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研究生: 蔡政宏
Tsai, Cheng-Hung
論文名稱: 視覺化模擬輔助人工智慧教學之研究-以類神經網路為例
Learning Artificial Intelligence with Visualization and Simulation: The Case of Neural Networks
指導教授: 林育慈
Lin, Yu-Tzu
口試委員: 吳正己
Wu, Cheng-Chih
張凌倩
Chang, Ling-Chian
林育慈
Lin, Yu-Tzu
口試日期: 2022/08/05
學位類別: 碩士
Master
系所名稱: 資訊教育研究所
Graduate Institute of Information and Computer Education
論文出版年: 2022
畢業學年度: 110
語文別: 中文
論文頁數: 191
中文關鍵詞: 人工智慧電腦科學教育模擬式教學演算法視覺化
英文關鍵詞: Artificial Intelligence, Simulation, Visualization, Computer Science Education
DOI URL: http://doi.org/10.6345/NTNU202201807
論文種類: 學術論文
相關次數: 點閱:139下載:13
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  • 當今社會中,人工智慧影響我們的生活面向甚廣。目前國際上的教育相關政策也將人工智慧議題納入探討,並期望從小教導孩子人工智慧相關知能。本研究針對高中年段的學生,設計視覺化模擬輔助人工智慧教學策略,並發展學習平台,透過「概念理解」、「概念反思」、「概念應用」三個教學步驟引導學生進行概念學習,教學主題聚焦於類神經網路。本研究以實證研究探討視覺化模擬輔助人工智慧教學與傳統講述式教學對高中生之人工智慧學習成就、學習態度影響之差異,以及學生對於視覺化模擬輔助教學的感受。從教學實驗結果發現:

    一、視覺化模擬輔助教學對人工智慧學習成就之影響
    本研究發展之視覺化模擬輔助教學能透過模擬操作幫助學生建立概念:(1), 提供學生操作與調整參數、觀察實驗結果的機會,使之能於操作過程中檢驗概念;(2) 輔助進行運算過程,以降低認知負荷並聚焦重要學習概念;(3) 模擬工具的設計融入日常生活情境,以幫助學生以解實際應用。實驗組學生因而能建立較正確清晰的人工智慧概念,進而增進其概念理解上的學習成就。但由於本研究的教學中程式設計相關教學內容較少,因此與傳統教學相較,雖亦使演算法程式實作有更好的表現,但其差異未達顯著。

    二、視覺化模擬輔助教學對人工智慧學習態度之影響
    實驗結果發現,使用視覺化模擬輔助教學之學生,由於能透過模擬操作測試概念並即時得到概念學習的回饋,對於自身學習成果的信心顯著高於接受傳統教學之學生。但兩組學生在「學習動機」、「自我效能」、「資訊科學抽象概念/程序之學習感受」面向沒有顯著差異。

    三、學生對視覺化模擬輔助教學之感受
    基於本研究的量化資料與質性訪談資料分析結果,視覺化模擬輔助教學相較於傳統教學,能使學生有更高的學習成就,實驗組學生也普遍認為視覺化模擬輔助教學對他們學習人工智慧相關知識有所幫助,此助益對學習艱深複雜的概念更加明顯。此外,訪談結果亦顯示學生認為模擬平台能夠幫助他們學習較抽象、具複雜運算的課程概念。

    Artificial Intelligence (AI) is growing rapidly to fit the needs of our everyday life. To introduce students to the AI world, many advanced countries start to discuss how to develop and implement effective AI instruction for k-12 students. However, existing AI instruction focuses more on higher education. In addition, AI topics involve abstract and complex concepts and skills, which are difficult for k-12 students.
    This research aims to design and develop an AI instruction for high school students by employing visualization. Students learn with a simulation-based platform, and the proposed instruction consists three stages: concept comprehension, concept reflection, and concept application. This study conducts an empirical study to explore the effects of the proposed simulation-based instruction on learning achievement, attitude toward AI learning, and perceptions of simulation-based instruction. The research findings are as the following:
    1. The effectiveness of the simulation-based AI instruction on learning achievement:
    The proposed simulation-based AI instruction provides students with the opportunity of adjusting parameters and observing the changes, which helps clarify the concepts. The computing function of the simulation platform can also help reduce the cognitive load. In addition, the authentic simulation context connects abstract concepts to real-world applications, so that students can grasp the AI concepts more deeply.

    2. The effectiveness of the simulation-based AI instruction on learning toward AI learning:
    The experimental group with the simulation-based AI instruction has significantly higher confidence in their AI learning than the control group with traditional instruction. However, there is no significant difference between the two groups of students in terms of "learning motivation", "self-efficacy" and "perceptions of learning abstract concepts/skills". This might be because the experimental period is not long enough.

    3. Students' perceptions of the simulation-based AI instruction
    The experimental group with the simulation-based AI instruction has significantly higher learning achievement than the control group. Students in the experimental group also generally believe that the simulation-based AI instruction is beneficial for their AI learning. The simulation tools help students manipulate the abstract and complex AI concepts, and then understand more clearly.

    第壹章 緒論 1 第一節 研究背景與動機 1 第二節 研究目的 5 第三節 名詞釋義 6 第貳章 文獻探討 8 第一節 人工智慧 8 第二節 程式設計與演算法教學 12 第三節 模擬式教學 14 第四節 演算法視覺化 18 第參章 研究方法 20 第一節 研究設計與架構 20 第二節 研究實驗參與者 22 第三節 視覺化模擬輔助教學 23 第四節 研究程序 33 第五節 研究工具 40 第六節 資料蒐集與分析 45 第肆章 分析結果與討論 48 第一節 對人工智慧學習成就之影響 48 第二節 對學習態度之影響 54 第三節 學生對視覺化模擬輔助教學之感受 59 第四節 講述式教學之感受 64 第五節 討論 66 第伍章 結論與建議 86 第一節 結論 86 第二節 建議 91 參考文獻 93 附錄一 類神經網路概念學習單 101 附錄二 程式設計學習單 142 附錄三 隨堂測驗 148 附錄四 人工智慧概念前測 157 附錄五 人工智慧概念後測 165 附錄六 專題實作 171 附錄七 電腦科學態度問卷與視覺化模擬輔助教學策略感受調查 184

    Abu-Naser, S. S. (2008). Developing visualization tool for teaching AI searching algorithms.
    Alessi, S. M., & Trollip, S. R. (2001). Multimedia for learning: Methods and development. Allyn & Bacon.
    Barella, A., Valero, S., & Carrascosa, C. (2008). JGOMAS: New approach to AI teaching. IEEE Transactions on education, 52(2), 228-235.
    Bellstrom, P., Thoren, C. (2009). Learning how to program through visualization: A pilot study on the bubble sort algorithm. 2009 Second International Conference on the Applications of Digital Information and Web Technologies (Icadiwt 2009), 90–94. doi:10.1109/icadiwt.2009.5273943
    Burgsteiner, H., Kandlhofer, M., & Steinbauer, G. (2016, March). Irobot: Teaching the basics of artificial intelligence in high schools. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 30, No. 1).
    Cayvaz, A., Akcay, H., & Kapici, H. O. (2020). Comparison of simulation-based and textbook-based instructions on middle school students’ achievement, inquiry skills and attitudes. International Journal of Education in Mathematics, Science and Technology, 8(1), 34-43.
    Chen, Y. L., Hong, Y. R., Sung, Y. T., & Chang, K. E. (2011). Efficacy of simulation-based learning of electronics using visualization and manipulation. Journal of Educational Technology & Society, 14(2), 269-277.
    Colaso, V., Kamal, A., Saraiya, P., North, C., McCrickard, S., & Shaffer, C. (2002). Learning and retention in data structures: A comparison of visualization, text, and combined methods. Paper presented at the Proceedings of ED-MEDIA 2002, June 24-29, Denver, Colorado, USA.
    Cuéllar, M. P., & Pegalajar, M. C. (2014). Design and implementation of intelligent systems with LEGO Mindstorms for undergraduate computer engineers. Computer Applications in Engineering Education, 22(1), 153-166.
    De Jong, T., & Van Joolingen, W. R. (1998). Scientific discovery learning with computer simulations of conceptual domains. Review of educational research, 68(2), 179-201.
    Diehl, S. (2007). Software visualization: visualizing the structure, behaviour, and evolution of software. Springer Science & Business Media.
    Eccles, J. S., & Wigfield, A. (1995). In the mind of the actor: The structure of adolescents' achievement task values and expectancy-related beliefs. Personality and social psychology bulletin, 21(3), 215-225.
    Esteves, M., Fonseca, B., Morgado, L., & Martins, P. (2011). Improving teaching and learning of computer programming through the use of the Second Life virtual world. British Journal of Educational Technology, 42(4), 624-637.
    Estevez, J., Garate, G., Guede, J. M., & Grana, M. (2019). Using Scratch to Teach Undergraduate Students' Skills on Artificial Intelligence. arXiv preprint arXiv:1904.00296.
    Faryniarz, J. V., & Lockwood, L. G. (1992). Effectiveness of microcomputer simulations in stimulating environmental problem solving by community college students. Journal of Research in Science Teaching, 29(5), 453-470.
    Fernandes, M. A. (2016). Problem‐based learning applied to the artificial intelligence course. Computer Applications in Engineering Education, 24(3), 388-399.
    Garay, G. R., Tchernykh, A., Drozdov, A. Y., Garichev, S. N., Nesmachnow, S., & Torres-Martinez, M. (2017). Visualization of VHDL-based simulations as a pedagogical tool for supporting computer science education. Journal of Computational Science.
    Grivokostopoulou, F., Perikos, I., & Hatzilygeroudis, I. (2014, December). Using semantic web technologies in a web based system for personalized learning AI course. In 2014 IEEE Sixth International Conference on Technology for Education (pp. 257-260). IEEE.
    Gurka, J. S., & Citrin, W. (1996, September). Testing effectiveness of algorithm animation. In Proceedings 1996 IEEE Symposium on Visual Languages (pp. 182-189). IEEE.
    Hansen, S. R., Narayanan, N. H., & Schrimpsher, D. (2000). Helping learners visualize and comprehend algorithms. Interactive Multimedia Electronic Journal of Computer-Enhanced Learning, 2(1), 10.
    Homer, B. D., & Plass, J. L. (2014). Level of interactivity and executive functions as predictors of learning in computer-based chemistry simulations. Computers in Human Behavior, 36, 365-375.
    Hundhausen, C., & Douglas, S. (2000, September). Using visualizations to learn algorithms: should students construct their own, or view an expert's?. In Proceeding 2000 IEEE International Symposium on Visual Languages (pp. 21-28). IEEE.
    Hundhausen, C. D., Douglas, S. A., & Stasko, J. T. (2002). A meta-study of algorithm visualization effectiveness. Journal of Visual Languages & Computing, 13(3), 259-290.
    Huppert, J., Yaakobi, J., & Lazarowitz, R. (1998). Learning microbiology with computer simulations: Students’ academic achievement by method and gender. Research in Science & Technological Education, 16(2), 231-245.
    Jacob, S. R., & Warschauer, M. (2018). Computational thinking and literacy. Journal of Computer Science Integration, 1(1).
    Jarc, D. J., Feldman, M. B., & Heller, R. S. (2000). Assessing the benefits of interactive prediction using web-based algorithm animation courseware. ACM SIGCSE Bulletin, 32(1), 377-381.
    Jensen, D., Self, B., Rhymer, D., Wood, J., & Bowe, M. (2002). A rocky journey toward effective assessment of visualization modules for learning enhancement in Engineering Mechanics. Journal of Educational Technology & Society, 5(3), 150-162.
    Jonassen, D. H., & Strobel, J. (2006). Modeling for meaningful learning. In Engaged learning with emerging technologies (pp. 1-27). Springer, Dordrecht.
    Kandlhofer, M., Steinbauer, G., Hirschmugl-Gaisch, S., & Huber, P. (2016, October). Artificial intelligence and computer science in education: From kindergarten to university. In 2016 IEEE Frontiers in Education Conference (FIE) (pp. 1-9). IEEE.
    Kind, P., Jones, K., & Barmby, P. (2007). Developing attitudes towards science measures. International journal of science education, 29(7), 871-893.
    Kochlán, M., & Hodon, M. (2014, September). Open hardware modular educational robotic platform—Yrobot. In 2014 23rd International Conference on Robotics in Alpe-Adria-Danube Region (RAAD) (pp. 1-6). IEEE.
    Korhonen, A., & Malmi, L. (2000). Algorithm Simulation with Automatic Assessment. Paper presented at the 5th Annual ACM SIGCSE/SIGCUE Conference on Innovation and Technology in Computer Science Education (ITiCSE 2000). Helsinki, Finland.
    Krishnan, D. G., Keloth, A. V., & Ubedulla, S. (2017). Pros and cons of simulation in medical education: A review. Education, 5, 7.
    Kumar, A. N. (2004). Three years of using robots in an artificial intelligence course: lessons learned. Journal on Educational Resources in Computing (JERIC), 4(3), 2-es.
    Lawrence, A. W., Badre, A. M., & Stasko, J. T. (1994, October). Empirically evaluating the use of animations to teach algorithms. In Proceedings of 1994 IEEE Symposium on Visual Languages (pp. 48-54). IEEE.
    Marković, M., Kostić Kovačević, I., Nikolić, O., & Nikolić, B. (2015). INSOS—educational system for teaching intelligent systems. Computer Applications in Engineering Education, 23(2), 268-276.
    McNally, M., Naps, T., Furcy, D., Grissom, S., & Trefftz, C. (2007). Supporting the rapid development of pedagogically effective algorithm visualizations. Journal of Computing Sciences in Colleges, 23(1), 80-90.
    Mintz, R. (1993). Computerized simulation as an inquiry tool. Scool Science and Mathematics, 93(2), 76-80.
    Moyer-Packenham, P. S., Lommatsch, C. W., Litster, K., Ashby, J., Bullock, E. K., Roxburgh, A. L., ... & Clarke-Midura, J. (2019). How design features in digital math games support learning and mathematics connections. Computers in Human Behavior, 91, 316-332.
    Naps, T. L., Rößling, G., Almstrum, V., Dann, W., Fleischer, R., Hundhausen, C., et al. (2003). Exploring the role of visualization and engagement in computer science education. ACM SIGCSE Bulletin, 35(2), 131-152.
    O'Neil, H. F., Wainess, R., & Baker, E. L. (2005). Classification of learning outcomes: Evidence from the computer games literature. The Cirriculum Journal, 16(4), 455-474.
    Osborne, J., Simon, S., & Collins, S. (2003). Attitudes towards science: A review of the literature and its implications. International journal of science education, 25(9), 1049-1079.
    Pedro, F., Subosa, M., Rivas, A., & Valverde, P. (2019). Artificial intelligence in education: Challenges and opportunities for sustainable development.
    Prensky, M. (2002). The Motivation of Gameplay or, the REAL 21th century learning revolution. URL http://www. marcprensky. com/writing/Prensky, 2010-1.
    Rudder, A., Bernard, M., & Mohammed, S. (2007, March). Teaching programming using visualization. In Proceedings of the Sixth IASTED International Conference on Web-Based Education (pp. 487-492).
    Russell, S., & Norvig, P. (2002). Artificial intelligence: a modern approach.
    Saraiya, P., Shaffer, C. A., McCrickard, D. S., & North, C. (2004, March). Effective features of algorithm visualizations. In Proceedings of the 35th SIGCSE technical symposium on Computer Science Education (pp. 382-386).
    Seehorn, D., Carey, S., Fuschetto, B., Lee, I., Moix, D., O'Grady-Cunniff, D., ... & Verno, A. (2011). CSTA K--12 Computer Science Standards: Revised 2011. ACM.
    Selby, C., & Woollard, J. (2013). Computational thinking: the developing definition.
    Shaffer, C. A., Cooper, M. L., Alon, A. J. D., Akbar, M., Stewart, M., Ponce, S., & Edwards, S. H. (2010). Algorithm visualization: The state of the field. ACM Transactions on Computing Education (TOCE), 10(3), 1-22.
    Simoňák, S. (2016, January). Algorithm visualizations as a way of increasing the quality in computer science education. In 2016 IEEE 14th international symposium on applied machine intelligence and informatics (SAMI) (pp. 153-157). IEEE.
    Sklar, E., Eguchi, A., & Johnson, J. (2002, June). RoboCupJunior: learning with educational robotics. In Robot Soccer World Cup (pp. 238-253). Springer, Berlin, Heidelberg.
    Stone, P., Brooks, R., Brynjolfsson, E., Calo, R., Etzioni, O., Hager, G., ... & Teller, A. (2016). Artificial intelligence and life in 2030: the one hundred year study on artificial intelligence.
    Thomas, R., & Neilson, I. (1995). Harnessing simulations in the service of education: The interact simulation environment. Computers & Education, 25(1-2), 21-29.
    Tudoreanu, M. E., Wu, R., Hamilton-Taylor, A., & Kraemer, E. (2002, September). Empirical evidence that algorithm animation promotes understanding of distributed algorithms. In Proceedings IEEE 2002 Symposia on Human Centric Computing Languages and Environments (pp. 236-243). IEEE.
    Tversky, B., Morrison, J. B., & Betrancourt, M. (2002). Animation: can it facilitate?. International journal of human-computer studies, 57(4), 247-262.
    Végh, L., & Stoffová, V. (2017). Algorithm animations for teaching and learning the main ideas of basic sortings. Informatics in Education, 16(1), 121-140.
    Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35.
    Yoon, D. M., & Kim, K. J. (2015). Challenges and opportunities in game artificial intelligence education using Angry Birds. IEEE Access, 3, 793-804.
    教育部(2018)。十二年國民基本教育課程綱要 國民中學暨普通型高級中等學校 科技領域。教育部。
    黃仁暐、涂益郎(2019)。和AI做朋友-相知篇。教育部。

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