的學生也可能無法在所有解釋表徵的情境下,採取語意層次的解釋;(二)低表現的學生僅能描述巨觀表徵所體現的現象,即便僅需對微觀表徵的外觀進行描述,也無法選擇出正確答案。
    本研究針對教學及研究提出幾點建議,就教學建議來說,(一)有關原子與分子觀的概念,即便教科書中包含了各種原子與分子相關的視覺表徵,但可能較不容易被學生理解,在教學上需要被強調;(二)在學習原子與分子之概念時,或許能夠以學生較熟悉的化學過程導入,或者以簡單的過程符號進行原子、分子屬性的解說;(三)教學時宜幫助學生釐清物質粒子觀與原子分子觀的異同;(四)加強學生對微觀表徵的閱讀能力。就研究建議來說,(一)「表徵解構架構修正」中不具有將不同能力學習者分級或歸類的標準,為待確立的表徵架構;(二)閱讀微觀表徵及符號對學習者而言是個複雜的過程,這個過程需要進一步的研究發現。

    This research examined students’ comprehension of visual representations, conceptions of molecule, and their correlation. In order to deconstruct the visual representational competence, the present study developed a taxonomy of visual representational ability index (VRAI) and an instrument, the components were included in the VRAI framework, that is perception, interpretation, transformation and connection, selection and evaluation. The design of levels of visualization skills were also included in the VRAI. The present study developed two instruments, one is visual representational competence test, and the other is molecule conceptions comprehension test. The internal reliability were .88 and .84 respectively.
    The results of visual representational competence revealed that (1) Students comprehended better in representations with single information than in representations with multiple information. (2) Students had difficulties in describing the surface features of representations for the microscopic phenomenon, which were concept-laden to students. (3) Students who held some concepts of molecules could describe the representations based on syntactic rules. (4) Students tended to interpret and connect different representations based on surface features. (5) Students had a lot of difficulties in describing representations which involved the nature of molecule. (6) Students had difficulties in perception of three-dimensional information.
    The results of molecule comprehension revealed that (1) Students tended to interpret the concepts of molecule based on the concepts of the nature of particles. (2) Students comprehended the concepts of chemical process than the concepts of the nature of molecule.
    The results of visual representational competence revealed that (1) Some of the high performance students could not interpret representations based on semantic meaning. (2) Low performance students could not describe the surface features of microscopic representations.
    Several pedagogical implications could be drawn from this study. (1) It appeared that students had limited comprehension in different molecule-related representations, which need to be emphasized in learning. (2) It seems that understanding of process-related concepts may promote students deep understanding of the concepts of molecule. (3) It may be helpful for students to learn how to distinguish the nature of particles and the perspective of molecule.

    目次 第壹章 緒論 1 第一節 研究動機 1 第二節 研究背景 3 第三節 研究目的與問題 5 第四節 名詞釋義 7 第五節 研究範圍與限制 9 第貳章 文獻探討與分析 11 第一節 視覺化表徵 11 第二節 視覺化表徵與化學學習 27 第三節 分子相關概念與化學學習 34 第參章 研究方法 37 第一節 研究設計 37 第二節 研究對象 40 第三節 研究工具 42 第四節 資料處理與分析 46 第肆章 研究結果與分析 49 第一節 視覺表徵能力測驗表現 49 第二節 視覺表徵能力層級 68 第三節 分子概念理解 74 第四節 視覺表徵與分子概念理解 85 第伍章 結論與建議 91 第一節 結論與討論 91 第二節 建議 99 參考文獻 101 附錄 109 附件一:概念理解測驗 109 附件二:視覺表徵能力測驗 113 表次 表2-1-1 表徵的維度和層級及其實例 21 表2-2-1 視覺表徵能力指標(VAI) 30 表2-2-1 視覺表徵能力指標(VAI) 39 表3-3-1 視覺表徵能力判準與表徵能力試題對應 44 表3-3-2 分子概念理解測驗雙向細目表 45 表4-1-1 視覺表徵能力測驗表現在各成分答對率 50 表4-1-2 視覺表徵的理解在知覺的答對率 51 表4-1-3 液體的體積答題表現 52 表4-1-4 溶解與沸點答題表現 52 表4-1-5 水分子的重量與水分子的形狀答題表現比較 53 表4-1-6 反應物的判斷與生成物的判斷答題表現比較 54 表4-1-7 水分子的組成答題表現 55 表4-1-8 視覺表徵的理解在詮釋的答對率 56 表4-1-9 原子守恆答題表現 57 表4-1-10 去概念化命名規則及概念化命名規則答題表現比較 58 表4-1-11 定比定律答題表現 58 表4-1-12 H2O的性質答題表現 59 表4-1-13 水的生成答題表現 60 表4-1-14 定比定律答題表現 60 表4-1-15 視覺表徵的理解在轉換和連結的答對率 61 表4-1-16 氯化鈉的表徵答題表現 62 表4-1-17 烷類的表徵答題表現 62 表4-1-18 視覺表徵的理解在選擇和評估的答對率 63 表4-1-19 原子的排列答題表現 64 表4-1-20 熱含量答題表現 64 表4-1-21 電子的分布答題表現 65 表4-1-22 分子的結構答題表現 65 表4-1-23 電子的共用答題表現 66 表4-2-1 視覺表徵能力指標分級標準 68 表4-2-2與原架構相符之表徵能力層級人數百分比 71 表4-2-3 與原架構相異之表徵能力層級人數百分比 72 表4-3-1 概念理解在巨觀概念的答對率 75 表4-3-2 液體的狀態答題表現 75 表4-3-3 物質三態的各種特徵答題表現 76 表4-3-4 物質狀態的判斷答題表現 76 表4-3-5 溶解與沸點答題表現 77 表4-3-6 純物質的判斷與純物質與混合物的特性答題表現比較 78 表4-3-7 概念理解在微觀概念的答對率 79 表4-3-8 粒子的堆積答題表現 79 表4-3-9 氣體粒子答題表現 80 表4-3-10 粒子的本質答題表現 80 表4-3-11 水分子的形狀答題表現 81 表4-3-12 純物質與混合物的特性與水分子的組成答題表現比較 82 表4-3-13 分子的大小答題表現 82 表4-3-14 原子與化學反應答題表現 83 表4-3-15 分子與化學反應與蠟燭的燃燒答題表現比較 84 表4-3-1 概念理解測驗與視覺表徵能力測驗在液體的狀態答題表現比較 85 表4-3-2 概念理解測驗與視覺表徵能力測驗在溶解與沸點答題表現比較 86 表4-3-3 概念理解測驗與視覺表徵能力測驗在水分子的形狀答題表現比較 87 表4-3-4 概念理解測驗與視覺表徵能力測驗在水分子的組成答題表現比較 87 表4-3-5 概念理解測驗與視覺表徵能力測驗在原子守恆答題表現比較 88 表4-3-6 概念理解測驗與視覺表徵能力測驗答題表現比較 89 表5-1-1 表徵能力指標修正及定義 97 表5-1-2 表徵能力指標修正及舉例 97 圖次 圖2-1-1 模型與現象的交互關係 15 圖2-1-2 語言及非語言符號系統基模 17 圖2-1-3 二元編碼理論的視覺和概念系統 18 圖2-2-1 表徵能力測驗學生答題範例 28 圖3-1-1 研究架構與研究問題對應 38 圖5-1-1 由二元編碼的觀點看視覺表徵能力 98

    邱美虹、傅化文(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,54(2),145-151。
    廖焜熙、邱美虹(1996)。三維度視覺在技能與化學學習上的探討。科學教育月刊,189,14-36。
    Ainsworth, S.E (2006) DeFT: A conceptual framework for learning with multiple representations. Learning and Instruction, 16(3), 183-198.
    Amman, K., & Cetina, K. (1990). The fixation of (visual) evidence. In M. Lynch & S. Woolgar (Eds.), Representation in scientific practice (pp. 85-122). Cambridge, MA: MIT Press.
    Ardac, D., & Akaygun, S. (2004). Effectiveness of multimedia-based instruction that emphasizes molecular representations on students' understanding of chemical change. Journal of Research in Science Teaching, 41(4), 317-337.
    Ardac, D., & Akaygun, S. (2005). Using static and dynamic visuals to represent chemical change at molecular level. International Journal of Science Education, 27(11), 1269–1298.
    Barnea, N. (2000). Teaching and learning about chemistry and modelling with a computer managed modelling system. In J. K. Gilbert & C. J. Boulter (Eds.), Developing Models in Science Education. Dordrecht: Kluwer Academic.
    Benson, D. L. (1993). Students’ preconceptions of the nature of gases. Journal of Research in Science Teaching, 30(6), 587-597.
    Ben-Zvi, R., Eylon, B., & Silberstein, J. (1986). Is an atom of copper malleable? Journal of Chemical Education, 63, 64-66.
    Boulter, C., & Buckley, B. (2000). Constructing a typology of models for science education. In J. Gilbert & C. Boulter (Eds.), Developing models in science education. (pp. 3-17). Netherlands: Kluwer.
    Bowen, G. M., & Roth, W.-M. (2005). Data and graph interpretation practices among preservice science teachers. Journal of Research in Science Teaching, 42 (10), 1063-1088.
    Briggs, M., & G. Bodner. (2007). A model of molecular visualization. In J. K. Gilbert (Ed.), Visualization in science education. (2nd ed.). Dordrecht: Springer.
    Buckley, B. C., & Boulter, C. J. (2000). Investigating the role of representations and expressed models in building mental models. In J. K. Gilbert & C. J. Boulter (Eds.), Developing models in science education (pp. 119-135): Netherlands: Kluwer.
    Chandrasegaran, AL, Treagust, D. F. & Mocerino, M. (2008). An evaluation of a teaching intervention to promote students’ ability to use multiple levels of representation when describing and explaining chemical reactions. Research in Science Education, 38(2), 237-248.
    Chi, M., Feltovich, P., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5(2), 121-152.
    Cokelez, A., Dumon, A., & Taber, K. S. (2008). Upper secondary French students, chemical transformations and the “Register of models”. International Journal of Science Education, 30(6), 807-836.
    Copolo, C. F., & Hounshell, P. B. (1995). Using three-dimensional models to teach molecular structures in high school chemistry. Journal of Science Education and Technology, 4, 295–305.
    diSessa, A. A., Hammer, D., Sherin, B., & Kolpakowski, T. (1991). Inventing graphing: Meta-representational expertise in children. Journal of Mathematical Behavior, 10, 117-160.
    Dunbar, K. (1995). How scientists really reason: Scientific reasoning in real-world laboratories. In R. J. Sterberg & J. E. Davidson (Eds.) The Nature of Insight. Cambridge, MA: MIT Press.
    Ekstrom, R. B., French, J. W., Harman, H. H., & Dermen, D. (1976). Kit of factor-referenced cognitive tests. NJ: Princeton.
    Ferk, V., Vrtacnik, M., Blejec A. & Gril A. (2003). Students’ understanding of molecular structure representations. International Journal of Science Education, 25(10), 1227–1245.
    Gabel, D.L. (1999). Improving teaching and learning through chemistry education research: A look to the future. Journal of Chemical Education, 76, 548-554.
    Gentner, D. & Stevens, A.(1983). Mental Models. Hillsdale, NJ: Erlbaum.
    Gilbert, J., & Priest, M. (1997). Models and discourse: A primary school science class visit to a museum. Science Education, 81, 749-762.
    Gilbert, J. K. (2007). Visualization: A metacognitive skill in science and science education. In Gilbert, J. K. (2nd ed.), Visualization in science education. Dordrecht: Springer.
    Gilbert, J. K. (2008). Visualization: An emergent field of practice and enquiry in science education. In Gilbert, J. K., Reiner, M., Nakhleh, M. (eds.), Visualization: Theory and practice in science education. Dordrecht: Springer.
    Gobert, J. D. (2005). Learning technology and cognitive theory on visualization to promote students’ science learning and literacy. In J. Gilbert (Ed.), Visualization in science education (pp. 121-146). London: Kluwer.
    Goldsmith, E. (1984). Research into illustration: An approach and a review. Cambridge: Cambridge University Press.
    Goodwin, C. (1995). Seeing in Depth. Social Studies of Science. 25(2) 237-274.
    Greeno, J. (1998). The situativity of knowing, learning, and research. American Psychologist, 53(1), 5-26.
    Griffiths, A. K., & Preston, K. R. (1992). Grade 12 students' misconceptions relating to fundamental characteristics of atoms and molecules. Journal of Research in Science Teaching , 29, 611-628.
    Johnson-Laird, P. N. (1983). Mental models: Towards a cognitive science of language, inference, and consciousness. Cambridge, MA: Harvard University Press.
    Johnson-Laird, P. N. (2001), ‘Mental models and deduction’, Trends in Cognitive Sciences, 5(10), 434–442.
    Johnstone, A.H. (1993). The development of chemistry teaching. Journal of Chemical Education , 70 (9), 701-705.
    Kindfield, A. C. H. (1993/1994) Biology diagrams: Tools to think with. The Journal of the Learning Sciences, 3 , 1-36.
    Kozma, R. (1991). Learning with media. Review of Educational Research, 61(2), 179-212.
    Kozma, R. (2000). Students collaborating with computer models and physical experiments. In J. Roschelle & C. Hoadley (Eds.), Proceedings of the Conference on Computer-Supported Collaborative Learning 1999. Mahwah, NJ: Erlbaum.
    Kozma, R.B., Chin, E., Russell, J., & Marx, N. (2000). The roles of representations and tools in the chemistry laboratory and their implications for chemistry instruction. Journal of the Learning Sciences , 9(2), 105-143.
    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. Gilbert (Ed.), Visualization in science education. London: Kluwer. p. 121-146.
    Kozma, R., & Russell, J. (2007). Students becoming chemists: Developing representational competence. In Gilbert, J. K. (2nd ed.), Visualization in science education. Dordrecht: Springer.
    Krajcik, J.S. (1991). Developing students' understanding of chemical concepts. In S.M. Glynn, R.H. Yeany, & B.K. Britton (Eds.), The psychology of learning science: International perspective on the psychological foundations of technology-based learning environments (pp. 117-145). Hillsdale, NJ: Erlbaum.
    Larkin, J. H., & Simon, H. A. (1987). Why a diagram is (sometimes) worth ten thousand words. Cognitive Science, 11, 65-99.
    Lave, J., & Wenger, E. (1991). Situated learning: legitimate peripheral participation. Cambridge: Cambridge University Press.
    Lemke, J.L. (1990). Talking Science: Language, learning, and values. Norwood, NJ: Ablex.
    Lowe, R.K. (2003). Animation and learning: Selective processing of information in dynamic graphics. Learning and Instruction, 13, 157-176.
    Mayer, R.E., (1997). Multimedia learning: Are we asking the right questions?. Educational Psychologist, 32 (1), 1–19.
    Michalchik, V., Rosenquist, A., Kozma, R., Kreikemeier, P., Schank, P., & Coppola, B. (2008). Representational resources for constructing shared understandings in the high school chemistry classroom. In J. Gilbert, M. Nakhleh, & M. Reiner (eds.). Visualization: Theory and practice in science education, (pp. 233-282). New York: Springer.
    Nicholson, J. R., & Seddon, G. M. (1977). The understanding of pictorial spatial relationships by Nigerian secondary school students. Journal of Cross-Cultural Psychology, 8, 381–400.
    Novick, S. & Nussbaum J. (1978). Junior high school pupils’ understanding of the particulate nature of matter: an interview study. Science Education, 62(3), 273-281.
    Novick, S. & Nassumsbaum, J.(1981). Pupils’ Understanding of the Particles Nature of Matter: A Cross-Age Study. Science Education, 65(2), 187-196.
    Paivio, A. (1986). Mental representations: A dual-coding approach. New York: Oxford University Press.
    Paivio, A. (1990). Mental representations: a dual coding approach (2nd ed.). NY: Oxford University Press.
    Peterson, R.F., Treagust, D.F., & Garnett, P. (1986). Indetification of secondary students' misconception of covalant bonding and and structure concepts using a diagnostic test instrument. Research in Science Education, 16(1), 40-48.
    Piaget, J. (1972). Intellectual evolution from adolescence to adulthood. Human Development, 15(1), 1-12.
    Rapp, D. (2007). Mental models: Theoretical issues for visualizations in science education. In Gilbert, J. K. (Ed.), Visualization in science education. Dordrecht: Springer.
    Reiber, L. (1988). The effects of computer animated elaboration strategies and practice on factual and application learning in an elementary science lesson. Journal of Educational Computing Research, 5, 431–444.
    Reiber, L. (1990). Using computer animated graphics in science instruction with children. Journal of Educational Psychology, 82(1), 135–140.
    Reiber, L. (1991). Animation, incidental learning and continuing motivation. Journal of Educational Psychology, 83(3), 318–328.
    Roth, W.-M. (2001). Learning science through technological design. Journal of Research in Science Teaching, 38, 768-790.
    Rouse, W.B. & Morris, (1986). On looking into the black box: Prospects and limits in the search for mental models. Psychological Bulletin, 100, 349–363.
    Seddon, G. M., and Moore, R. G. (1986). An unexpected effect in the use of models for teaching the visualization of rotation in molecular structures. European Journal of Science Education, 8, 79–86.
    Seddon, G. M., Tariq, R. H., & Dos Santos Viega, A. (1982). The visualisation of spatial transformations in diagrams of molecular structures. European Journal of Science Education, 4, 409–420.
    Teichert, M. A., Tien, L. T., Anthony, S. & Rickey, D. (2008). Effects of context on students’ molecular-level ideas. International Journal of Science Education, 30(8), 1095-1114.
    Tuckey, H. & Selvaratnam, M. (1993). Studies involving three-dimensional visualisation skills in chemistry: a review. Studies in Science Education, 21, 99–121.
    Tufte, E. R. (1983). The Visual Display of Quantitative Information. Cheshire: Connecticut: Graphics Press.
    Vygotsky, L.S. (1980). Mind in Society: The development of higher psychological processes. Cambridge, MA: Harvard University Press.
    Vygotsky. (1986). Thought and language, MIT Press, Boston.
    Williamson, V. M., & Abraham, M. R. (1995). The Effects of Computer Animation on the Particulate Mental Models of College Chemistry Students. Journal of Research in Science Teaching, 32(5), 521-534.
    Woolgar, S. (1990). Time and documents in researcher interaction: Some ways of making out what is happening in experimental science. In M Lynch and S. Woolgar (Eds.), Representation in scientific practice (pp. 123-152). Cambridge, MA: MIT Press.
    Wu, H.-K., Krajcik, J. S., & Soloway, E. (2001). Promoting conceptual understanding of chemical representations: students’ use of a visualization tool in the classroom, Journal of Research in Science Teaching, 38, 821-842.
    Wu, H.-K., & Shah, P. (2004). Exploring visuospatial thinking in chemistry learning, Science Education, 88, 465-492.
    Yang, E., Andre, T. & Greenbowe, T. J., (2003). Spatial ability and the impact of visualization/animation on learning electrochemistry, International Journal of Science Education, 25(3), 329–349.

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