English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 113160/144130 (79%)
Visitors : 50753474      Online Users : 704
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 >  Item 140.119/146574
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/146574


    Title: 應用設計思考提升服務品質:以教學助理即服務為案例
    Design Thinking for Service Enhancement: a case of Teaching Assistant as a Service
    Authors: 郭丞哲
    Guo, Cheng-Zhe
    Contributors: 蔡瑞煌
    Tsaih, Rua-Huan
    郭丞哲
    Guo, Cheng-Zhe
    Keywords: 人工智慧即服務
    機器學習維運
    設計思考
    教學助理即服務
    學習演算法
    AIaaS
    MLOps
    Design thinking
    TAaaS
    learning algorithm
    Date: 2023
    Issue Date: 2023-08-02 14:05:19 (UTC+8)
    Abstract: 本研究探討了在教學助理即服務(TAaaS)的背景下,應用設計思考原則來提升服務品質。所提出的TAaaS具有MLOps功能,使學生能夠在修讀新型學習演算法課程時,利用自己和他人的學習模組開發和部署他們自己的新型學習演算法、程式碼和AI模型。通過整合強調同理心、實驗和原型設計的設計思考原則,本研究旨在提高使用TAaaS系統的使用者體驗和滿意度。挑戰在於允許學生通過反覆嘗試,獨立於多個管道(如模型管道、部署管道和預測服務)創建自己的「新型學習演算法」。通過設計思考的迭代和以人為本的特性,本研究展示了將設計思考原則納入服務設計過程的潛在利益,最終形成更符合使用需求和期望的一套AI解決方案。
    This study explores the application of design thinking principles for service enhancement in the context of a Teaching Assistant as a Service (TAaaS). The TAaaS is equipped with MLOps capabilities, enabling students to develop and deploy their own new learning algorithms, codes, and AI models by utilizing their own and others’ learning modules while enrolled in the New Learning Algorithms course. By integrating design thinking principles, which emphasize empathy, experimentation, and prototyping, this study aims to enhance the user experience and satisfaction in using the TAaaS system. The challenge lies in allowing students to create their own “new learning algorithm” through trial and error, independently from the multiple pipelines, such as model pipeline, deployment pipeline, and prediction service. Through the iterative and user-centric nature of design thinking, this study demonstrates the potential benefits of incorporating design thinking principles into the service design process, ultimately leading to a more successful AI solution tailored to the users’ needs and expectations.
    Reference: Barlas, P., Kyriakou, K., Guest, O., Kleanthous, S., and Otterbacher, J. (2021). To” see” is to stereotype: Image tagging algorithms, gender recognition, and the accuracy-fairness trade-off. Proceedings of the ACM on Human-Computer Interaction, 4(CSCW3):1–31.
    Boag, S., Dube, P., El Maghraoui, K., Herta, B., Hummer, W., Jayaram, K., Khalaf, R., Muthusamy, V., Kalantar, M., and Verma, A. (2018). Dependability in a multi-tenant multi-framework deep learning as-a-service platform. In 2018 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W), pages 43–46. IEEE.
    Brown, T. et al. (2008). Design thinking. Harvard business review, 86(6):84.
    Brown, T. and Katz, B. (2011). Change by design. Journal of product innovation management, 28(3):381–383.
    Brown, T. and Martin, R. (2015). Design for action. Harvard Business Review, 93(9):57–64.
    Clark, K., Smith, R., et al. (2008). Unleashing the power of design thinking. Design Management Review, 19(3):8–15.
    Dhillon, S. K., Ganggayah, M. D., Sinnadurai, S., Lio, P., and Taib, N. A. (2022). Theory and practice of integrating machine learning and conventional statistics in medical data analysis. Diagnostics, 12(10):2526.
    Ebert, C., Gallardo, G., Hernantes, J., and Serrano, N. (2016). Devops. Ieee Software, 33(3):94–100.
    Elshawi, R., Sakr, S., Talia, D., and Trunfio, P. (2018). Big data systems meet machine learning challenges: towards big data science as a service. Big data research, 14:1–11.
    Gasson, S. (2003). Human-centered vs. user-centered approaches to information system design. Journal of Information Technology Theory and Application (JITTA), 5(2):5.
    Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep learning. MIT press.
    Google Cloud Architecture Center (2020). Mlops: Continuous delivery and automation pipelines in machine learning.
    Hasso Plattner Institute of Design at Stanford University (2023). Tools for taking action.
    KELLEY, T. A. (2001). The art of innovation: Lessons in creativity from IDEO, America’s leading design firm, volume 10. Broadway Business.
    Kreuzberger, D., Kühl, N., and Hirschl, S. (2022). Machine learning operations (mlops): Overview, definition, and architecture. arXiv preprint arXiv:2205.02302.
    LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. nature, 521(7553):436–444.
    Leite, L., Rocha, C., Kon, F., Milojicic, D., and Meirelles, P. (2019). A survey of devops concepts and challenges. ACM Computing Surveys (CSUR), 52(6):1–35.
    Lindberg, T., Meinel, C., and Wagner, R. (2011). Design thinking: A fruitful concept for it development? In Design thinking, pages 3–18. Springer.
    Lins, S., Pandl, K. D., Teigeler, H., Thiebes, S., Bayer, C., and Sunyaev, A. (2021). Artificial intelligence as a service. Business & Information Systems Engineering, 63(4):441–456.
    Lu, Y. (2019). Artificial intelligence: a survey on evolution, models, applications and future trends. Journal of Management Analytics, 6(1):1–29.
    Lucena, P., Braz, A., Chicoria, A., and Tizzei, L. (2017). Ibm design thinking software development framework. In Brazilian workshop on agile methods, pages 98–109. Springer.
    Mell, P., Grance, T., et al. (2011). The nist definition of cloud computing.
    Nagarhalli, T. P., Vaze, V., and Rana, N. (2021). Impact of machine learning in natural language processing: A review. In 2021 third international conference on intelligent communication technologies and virtual mobile networks (ICICV), pages 1529–1534. IEEE.
    Norman, D. (2017). Design, business models, and human-technology teamwork: As automation and artificial intelligence technologies develop, we need to think less about human-machine interfaces and more about human-machine teamwork. Research Technology Management, 60(1):26–30.
    Pereira, J. C. and de F.S.M. Russo, R. (2018). Design thinking integrated in agile software development: A systematic literature review. Procedia Computer Science, 138:775–782. CENTERIS 2018 - International Conference on ENTERprise Information Systems/ ProjMAN 2018 - International Conference on Project MANagement / HCist 2018 -International Conference on Health and Social Care Information Systems and Technologies, CENTERIS/ProjMAN/HCist 2018.
    Rai, A., Constantinides, P., and Sarker, S. (2019). Next generation digital platforms:: Toward human-ai hybrids. Mis Quarterly, 43(1):iii–ix.
    Riedl, M. O. (2019). Human-centered artificial intelligence and machine learning. Human Behavior and Emerging Technologies, 1(1):33–36.
    Shrestha, A. and Mahmood, A. (2019). Review of deep learning algorithms and architectures. IEEE access, 7:53040–53065.
    Stahl, B. C., Andreou, A., Brey, P., Hatzakis, T., Kirichenko, A., Macnish, K., Shaelou, S. L., Patel, A., Ryan, M., and Wright, D. (2021). Artificial intelligence for human flourishing–beyond principles for machine learning. Journal of Business Research, 124:374–388.
    Stembert, N. and Harbers, M. (2019). Accounting for the human when designing with ai: challenges identified. CHI’19-Extended Abstracts, Glasgow, Scotland Uk—May 04-09, 2019.
    Tsaih, R.-H. (2022). The course materials of new learning algorithm
    Verganti, R., Vendraminelli, L., and Iansiti, M. (2020). Innovation and design in the age of artificial intelligence. Journal of Product Innovation Management, 37(3):212–227.
    Vetterli, C., Uebernickel, F., Brenner, W., Petrie, C., and Stermann, D. (2016). How deutsche bank’s it division used design thinking to achieve customer proximity. 15:37–53.
    Xu, W. (2019). Toward human-centered ai: a perspective from human-computer interaction. interactions, 26(4):42–46.
    Xu, W., Dainoff, M. J., Ge, L., and Gao, Z. (2021). From human-computer interaction to human-ai interaction: new challenges and opportunities for enabling human-centered ai. arXiv preprint arXiv:2105.05424, 5.
    Zhang, C. and Lu, Y. (2021). Study on artificial intelligence: The state of the art and future prospects. Journal of Industrial Information Integration, 23:100224.
    Description: 碩士
    國立政治大學
    資訊管理學系
    110356021
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0110356021
    Data Type: thesis
    Appears in Collections:[資訊管理學系] 學位論文

    Files in This Item:

    File Description SizeFormat
    602101.pdf2020KbAdobe PDF20View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback