研究生: |
胡琇惠 Hu, Siou-Huei |
---|---|
論文名稱: |
探討建模教學對於密度及水溶液概念的學習成效 The Effectiveness of Modeling Instruction for Students in Learning Density and Aqueous Solution |
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: | 2019 |
畢業學年度: | 107 |
語文別: | 中文 |
論文頁數: | 136 |
中文關鍵詞: | 密度 、水溶液 、建模能力 、認知負荷 |
英文關鍵詞: | density, aqueous solution, modeling ability, cognitive load |
DOI URL: | http://doi.org/10.6345/THE.NTNU.GSE.001.2019.F02 |
論文種類: | 學術論文 |
相關次數: | 點閱:209 下載:0 |
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科學家利用模型思考,解決問題。在科學課程中,教師利用模型,幫助孩童認識世界。學生透過模型了解理論,教師可運用不同的表徵與模型,幫助學生探索與認識自然。本研究依課程內容可分為密度及水溶液兩單元,課程設計以邱美虹(2016)提出的建模歷程為架構,其過程包含八個步驟:分別為模型選擇、模型建立、模型效化、模型分析、模型應用、模型調度、模型修正、模型重建。
本研究主要探討在進行建模教學後,對於學生在密度與水溶液的學習成效上是否有幫助。本研究分為兩個研究主題:研究一:探討建模教學對於「密度概念」;研究二:探討建模教學對於「水溶液概念」學習成效。研究對象為台北市某國中八年級四班學生進行研究,將四班隨機分成建模教學組與一般教學組,建模教學組學生有55位,一般教學組學生有54位,共109位。研究工具分為量化與質性工具,量化工具包含密度及水溶液概念之前測、後測與延宕測驗、認知負荷問卷,質性工具包含學生晤談資料。概念測驗工具由兩位具有化學背景的大學教授,與一位資深國中教師建立專家效度,且密度與水溶液試卷信度為0.81及0.79。針對學生測驗結果,將以SPSS進行t-test及共變數分析(ANCOVA)
整體研究結果顯示:(1)建模教學可以幫助學生科學概念的學習與問題解決,其中在密度概念(F=25.1,p<0.001)及水溶液概念(F=13.83,p<0.001)中均達顯著差異。(2)建模教學最能幫助中能力學生學習科學概念,而在較複雜的科學概念上,對低能力學生幫助較低,但對於高能力學生則幫助較大。(3)建模教學運用模型思考,幫助學生學習與解決問題。但由於過程中學生要學習科學概念與建模歷程框架,相較於一般傳統教學,建模教學更容易造成個體的認知負荷,其中在密度概念整體認知負荷(t=2.66,p=0.009,df=89)及水溶液概念整體認知負荷(t=2.58,p=0.01,df=84)中均達顯著差異。
Scientists solve problems through model thinking. In science education, teachers use models to help students learn about the world, and students understand a theory through models. Teachers use various characteristics and models to assist students in exploring and learning the nature. The instructions in this study covered two units, including density and aqueous solution, and were designed based on the modeling process proposed by Chiu, Mei-Hung (2016). This process includes eight steps: model selection, model building, model validation, model analysis, model application, model deployment, model adjustment, and model rebuilding.
The objective of this study was to investigate whether modeling instruction is helpful for students in learning the density and aqueous solution units. The focus of this study was two-fold. One was to examine the effectiveness of modeling instruction for students in learning “density”, and the other was to evaluate its effectiveness in learning “aqueous solution”. The participants were students of four eight-grade classes of a junior high school in Taipei City. They were randomly divided into two groups, including the modeling instruction group and the traditional instruction group. There were 109 participants in total, 55 in the modeling instruction group and 54 in the traditional instruction group. Both quantitative and qualitative instruments were employed. The quantitative instruments included pretest, posttest, and retention test for understanding of the density and aqueous solution concepts and a cognitive load questionnaire. The qualitative instruments included the students’ interview data. The expert validity of the tests for understanding of concepts was established by two college professors with a chemistry background and a senior junior high school teacher. In terms of reliability, the tests for the density and aqueous solution units were respectively measured at 0.81 and 0.79. The students’ test results were analyzed using t-test and ANCOVA on SPSS.
The results were as follows: (1) Modeling instruction helped students in learning scientific concepts and solving scientific problems. The participants showed significant improvements in learning the concepts of density (F=25.1,p<0.001) and the concepts of aqueous solution (F=13.83,p<0.001); (2) Modeling instruction was most helpful for students of intermediate competence in learning scientific concepts. When applied to more complicated scientific concepts, it was less helpful for students of low competence but more helpful for students of high competence; (3) Modeling instruction uses model thinking as an approach to assist students with learning and problem-solving. In this study, as students needed to learn scientific concepts and the modeling framework in this process, modeling instruction would more easily cause cognitive load on individuals compared to the traditional instruction. The difference in overall cognitive load between the two instruction groups was significant across both the density unit (t=2.66, p=0.009,df=89) and the aqueous solution unit (t=2.58,p=0.01,df=84).
中文文獻
左台益、蔡志仁(2001)。高中生建構橢圓多重表徵之認知特性。科學教育學刊,9(3),281-297。
吳明珠(2008)。科學模型本質剖析。科學教育月刊。
吳金聰、梁淑坤(2008)。遠哲西子灣數學教師成長工作坊成果分享:認知負荷理論在數學教學上的應用。數學教育。
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。
官翰德、陳龍川(2000)。融入電腦模擬的概念改變教學策略對兒童密度相關概念學習成就之影響。花蓮師院學報(10),299-323。
邱美虹、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。
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,11。
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。
許文清、吳慧敏、譚寧君、楊凱翔(2013)。工作範例之教學順序對學生學習成效與認知負荷影響之研究--以面積覆蓋活動為例。科學教育月刊。
許良榮、劉政華(2004)。中小學生之溶解概念的形成與發展。科學教育學刊,12(3),265-287。
黃萬居、張萬居、葉欣儒(2005)。以認知架構為基礎的教學模式進行國小學童水溶液概念改變之研究。科學教育研究與發展季刊。
簡美容(2001)。國小學童對溶解相關概念認知之研究。國立臺北教育大學數理教育研究所學位論文,1-148。
蘇國章(2011)。應用認知負荷理論於資訊融入教學多媒體設計之分析。生活科技教育月刊。
顧炳宏、陳瓊森、溫媺純(2011)。從學生的表現與觀點探討引導發現式教學作為發展探究教學之折衷方案角色的成效-以密度概念為例。科學教育學刊,19(3),257-282。
顧炳宏、楊和學、陳瓊森(2012)。結合學習環教學模式的密度概念探究教學活動設計。科學教育月刊。
英文文獻
Ainsworth, S. (2008). The educational value of multiple-representations when learning complex scientific concepts Visualization: Theory and practice in science education (pp. 191-208): Springer.
Chandler, P., & Sweller, J. (1996). Cognitive load while learning to use a computer program. Applied cognitive psychology, 10(2), 151-170.
Clement, J. (1989). Learning via model construction and criticism. In Handbook of creativity (pp. 341-381). Springer, Boston, MA.
Garnett, P. J., Garnett, P. J., & Hackling, M. W. (1995). Students' alternative conceptions in chemistry: A review of research and implications for teaching and learning.
Gilbert, J. K. (2004). Models and modelling: Routes to more authentic science education. International Journal of Science and Mathematics Education, 2(2), 115-130
Greca, I. M., & Moreira, M. A. (2002). Mental, physical, and mathematical models in the teaching and learning of physics. Science Education, 86(1), 106-121.
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.
Hawkes, S. J. (2004). The concept of density. Journal of Chemical Education, 81(1), 14.
Kaput, J. J. (1992). Linking representations in the symbol systems of algebra. Handbook of research on mathematics teaching and learning.
Leppink, J., Paas, F., Van der Vleuten, C. P., Van Gog, T., & Van Merriënboer, J. J. (2013). Development of an instrument for measuring different types of cognitive load. Behavior research methods, 45(4), 1058-1072.
Lesh, R., Post, T. R., & Behr, M. (1987). Representations and translations among representations in mathematics learning and problem solving Problems of representations in the teaching and learning of mathematics: Lawrence Erlbaum.
Namdar, B., & Shen, J. (2015). Modeling-oriented assessment in K-12 science education: A synthesis of research from 1980 to 2013 and new directions. International Journal of Science Education, 37(7), 993-1023.
Nersessian, N. J. (2009). How do engineering scientists think? Model‐based simulation in biomedical engineering research laboratories. Topics in Cognitive Science, 1(4), 730-757.
National Research Council. (2012). A framework for K-12 science education: Practices, crosscutting concepts, and core ideas. National Academies Press.
Penner, D. E., Lehrer, R., & Schauble, L. (1998). From physical models to biomechanics: A design-based modeling approach. Journal of the Learning Sciences, 7(3-4), 429-449.
Schwarz, B., Nathan, M., & Resnick, L. (1996). Acquisition of meaning for arithmetic structures with the Planner. International perspectives on the design of technology-supported learning environments, 105-129.
Schwarz, C. V., Reiser, B. J., Davis, E. A., Kenyon, L., Achér, A., Fortus, D., . . . Krajcik, J. (2009). Developing a learning progression for scientific modeling: Making scientific modeling accessible and meaningful for learners. Journal of Research in Science Teaching, 46(6), 632-654.
Shen, J. (2006). Teaching strategies and conceptual change in a professional development program for science teachers of K--8.
Stratford, S. J., Krajcik, J., & Soloway, E. (1998). Secondary students' dynamic modeling processes: Analyzing, reasoning about, synthesizing, and testing models of stream ecosystems. Journal of Science Education and Technology, 7(3), 215-234.
Sweller, J. (2010). Element interactivity and intrinsic, extraneous, and germane cognitive load. Educational psychology review, 22(2), 123-138.
Sweller, J., Van Merrienboer, J. J., & Paas, F. G. (1998). Cognitive architecture and instructional design. Educational psychology review, 10(3), 251-296.
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.
Van Driel, J. H., & Verloop, N. (1999). Teachers' knowledge of models and modelling in science. International Journal of Science Education, 21(11), 1141-1153.
Van Merrienboer, J. J., & Sweller, J. (2005). Cognitive load theory and complex learning: Recent developments and future directions. Educational psychology review, 17(2), 147-177.
Wu, H.-K., & Puntambekar, S. (2012). Pedagogical affordances of multiple external representations in scientific processes. Journal of Science Education and Technology, 21(6), 754-767.