現今國中生在升學壓力以及3C產品的影響下，身體活動不足率仍高居不下，此外青少年對於體育課的喜愛程度及自己的運動表現也較無正向的自覺。基於國內外對於全民運動的重視，以及規律運動對於青少年健康、學業、情緒的正向影響，若能於青少年時期奠定規律的運動習慣，長久下來或許能實現降低身體活動不足率的長遠目標。因此本研究希望藉由科技融入體育教學，提升國中生在羽球技能、運動動機與自我效能的表現。爰此，本研究旨在探討智聯網羽球課程對國中生羽球技能、運動動機及自我效能的影響，並於後測之課程滿意度問卷進一步探討影響運動動機與自我效能及學習羽球技能的成效因素。本研究採為期四週的不等組準實驗研究設計，研究對象為台南市某國中一年級共二班的學生，由同一位專業體育教師進行教學。兩個班級分為實驗班和對照班各23位學生，進行量化前測、後測及問卷調查。
主要研究結果發現如下：一、就羽球技能而言，實驗班與對照班的後測皆由提升，但接受智聯網羽球課程的實驗班後測平均數優於對照班。二、就運動動機而言，實驗班後測優於對照班，且實驗班後測呈現上升趨勢，對照班則呈現下降趨勢。三、就自我效能而言，實驗班後測優於對照班，且實驗班後測呈現上升趨勢，對照班則呈現下降趨勢。四、從課程滿意度問卷之調查結果可知，實驗班學生對於課程的滿意度遠勝於對照班，且超過半數的學生仍認為智聯網羽球課程十分有趣，反映了智聯網羽球課程有助於改善學生對於體育課「無聊」的感受。
本研究說明研究限制，並予以未來研究建議如下：一、加入羽球步伐與跑動擊球進行驗證。二、結合即時影像回饋。三、進行長時間的觀察與測量。

This study examined the effects of integrating AIoT technology into badminton instruction on junior high school students’ badminton skills, exercise motivation, and self-efficacy. A four-week quasi-experimental design with nonequivalent groups was employed. Participants were 46 first-year students from two classes at a junior high school in Tainan City, taught by the same physical education teacher. One class received an AIoT badminton course (experimental group), while the other received traditional badminton instruction (control group). Quantitative pretests, posttests, and questionnaire surveys were conducted.
Results indicated that both groups improved in badminton skills; however, the experimental group demonstrated greater gains than the control group. In addition, the experimental group showed higher levels of exercise motivation and self-efficacy at posttest, whereas both variables declined in the control group. Course satisfaction was also significantly higher in the experimental group, with many students reporting that the AIoT badminton course was engaging and reduced feelings of boredom in physical education classes.
Future research is recommended to incorporate badminton footwork and dynamic hitting, integrate real-time video feedback, and conduct longitudinal investigations to examine long-term effects.

王振興、許煜亮、龔雅慈、林裕晴、楊雅婷（2023）。打造全民皆宜之智聯羽球學院—精準培育下一個台灣羽球之光。國家科學及技術委員會補助專題研究計畫報告（編號：NSTC 112-2425-H-006-003-），未出版。
行政院（2022）。台灣運動 x 科技行動計畫。臺科會字第 1110175552 號函
何全進（2002）。運動員認同、運動樂趣來源與運動動機之相關研究。大專體育學術專刊，162-171。
林幸樺、吳詩薇、梁嘉音、卓俊伶（2022）。自我控制回饋頻率的限制效應：動作表現與學習。大專體育學刊，24(2)，271-286。
林季燕、季力康（2003）。運動動機量表之編製—信度與效度分析。臺灣運動心理學報，(2)，15-32。
林俐伶、黃秀敏、林瑞興（2009）。規律運動對健康體適能之影響。載於林瑞興（主編）2009年第二屆運動科學暨休閒遊憩管理學術研討會論文集（頁187-192）。國立屏東大學。
兒童福利聯盟（2022）。兒童福祉調查報告。
張春興（1994）。教育心理學－三化取向的理論與實踐。東華。
陳瑞辰、張川鈴（2020）。臺灣師範校院學生體育課程滿意度、休閒運動態度與休閒運動參與之研究。臺灣體育運動管理學報，20(1)，59-86。
教育部體育署（2021）。運動現況調查結案報告書。
教育部體育署（2018）。教育部體育署 107 學年度各級學校學生運動參與情形。取自https://www.sa.gov.tw/Resource/Ebook/637273139031334410.pdf
教育部體育署（2019）。教育部體育署 108 學年度各級學校學生運動參與情形。取自https://www.sa.gov.tw/Resource/Ebook/637475177067642544.pdf
教育部體育署（2020）。教育部體育署 109 學年度各級學校學生運動參與情形。取自https://www.sa.gov.tw/Resource/Ebook/637824163470732737.pdf
教育部體育署（2021）。教育部體育署 110 學年度各級學校學生運動參與情形。取自https://www.sa.gov.tw/Resource/Ebook/638137983627178180.pdf
蔡俊傑（2011）。青少年健身自我效能量表之編製。大專體育學刊，13(3)，277-288。
蔡耀程（2018）。羽球技能運動訓練暨體能提升之探討。嶺東體育暨休閒學刊，16，15-23。
衛生福利部國民健康署（2018）。全民身體活動指引。取自https://www.hpa.gov.tw/Pages/Detail.aspx?nodeid=1412&pid=8170
衛生福利部國民健康署（2021）。青少年健康行為調查報告。取自https://www.hpa.gov.tw/Pages/Detail.aspx?nodeid=257&pid=16037
龔雅慈、紀世清（2020）。利用多層面Rasch模式發展大專羽球教學之評量觀察表以高遠球動作為例。臺灣運動教育學報，15(2)，1-22。
Abós, Á., García-González, L., Aibar, A., & Serrano, J. S. (2021). Towards a Better Understanding of the Role of Perceived Task Variety in Physical Education: A Self-Determination Theory Approach. Psychology of Sport and Exercise, 56(3).
Bandura, A. (1997a). Self-efficacy: The exercise of control. Freeman.
Bandura, A. (1977b) . Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84, 191 - 215.
Bandura, A. (1982). Self-efficacy mechanism in human agency. American Psychologist, 37, 122 - 147.
Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, N.J.: Prentice-Hall.
Bandura, A. (1991). Self-efficacy mechanism in physiological activation and health-promoting behavior. In rJ. Madden, IV (Ed. ), Neurobiology of learning, emotion and affect (pp. 229 - 270) . New York:Raven, 1991.
Bangert-Drowns, R. L., Kulik, C.-L. C., Kulik, J. A., & Morgan, M. (1991). The Instructional Effect of Feedback in Test-Like Events. Review of Educational Research, 61(2), 213-238.
Cao, L., & Zhan, C. (2022). Exploration of new community fitness mode using intelligent life technology and AIoT. International Journal of Grid and Utility Computing, 13(1), 57-65.
Caroll, W. R., & Bandura, A. (1985). Role of timing of visual monitoring and motor rehearsal in observational learning of action patterns. Journal of Motor Behavior,17, 269 - 281.
Carroll, W. R., & Bandura, A.(1987) . Translating cognition into action: The role of visual guidance in observational learning. Journal of Motor Behavior, 19, 385 - 398.
Carroll, W. R., & Bandura, A. (1990). Representational guidance of action production in observational learning: A causal analysis.Journal of Motor Behavior, 22, 85 - 97.
Chambliss, C. A., & Murray, E. J. (1979). Cognitive procedures for smoking reduction: Symptom attribution lur versus efficacy attribution. Cognitive Therapy and Re-search, 3, 91 - 96.
Chang, Y. S., Chen, S. Y., Yu, K. C., Chu, Y. H., & Chien, Y. H. (2015). Effects of cloud‐based m‐learning on student creative performance in engineering design. British Journal of Educational Technology, 48(1), 101-112.
Chiviacowsky, S., & Wulf, G. (2002). Self-controlled feedback: does it enhance learning because performers get feedback when they need it? Res Q Exerc Sport, 73(4), 408-415.
Cioffi, D. (1991). Beyond attentional strategies: A cognitive-perceptual model of somatic interpretation.Psychological Bulletin, 109, 25 - 41.
Chu, W., Shih, C., Chou, W.-Y., Ahamed, S., & Hsiung, P.-A. (2019). Artificial Intelligence of Things in Sports Science: Weight Training as an Example. Computer, 52, 52-61.
Deci, E. L., & Ryan, R. M. (1985). Intrinsic Motivation and Self-Determination in Human Behavior. Springer New York, NY.
Fang, L., & Sun, M. (2021). Motion recognition technology of badminton players in sports video images. Future Generation Computer Systems,124, 381-389.
Fiona, C. B., Salih, S. A.-A., Stuart, B., Katja, B., Matthew, P. B., Greet, C., Catherine, C., Jean-Philippe, C., Sebastien, C., Roger, C., Paddy, C. D., Loretta, D., Ulf, E., Joseph, F., Christine, M. F., Leandro, G., Muthoni, G., Russell, J., Peter, T. K., . . . Juana, F. W. (2020). World Health Organization 2020 guidelines on physical activity and sedentary behaviour. British Journal of Sports Medicine, 54(24), 1451.
Garcia, A. W., & King, A. C. (1991) . Predicting long-term adherence to aerobic exercise: A comparison of two models. Journal of Sport and Exercise Psychology, 13, 394 - 410.
Hagger, M. S., & Chatzisarantis, N. L. (2016). The Trans-Contextual Model of Autonomous Motivation in Education: Conceptual and Empirical Issues and Meta-Analysis. Rev Educ Res, 86(2), 360-407.
Hinojo-Lucena, F., López-Belmonte, J., Cabrera, A., Torres, J. M., & Sánchez, S. (2020). Academic Effects of the Use of Flipped Learning in Physical Education. International Journal of Environmental Research and Public Health, 17, 276.
Hofstetter, C. R., Hovell, M. F., & Sallis, J. F. (1990).Social learning correlates of exercise self-efficacy:Early experiences with physical activity. Social Science Medicine, 31, 1169 - 1176.
Huang, T., Li, Y., & Zhu, W. (2021, 17-21 Aug. 2021). An Auxiliary Training Method for Single-Player Badminton. 2021 16th International Conference on Computer Science & Education (ICCSE).
Hung, H.-C., Young, S. S.-C., & Lin, K.-C. (2018). Exploring the effects of integrating the iPad to improve students’ motivation and badminton skills: a WISER model for physical education. Technology, Pedagogy and Education, 27(3), 265-278.
Juvancic-Heltzel, J. A., Glickman, E. L., & Barkley, J. E. (2013). The effect of variety on physical activity: a cross-sectional study. J Strength Cond Res, 27(1), 244-251.
Kulhavy, R. W., & Stock, W. A. (1989). Feedback in written instruction: The place of response certitude. Educational Psychology Review, 1(4), 279-308.
Kuo, K.-P., Tsai, H.-H., Lin, C.-Y., & Wu, W.-T. (2020). Verification and Evaluation of a Visual Reaction System for Badminton Training. Sensors, 20, 6808.
Lin, K.-C., Wei, C.-W., Lai, C.-L., Cheng, I. L., & Chen, N.-S. (2021). Development of a badminton teaching system with wearable technology for improving students’ badminton doubles skills. Educational Technology Research and Development, 69(2), 945-969.
Lin, Y.-N., Hsia, L.-H., & Hwang, G.-J. (2021). Promoting pre-class guidance and in-class reflection: A SQIRC-based mobile flipped learning approach to promoting students’ billiards skills, strategies, motivation and self-efficacy. Computers & Education, 160, 104035.
Liu, D. (2020). Your Experimental Study on Many Balls Training Method in Badminton Teaching of the Teenagers. International Journal of Social Science and Education Research, 3(2), 147-151.
Luo, J., Hu, Y., Davids, K., Zhang, D., Gouin, C., Li, X., & Xu, X. (2022). Vision-based movement recognition reveals badminton player footwork using deep learning and binocular positioning. Heliyon, 8(8).
Lu, S., Zhang, X., Wang, J., Wang, Y., Fan, M., & Zhou, Y. (2021). An IoT-Based Motion Tracking System for Next-Generation Foot-Related Sports Training and Talent Selection. Journal of Healthcare Engineering, 2021, 9958256.
Mason, B., & Bruning, R. (2001). Providing Feedback in Computer-based Instruction: What the Research Tells Us. Center for Instructional Innovation, 15.
Ntoumanis, N. (2001). A self-determination approach to the understanding of motivation in physical education. Br J Educ Psychol, 71(Pt 2), 225-242.
Østerlie, O., & Mehus, I. (2020). The Impact of Flipped Learning on Cognitive Knowledge Learning and Intrinsic Motivation in Norwegian Secondary Physical Education. Education Sciences, 10.
Pan, L. (2019). A Big Data-Based Data Mining Tool for Physical Education and Technical and Tactical Analysis. International Journal of Emerging Technologies in Learning (iJET), 14, 220.
Pelletier, L. G., Rocchi, M. A., Vallerand, R. J., Deci, E. L., & Ryan, R. M. (2013). Validation of the revised sport motivation scale (SMS-II). Psychology of Sport and Exercise, 144(3), 329-341.
Rosenthal, T. L., & Zimmerman, B. J. (1978) . Social learning and cognition. New York: Academic.
Schmidt, S. R. (1988). Test expectancy and individual-item versus relational processing. The American Journal of Psychology, 101(1), 59–71.
Straub, R. (1997). Students’ Reactions to Teacher Comments: An Exploratory Study. Research in the Teaching of English.
Sylvester, B. D., Gilchrist, J. D., O’Loughlin, J., & Sabiston, C. M. (2020). Sampling sports during adolescence, exercise behaviour in adulthood, and the mediating role of perceived variety in exercise. Psychology & Health, 35(11), 1368-1383.
Vasconcellos, D., Parker, P., Hilland, T., Cinelli, R., Owen, K., Kapsal, N., Lee, J., Antczak, D., Ntoumanis, N., Ryan, R., & Lonsdale, C. (2019). Self-Determination theory applied to physical education: A systematic review and meta-analysis. Journal of Educational Psychology, 112.
Wang, Y., Zhao, Y., Chan, R. H. M., & Li, W. J. (2018). Volleyball Skill Assessment Using a Single Wearable Micro Inertial Measurement Unit at Wrist. IEEE Access, 6, 13758-13765.
Wei, W., Piuri, V., Pedrycz, W., & Ahmed, S. H. (2022). Special Issue on Artificial Intelligence-of-Things (AIoT): Opportunities,Challenges, and Solutions-Part I: Artificial Intelligence Applications in Various Fields. Future Generation Computer Systems, 137, 216-218.
Wei, W., Piuri, V., Pedrycz, W., & Ahmed, S. H. (2022b). Special Issue on Artificial Intelligence-of-Things (AIoT): Opportunities, Challenges, and Solutions-Part II: Artificial-Intelligence-Powered Internet of Things. Future Gener. Comput. Syst., 137(C), 186–188.
Xie, Q., Jin, N., & Lu, S. (2023). Lightweight Football Motion Recognition and Intensity Analysis Using Low-Cost Wearable Sensors. Applied Bionics and Biomechanics, 2023(1), 2354728.
Xu, Q., & Jia, Q. (2022). The Cultivation and Training Effect of the Subconscious Mind in Physical Education and Training by Intelligent Internet of Things Network Computing. Security and Communication Networks, 2022.
Zou, Y., Wang, D., Hong, S., Ruby, R., Zhang, D., & Wu, K. (2020). A Low-Cost Smart Glove System for Real-Time Fitness Coaching. IEEE Internet of Things Journal, 7, 7377-7391.
Zulkifli, A. F., & Danis, A. (2022). Technology in physical education: Using movement analysis application to improve feedback on sports skills among undergraduate physical education students. Social Sciences & Humanities Open, 6(1), 100350.