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研究生: 甘曉晴
KAM, HIO-CHENG
論文名稱: 科學動畫的多模態分析及其對大學生概念理解之研究
A Multimodal Analysis of Science Animation and Its Effect on Undergraduates Comprehension
指導教授: 楊文金
Yang, Wen-Jin
口試委員: 蓋允萍
Ge, Yun-Ping
陳世文
Chen, Shih-Wen
楊文金
Yang, Wen-Jin
口試日期: 2022/07/15
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: 2022
畢業學年度: 110
語文別: 中文
論文頁數: 136
中文關鍵詞: 科學影片影片分析多模態分析免疫系統系統功能語言學
英文關鍵詞: science animation, video analysis, multimodal analysis, immune system, Systemic Functional Linguistics
研究方法: 文件分析法深度訪談法
DOI URL: http://doi.org/10.6345/NTNU202201294
論文種類: 學術論文
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  • 隨著二十一世紀科技與網路平台的快速發展,影像成為了網路平台上普遍的敘事媒介,影片也成為現今知識的傳遞方式之一。特別是在解釋複雜的科學知識時,概念與知識會藉以影片的方式,將科學概念中的抽象知識內容轉化成具像化的動態影像,讓大眾也可藉此以簡單、生動的影片來了解複雜、深奧的科學知識,更多複雜科學知識都能因影像化得而更有效地傳達。
    而在網路的科學類影片中,科學動畫更是一種平易近人的科學知識呈現方式。動畫中的動態表徵不僅可以提供豐富的資訊,有助促進概念的理解整合,更可透過當中的角色、物件的動作和出現,與多種模態的不同呈現方法,結合來體現出動畫當中的意義建構。但龐大的訊息量對於觀看者來説需要投入大量的精力來解讀和提取動畫欲表達之意義內涵,進一步而言需剖析當中表達概念時使用之元素與元素變量,才能了解科學動畫中的科學概念如何被理解。
    本研究以分析一部由免疫系統為主題的科學動畫,利用系統功能語言學(SFL)之架構,分析影片元素本身、元素間的關係、元素動作間的意義和場景間的關聯性,從影片的本身探討元素結合與意義體現在概念、人際與語篇三大元功能中等特徵,探討科學動畫之論述方式和特性以及其對科學動畫的意義潛勢。並透過動畫影片中多種模態間的關係,進一步分析多模態在科學動畫中的成效與效果與必要性。最後,借由大學生的觀後訪談,探討大學生對於科學動畫之概念接收、理解以及解讀情況,從而研究科學動畫元素與多模態呈現之效果、運用特色以及體現特徵。

    In the advance of technology, information visualization becomes one of the most important way of conveying knowledge and information. By turning ideas into visuals, complex ideas can be accessible and understandable with animated resource, especially in communicate, and explain scientific concepts. Audiences therefore can appreciate complex scientific topics in an easier way.
    Animation, has been acknowledged for its advantage of visualizing complex scientific knowledge, and is often involved in field of popular science. In terms of effectiveness, a deeper understanding of the nature of animation is crucial to research on efficacy of animation.
    Influenced by Systemic Functional Linguistics (SFL), this research used a stratified framework to analysis the nature of animation, components of animation, metafunctional model of animation. Then, further multimodal analysis is introduced to interpret the relationship and necessities of multimodal circumstances in animation. Finally, an interview is presented for understanding the effectiveness and efficacy of animation.

    第一章 緒論 1 第一節 研究動機 1 第二節 研究目的與問題 4 一、 研究目的 4 二、 研究問題 5 第三節 研究範圍與限制 5 第四節 名詞解釋 6 一、 科學動畫 6 二、 多模態 6 三、 系統功能語言學 6 第二章 文獻探討 7 第一節 科學動畫與其相關研究 7 一、 動畫之應用及相關研究 7 二、 科學動畫之應用 9 三、 科學動畫與靜態圖像 12 第二節 系統功能語言學與科學動畫 15 一、 系統功能語言學 15 二、 社會符號學與系統功能語言學 16 三、 科學動畫之分析架構 17 第三節 動畫多模態分析與理解相關研究 28 一、 多模態與社會符號學 28 二、 多模態話語之分析 29 三、 多模態動畫與理解 32 第三章 研究方法 34 第一節 研究方法 34 一、 研究設計 34 二、 研究對象 36 第二節 研究工具(I)- Ted-Ed 免疫系統科學動畫 37 第三節 研究工具(Ⅱ)- 半結構式晤談內容 38 第四節 資料收集 39 第五節 資料分析 39 一、 以系統功能語言分析科學動畫 39 二、 深度訪談分析 40 第四章 研究結果與討論 41 第一節 動畫中元素與元功能之運用分析 41 第二節 動畫中多模態間關係之分析 47 第三節 動畫字幕模態與動畫模態之分析 51 一、 字幕文本之分析 51 二、 動畫呈現之分析 55 三、 動畫文本整合對比之分析 66 第四節 訪談結果分析 72 一、 動畫概念內容詮釋情況 73 二、 動畫中元功能與模態之運用情況 79 三、 同學觀看動畫後之回饋情況 83 第五節 綜合分析結果 86 第五章 結論與建議 90 第一節 結論與意義 90 一、 研究問題與結果之分析 90 第二節 建議與討論 97 一、 科學動畫多模態分析之啟示 97 二、 科學動畫中元素與多模態對理解之分析啟示 99 三、 後續研究之建議 102 參考文獻 104 中文文獻 104 英文文獻 105 研究附錄 113 附錄一 動畫影片的時間線 113 附錄二 元素分析 122 附錄三 動畫文本中的主述位與及物性 133

    中文文獻
    朱永生(2007)。多模態話語分析的理論基礎與研究方法。外語學刊,05,82-86。
    李戰子(2003)。多模式話語的社會符號學分析。外語研究。5,1-8。
    李戰子、陸丹云(2012)。多模態符號學: 理論基礎, 研究途徑與發展前景。外語研究。2(1),8。
    柯舜智(2020)。網路科學影片的敘事分析: YouTube 原生科學頻道為例。
    曾永祥、許瑛玿(2006)。線上課程對高二學生四季成因概念學習的影響。科學教育學刊,14(3),257-282。
    陳瀅蓮、林詩惟(2015)。科普影視節目收視質之質化評量—閱聽眾觀點。博物館學季刊,29(1),79-95。
    胡壯麟(2007)。社會符號學研究中的多模態化。語言教學與研究,01,1-10。
    張正杰、莊秀卿、羅綸新(2014)。多媒體呈現模式與認知風格對國小自然科學學習成效之影響。教育傳播與科技研究,108,31-48。
    張徳祿(2009)。多模態話語分析綜合理論框架探索。中國外語,01,24-30。
    黃仕奇、楊德清(2014)。數位動畫對小六學童數常識發展之研究。科學教育學,22(1),33-55。
    葉松霖、王年燦(2012)。生物科普動畫擬人化使用之研究探討。藝術論文集刊,19,161-182。
    顏玉俐(2006)。影片媒體在國小科技倫理的教學應用研究。高雄師範大學工業科技教育學系碩士論文。

    英文文獻
    Ainsworth, S. (2008). How do animations influence learning. Current perspectives on cognition, learning, and instruction: Recent innovations in educational technology that facilitate student learning, 37-67.
    Allgaier, J. (2016). Science on YouTube: What do people find when they are searching for Climate Science and Climate Manipulation. In 14th International Conference on Public Communication of Science and Technology (PCST) (pp. 26-28).
    Allgaier, J. (2020). Science and medicine on YouTube. Second international handbook of Internet research, 7-27.
    Barak, M., & Dori, Y. J. (2011). Science education in primary schools: Is an animation worth a thousand pictures?. Journal of Science Education and Technology, 20(5), 608-620.
    Bartram, L., Ware, C., & Calvert, T. (2003). Moticons:: detection, distraction and task. International Journal of Human-Computer Studies, 58(5), 515-545.
    Bello-Bravo, J., Seufferheld, F., Steele, L. D., Agunbiade, T. A., Guilot, D., & Pittendrigh, B. R. (2011). Scientific Animations Without Borders SM: An International Collaborative Approach for Building Scientific Educational Materials for Use on Cell Phones and the Internet in Developing Nations. International Journal of Science in Society, 2(4).
    Berlin, H. A. (2016). Communicating science: Lessons from film. Trends in immunology, 37(4), 256-260.
    Bétrancourt, M., Bisseret, A., & Faure, A. (2001). Effect of the sequential display of pictures on the user’s mental representation: an experimental study. J.-F. Rouet, J.-J. Levonen, A. Biardeau (Eds.), Multimedia learning: cognitive and instructional issues, 112-118.
    Bétrancourt, M. (2005). The animation and interactivity principles in multimedia learning. In R. E. Mayer (Ed.), Cambridge handbook of multimedia learning, (pp. 287–296). New York: Cambridge University Press.
    Berney, S., Betancourt, M. (2016), Does animation enhance learning? A meta-analysis. Computers & Education, 101, 150-167.
    Bezemer, J., & Kress, G. (2008). Writing in multimodal texts: A social semiotic account of designs for learning. Written communication, 25(2), 166-195.
    Bush, A. J., & Gresham, L. G. (1986). The communication effects of animation. Journal of Applied Communication Research, 14(1), 59–65.
    Carney, R. N., & Levin, J. R. (2002). Pictorial illustrations still improve students' learning from text. Educational psychology review, 14(1), 5-26.
    Castro-Alonso, J.C., Ayres, P., & Paas, F. (2016). Comparing apples and oranges? A critical look at research on learning from statics versus animations. Comput. Educ., 102, 234-243.
    Dahlstrom, M. F. (2014). Using narratives and storytelling to communicate science with nonexpert audiences. Proceedings of the National Academy of Sciences, 111, 13614-13620.
    De Koning, B. B., Tabbers, H. K., Rikers, R. M., & Paas, F. (2010). Attention guidance in learning from a complex animation: Seeing is understanding?. Learning and instruction, 20(2), 111-122.
    Djerf-Pierre, M., & Lindgren, M. (2021). Making sense of “superbugs” on YouTube: A storytelling approach. Public Understanding of Science, 30(5), 535–551.
    Ercan, O., Bilen, K., & Bulut, A. (2014). The effect of web-based instruction with educational animation content at sensory organs subject on students’ academic achievement and attitudes. Procedia-Social and Behavioral Sciences, 116, 2430-2436.
    Fischer, S., Lowe, R. K., & Schwan, S. (2008). Effects of presentation speed of a dynamic visualization on the understanding of a mechanical system. Applied Cognitive Psychology: The Official Journal of the Society for Applied Research in Memory and Cognition, 22(8), 1126-1141.
    Farinella, M. (2018). The potential of comics in science communication. Journal of science communication, 17(1), Y01.
    Freeman, D. (2019). 〈台灣人首選的影音平台是什麼〉? Ipsos in Taiwan, https://www.ipsos.com/sites/default/files/ct/publication/documents/2019-10/n108_bht_taiwanese_turn_to_youtube_for_online_video_content.pdf
    Graesser, A. C., Olde, B., & Klettke, B. (2002). How does the mind construct and represent stories. Narrative impact: Social and cognitive foundations, 229-262.
    Halliday, M.A.K & Hasan, R. (1989). Language, context and text: Aspects of language in a socialsemiotic perspective. London: Oxford University Press.
    Halliday, Michael Alexander Kirkwood. (1978). Language as social semiotic: The social interpretation of language and meaning. London: Arnold.
    He, Y. (2020). A functional perspective on the semiotic features of science animation. In Learning from Animations in Science Education (pp. 25-54). Springer, Cham.
    He, Y. (2021). Towards a stratified metafunctional model of animation. Semiotica, 2021(239), 1-35.
    He, Y., & van Leeuwen, T. (2020). Animation and the remediation of school physics–a social semiotic approach. Social Semiotics, 30(5), 665-684.
    Hodge, R. and Kress, G. (1988) Social Semiotics. Cambridge: Polity.
    Höffler, T. N., Prechtl, H., & Nerdel, C. (2010). The influence of visual cognitive style when learning from instructional animations and static pictures. Learning and Individual Differences, 20(5), 479-483.
    Jewitt, C. (Ed.). (2009). The Routledge handbook of multimodal analysis (Vol. 1). London: Routledge.
    Jewitt, C., Bezemer, J., & O'Halloran, K. (2016). Introducing multimodality. Routledge.
    Kent, M. L. (2015). Social media circa 2035: Directions in social media theory. Atlantic Journal of Communication, 23(1), 1-4.
    Kress, G. (2010). Multimodality: A social semiotic approach to contemporary communication. Routledge.
    Kress, G., & Van Leeuwen, T. (1996). Reading images: The grammar of visual design. Routledge.
    Khajavi, M. J. (2011). Decoding the real: A multimodal social semiotic analysis of reality in animated documentary. Animation Studies Journal, 6, 46-51.
    Leão, Gisela. (2012). A systemic functional approach to the analysis of animation in film opening titles. Sydney: University of Technology PhD diss.
    León, B. (2008). Science related information in European television: a study of prime-time news. Public Understanding of Science, 17(4), 443-460.
    Lemke, J. L. (1990). Talking science: Language, learning, and values. Ablex Publishing Corporation, 355 Chestnut Street, Norwood, NJ 07648.
    Lightner, N. J. (2001). Model Testing of Users’ Comprehension in Graphical Animation: The Effect of Speed and Focus Areas. International Journal of Human-Computer Interaction, 13(1), 53–73.
    Lowe, R. K. (1999). Extracting information from an animation during complex visual learning. European journal of psychology of education, 14(2), 225-244.
    Lowe, R. K. (2003). Animation and learning: Selective processing of information in dynamic graphics. Learning and instruction, 13(2), 157-176.
    Lowe, R. K., & Schnotz, W. (2014). Animation principles in multimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (p. 513–546). Cambridge University Press.
    Macedo-Rouet, M., Rouet, J. F., Epstein, I., & Fayard, P. (2003). Effects of online reading on popular science comprehension. Science Communication, 25(2), 99-128.
    Mayer, R. E. (2003). The promise of multimedia learning: using the same instructional design methods across different media. Learning and instruction, 13(2), 125-139.
    Mayer, R. E., & Gallini, J. K. (1990). When is an illustration worth ten thousand words?. Journal of educational psychology, 82(4), 715.
    Mayer, R. E., & Moreno, R. (2002). Animation as an aid to multimedia learning. Educational psychology review, 14(1), 87-99.
    Moldovan, A. M. (2021). Animation in Education. Theoretical and Practical Approaches to Non-Formal Education: Interdisciplinary Examinations into Various Instructional Models, 45.
    National Science Board, National Science Foundation. 2020. Science and Technology: Public Attitudes, Knowledge, and Interest. Science and Engineering Indicators 2020. NSB-2020-7. Alexandria, VA.
    O'Halloran, K. L. (Ed.). (2006). Multimodal discourse analysis: Systemic-functional perspectives. London: Continuum.
    Ploetzner, Rolf & Richard Lowe. (2012). A systematic characterisation of expository animations. Computers in Human Behavior, 28(3). 781–794.
    Reznik, G., Massarani, L., & Moreira, I. D. C. (2019). How does the image of the scientist appear in short animation films?. História, Ciências, Saúde-Manguinhos, 26, 753-777.
    Rieber, L. P., & Kini, A. S. (1991). Theoretical foundations of instructional applications of computer-generated animated visuals. J. COMP. BASED INSTR., 18(3), 83-88.
    Schummer, J., & Spector, T. I. (2007). Popular images versus self-images of science: visual representations of science in clipart cartoons and internet photographs. Science images and popular images of the sciences. New York: Routledge, 69-95.
    Stieff, M. (2017). Drawing for promoting learning and engagement with dynamic visualizations. Learning from dynamic visualization, 333-356.
    Tversky, B., Morrison, J. B., & Betrancourt, M. (2002). Animation: can it facilitate?. International journal of human-computer studies, 57(4), 247-262.
    Urquiza-Fuentes, J., & Velázquez-Iturbide, J.Á. (2013). Toward the effective use of educational program animations: The roles of student's engagement and topic complexity. Comput. Educ., 67, 178-192.
    Van Leeuwen, T. (2005). Introducing social semiotics. Psychology Press.
    Van Leeuwen, T. (2011). Rhythm and multimodal semiosis. Semiotic margins, 168-176.
    Végső, Á., & Steagall, M. (2021). The shifting role of animation in science dissemination. In Link Symposium Abstracts 2020 (Vol. 2, No. 1).
    Wagner, I., & Schnotz, W. (2017). Learning from static and dynamic visualizations: What kind of questions should we ask?. In Learning from Dynamic Visualization (pp. 69-91). Springer, Cham.
    Welbourne, D. J., & Grant, W. J. (2016). Science communication on YouTube: Factors that affect channel and video popularity. Public understanding of science, 25(6), 706-718.
    Xiao, L. (2013). Animation trends in education. International Journal of Information and Education Technology, 3(3), 286.

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