簡易檢索 / 詳目顯示

研究生: 陳沐生
Chen, Mu-Sheng
論文名稱: 探討同儕互評與教育桌遊在人工智慧單元中的學習成效-以語音辨識為例
Learning Effectiveness of Integrating Peer Assessment and board game in Artificial Intelligence Unit: Taking Speech Recognition as Examples
指導教授: 許庭嘉
Hsu, Ting-Chia
口試委員: 黃國禎
Hwang, Gwo-Jen
蔣旭政
Chiang, Hsu-Cheng
許庭嘉
Hsu, Ting-Chia
口試日期: 2022/06/01
學位類別: 碩士
Master
系所名稱: 科技應用與人力資源發展學系
Department of Technology Application and Human Resource Development
論文出版年: 2022
畢業學年度: 110
語文別: 中文
論文頁數: 109
中文關鍵詞: 運算思維教育人工智慧教育語音辨識同儕互評教育桌遊
英文關鍵詞: computational thinking education, artificial intelligence education, audio recognition, peer assessment, educational board game
研究方法: 準實驗設計法
DOI URL: http://doi.org/10.6345/NTNU202200758
論文種類: 學術論文
相關次數: 點閱:242下載:0
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  • 本研究結合教育科技設計了一個人工智慧語音辨識課程,適用於無運算思維與人工智慧相關基礎之大一學生,目的在培養其運算思維與人工智慧的基礎應用,本研究使用準實驗研究法,探討同儕互評與教育桌遊在人工智慧語音辨識課程的影響,進行了二因子(2x2)組別教學實驗。學習成效結果顯示,單因子主要效果之教育桌遊或同儕互評之學習成效會比控制組的好,另一方面只有一個實驗因子的組別比兩個實驗因子的組別更適合先備知識較低的學生學習,推測是由於初學者對於學習內容還不熟悉,如果增加更多任務可能會超過學生認知負荷。在運算思維自我效能上,透過桌遊引導可以提升合作學習與批判性思考和創造性思維,因為在桌遊中學生會進行討論、合作以及策略創新。在學習焦慮與學習投入度的部分,由於本研究所使用之桌遊有競爭性因素,因此導致進行桌遊的組別學習焦慮都有增加,在愉悅性面向上,有進行同儕互評的組別可能因為需要花更多額外的時間進行而導致愉悅性較低。

    This research integrated educational technology to design an artificial intelligence speech recognition course, which is suitable for first-year students without the foundation of computational thinking and artificial intelligence. The purpose is to cultivate their basic application of computational thinking and artificial intelligence. To evaluate the impact of educational board games in artificial intelligence speech recognition courses, two factors composed with four (2x2) groups were tested. The results of learning effectiveness show that the main effect of only one experimental factor on learning effectiveness of experimental groups is better than the control group. On the other hand, the group with one experimental factor is more suitable for students with lower prior knowledge than the group with two experimental factors. It was inferred that the novices are not familiar with the learning content. If too many tasks are added, it may cause overloading to the students. In terms of self-efficacy of computational thinking, cooperative learning and critical thinking and creative thinking can be enhanced through board game. Because students could discuss, cooperate and innovate strategies during board game. In the part of learning anxiety and learning engagement, due to the competitive factor of the board games used in this study, the learning anxiety of the group who played board game increased. In terms of enjoyment, the groups who conducted peer assessment felt less enjoyment because it took more extra time to do it.

    第一章 緒論 1 第一節 研究背景與動機 1 第二節 研究目的與待答問題 4 第三節 名詞解釋 5 第二章 文獻探討 7 第一節 運算思維 7 第二節 遊戲式學習 10 第三節 合作學習 13 第三章 研究設計與實施 17 第一節 研究架構與設計 17 第二節 研究對象 18 第三節 研究工具 19 第四節 研究步驟與實驗流程 21 第五節 課程與教材設計 26 第六節 資料分析 52 第四章 實驗結果 55 第一節 學習成就 55 第二節 運算思維自我效能 68 第三節 學習焦慮與學習投入度 78 第五章 結論與建議 87 第一節 研究結果與討論 87 第二節 研究限制與未來研究建議 91 參考文獻 93 一、 中文部分 93 二、 英文部分 93 附錄 101 附錄一 運算思維與人工智慧學習成就測驗卷(前測) 102 附錄二 運算思維與人工智慧學習成就測驗卷(後測) 104 附錄三 運算思維自我效能量表 106 附錄四 學習焦慮與學習投入度量表 107 附錄五 桌遊分數紀錄單 108 附錄六 個人測試紀錄單 109

    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:2018年9月20日 取至 https://www.k12ea.gov.tw/files/class_schema/課綱/13-科技/13-1/十二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.pdf

    資訊及科技教育司(2019)。AI教育X教育AI-人工智慧教育及數位先進個人化、適性化學習時代來臨!:2019年6月27日 取至https://ccds2019.ndhu.edu.tw/ezfiles/204/1204/img/3852/20190627_keynote_speech_B.pdf

    Angelo, T. A. (1993). Classroom assessment. Metropolitan Universities, 4(1), 5-15.

    Bandura, A. (1977). Self-efficacy: toward a unifying theory of behavioral change. Psychological review, 84(2), 191-215. https://doi.org/10.1037/0033-295X.84.2.191

    Barr, V., & Stephenson, C. (2011). Bringing computational thinking to K-12: what is Involved and what is the role of the computer science education community? Acm Inroads, 2(1), 48-54. https://doi.org/10.1145/1929887.1929905

    Bressler, D. M., & Bodzin, A. M. (2013). A mixed methods assessment of students' flow experiences during a mobile augmented reality science game. Journal of computer assisted learning, 29(6), 505-517. https://doi.org/10.1111/jcal.12008

    Buitrago Flórez, F., Casallas, R., Hernández, M., Reyes, A., Restrepo, S., & Danies, G. (2017). Changing a generation’s way of thinking: Teaching computational thinking through programming. Review of Educational Research, 87(4), 834-860. https://doi.org/10.3102/0034654317710096

    Bundy, A. (2007). Computational thinking is pervasive. Journal of Scientific and Practical Computing, 1(2), 67-69.

    Cartney, P. (2010). Exploring the use of peer assessment as a vehicle for closing the gap between feedback given and feedback used. Assessment & Evaluation in Higher Education, 35(5), 551-564. https://doi.org/10.1080/02602931003632381

    Cartney, P., & Rouse, A. (2006). The emotional impact of learning in small groups: highlighting the impact on student progression and retention. Teaching in Higher education, 11(1), 79-91. https://doi.org/10.1080/13562510500400180

    Caruana, R., & Niculescu-Mizil, A. (2006, June). An empirical comparison of supervised learning algorithms. In Proceedings of the 23rd international conference on Machine learning (pp. 161-168). https://doi.org/10.1145/1143844.1143865

    Cassidy, S. (2006). Developing employability skills: peer assessment in higher education. Education + Training, 48(7), 508-517. https://doi.org/10.1108/00400910610705890
    Chang, C.-K. (2014). Effects of using Alice and Scratch in an introductory programming course for corrective instruction. Journal of Educational Computing Research, 51(2), 185-204. https://doi.org/10.2190/EC.51.2.c

    Cheng, W., & Warren, M. (1997). Having second thoughts: Student perceptions before and after a peer assessment exercise. Studies in Higher Education, 22(2), 233-239. https://doi.org/10.1080/03075079712331381064

    Cheng, Y.-C. (2018). The Effect Of Using Board Games In Reducing Language Anxiety And Improving Oral Performance.

    Clark, D. B., Tanner-Smith, E. E., & Killingsworth, S. S. (2016). Digital games, design, and learning: A systematic review and meta-analysis. Review of educational research, 86(1), 79-122. https://doi.org/10.3102/0034654315582065

    Clegg, K., & Bryan, C. (2006). 20 Reflections, rationales and realities. Innovative assessment in higher education, 216.

    Cole, D. A. (1991). Change in self-perceived competence as a function of peer and teacher evaluation. Developmental Psychology, 27(4), 682–688. https://doi.org/10.1037/0012-1649.27.4.682

    Coller, B. D., & Scott, M. J. (2009). Effectiveness of using a video game to teach a course in mechanical engineering. Computers & Education, 53(3), 900-912. https://doi.org/10.1016/j.compedu.2009.05.012

    Dayan, P. (2002). Reinforcement learning. Stevens' Handbook of Experimental Psychology.

    DeNero, J., & Klein, D. (2010, July). Teaching introductory artificial intelligence with pac-man. In First AAAI Symposium on Educational Advances in Artificial Intelligence.

    Ehly, S. W., & Topping, K. J. (1998). Peer-assisted learning. L. Erlbaum Associates.

    Ezezika, O., Fusaro, M., Rebello, J., & Aslemand, A. (2021). The pedagogical impact of board games in public health biology education: the Bioracer Board Game. Journal of Biological Education, 1-12. https://doi.org/10.1080/00219266.2021.1909638

    Gee, J. P. (2005). Learning by design: Good video games as learning machines. E-learning and Digital Media, 2(1), 5-16. https://doi.org/10.2304/elea.2005.2.1.5

    Gokhale, A. A. (1995). Collaborative learning enhances critical thinking. Journal of Technology Education, 7. https://doi.org/10.21061/jte.v7i1.a.2

    Griffin, L. L., & Placek, J. H. (Eds.). (2001). The understanding and development of learners' domain-specific knowledge. Journal of Teaching in Physical Education, 20(4), 299–420.

    Hastie, T., Tibshirani, R., Friedman, J. H., & Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction (Vol. 2, pp. 1-758). New York: springer.

    Hoffman, B., Nadelson, L. Motivational engagement and video gaming: a mixed methods study. Education Tech Research Dev 58, 245–270 (2010). https://doi.org/10.1007/s11423-009-9134-9

    Hogle, J. G. (1996). Considering games as cognitive tools: In search of effective" edutainment.". Citeseer.

    Hsu, T. C., & Liang, Y. S. (2021). Simultaneously improving computational thinking and foreign language learning: Interdisciplinary media with plugged and unplugged approaches. Journal of Educational Computing Research, 59(6), 1184-1207. https://doi.org/10.1177/0735633121992480

    Hsu, T. C., Abelson, H., Lao, N., Tseng, Y. H. & Lin, Y. T. (2021). Behavioral-pattern exploration and development of an instructional tool for young children to learn AI. Computers and Education: Artificial Intelligence, 2, 100012. https://doi.org/10.1016/j.caeai.2021.100012

    Hubwieser, P., Giannakos, M. N., Berges, M., Brinda, T., Diethelm, I., Magenheim, J., Pal, Y., Jackova, J., & Jasute, E. (2015). A global snapshot of computer science education in K-12 schools. In Proceedings of the 2015 ITiCSE on working group reports (pp. 65-83). https://doi.org/10.1145/2858796.2858799

    Hwang, G.-J., Chiu, L.-Y., & Chen, C.-H. (2015). A contextual game-based learning approach to improving students' inquiry-based learning performance in social studies courses. Computers & Education, 81, 13-25. https://doi.org/10.1016/j.compedu.2014.09.006

    Johnson, D. W., & Johnson, R. T. (1989). Cooperation and competition: Theory and research. Interaction Book Company.

    Jonassen, D. H., Howland, J., Moore, J., & Marra, R. M. (2003). Learning to solve problems with technology: A constructivist perspective. Prentice Hall.

    Kline, R. B. (2005). Principles and practice of structural equation modeling 2nd ed. New York: Guilford, 3.

    Kirriemuir, J., & McFarlane, A. (2004). Literature review in games and learning.

    Kuo, W. C., & Hsu, T. C. (2020). Learning computational thinking without a computer: How computational participation happens in a computational thinking board game. The Asia-Pacific Education Researcher, 29(1), 67-83. https://doi.org/10.1007/s40299-019-00479-9

    Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge university press.

    Mercier, M., & Lubart, T. (2021). The effects of board games on creative potential. The Journal of Creative Behavior, 55(3), 875-885. https://doi.org/10.1002/jocb.494

    Mitchell, T. M. (1997). Does machine learning really work? AI magazine, 18(3), 11-11.

    Mitchell, T. M. (2006). The discipline of machine learning (Vol. 9). Pittsburgh: Carnegie Mellon University, School of Computer Science, Machine Learning Department.

    Nesi, Y. M. D., Kusairi, S., & Nafisah, A. W. L. (2022). Analysis of student perceptions of problem-solving learning and peer assessment. Momentum: Physics Education Journal, 6(1), 73-85. https://doi.org/10.21067/mpej.v6i1.6005

    Sweller, J. (2005). Implications of cognitive load theory for multimedia learning. The Cambridge handbook of multimedia learning, 3(2), 19-30.

    Panadero, E., Jonsson, A., Strijbos, JW. (2016). Scaffolding Self-Regulated Learning Through Self-Assessment and Peer Assessment: Guidelines for Classroom Implementation. In: Laveault, D., Allal, L. (eds)Assessment for Learning: Meeting the Challenge of Implementation. The Enabling Power of Assessment, 4. Springer, Cham. https://doi.org/10.1007/978-3-319-39211-0_18

    Panitz, T. (1999). Benefits of cooperative learning in relation to student motivation. Motivation from within: Approaches for encouraging faculty and students to excel, New directions for teaching and learning, 59-68.

    Papert, S. (1980). Mindstorms: children, computers, and powerful ideas Basic Books. Inc. New York, NY.

    Pellas, N. (2014). The influence of computer self-efficacy, metacognitive self-regulation and self-esteem on student engagement in online learning programs: Evidence from the virtual world of Second Life. Computers in Human Behavior, 35, 157-170. https://doi.org/10.1016/j.chb.2014.02.048

    Perkins, D. (1999). The many faces of constructivism. Educational leadership, 57(3), 6-11.

    Pond, K., & Ul-Haq, R. (1997). Learning to assess students using peer review. Studies in Educational Evaluation, 24, 331-348.

    Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural networks, 61, 85-117. https://doi.org/10.1016/j.neunet.2014.09.003

    Selby, C., & Woollard, J. (2013). Computational thinking: the developing definition.

    Sweller, J. (2005). Implications of cognitive load theory for multimedia learning. The Cambridge handbook of multimedia learning, 3(2), 19-30.

    Tedre, M., & Denning, P. J. (2016, November). The long quest for computational thinking. In Proceedings of the 16th Koli Calling international conference on computing education research (pp. 120-129). https://doi.org/10.1145/2999541.2999542

    Topping, K. J. (2009). Peer assessment. Theory into practice, 48(1), 20-27. https://doi.org/10.1080/00405840802577569

    Topping, K. J., Smith, E. F., Swanson, I., & Elliot, A. (2000). Formative peer assessment of academic writing between postgraduate students. Assessment & Evaluation in Higher Education, 25(2), 149-169. https://doi.org/10.1080/713611428

    Vygotsky, L. (1978). Interaction between learning and development. Readings on the development of children, 23(3), 34-41.

    Wen, M. L., & Tsai, C.-C. (2008). Online peer assessmnet in an inservice science and mathematics teacher education course. Teaching in Higher Education, 13(1), 55–67. https://doi.org/10.1080/13562510701794050.

    Willey, K., & Freeman, M. (2006). Improving teamwork and engagement: the case for self and peer assessment. Australasian journal of engineering education(February 2006).

    Wing, J. (2011). Research notebook: Computational thinking—What and why. The link magazine, 6.

    Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35.

    Wing, J. M. (2008). Computational thinking and thinking about computing. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 366(1881), 3717-3725. https://doi.org/10.1098/rsta.2008.0118

    Wu, C.-J., Chen, G.-D., & Huang, C.-W. (2014). Using digital board games for genuine communication in EFL classrooms. Educational Technology Research and Development, 62(2), 209-226. https://doi.org/10.1007/s11423-013-9329-y

    Yadav, A., Mayfield, C., Zhou, N., Hambrusch, S., & Korb, J. T. (2014). Computational thinking in elementary and secondary teacher education. ACM Transactions on Computing Education (TOCE), 14(1), 1-16. https://doi.org/10.1145/2576872

    Yağcı, M. (2019). A valid and reliable tool for examining computational thinking skills. Education and Information Technologies, 24(1), 929-951. https://doi.org/10.1007/s10639-018-9801-8

    Yorke, M. (2003). Formative assessment in higher education: Moves towards theory and the enhancement of pedagogic practice. Higher education, 45(4), 477-501. https://doi.org/10.1023/A:1023967026413

    Zhu, X. J. (2005). Semi-supervised learning literature survey.

    無法下載圖示 電子全文延後公開
    2027/07/05
    QR CODE