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研究生: 謝秉勳
Hsieh, Ping-Hsun
論文名稱: 以品質機能展開法進行機械工程學習APP介面設計-以粉末冶金為例
Interface Design of Mechanical Engineering Learning Apps Using Quality Function Deployment: A Case Study on Powder Metallurgy
指導教授: 劉說芳
Liu, Shuo-Fang
學位類別: 碩士
Master
系所名稱: 規劃與設計學院 - 工業設計學系
Department of Industrial Design
論文出版年: 2025
畢業學年度: 113
語文別: 英文
論文頁數: 103
中文關鍵詞: 機械工程教育STEMAPP設計體驗設計
外文關鍵詞: Mechanical Engineering Education, STEM, APP Design , Experience Design
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  • 機械原理出現在如今工業化社會的各方各面,對於這些原理及知識的理解可以讓人們更加通透的看待世界,目前各國也逐漸重視這些科學性的教育,不過目前的教育應用程式研究雖然提高了教學成效,但針對優化STEAM領域的學習體驗或提高學習動機等部分卻不多。本研究致力於融合STEAM的核心思想於原有的學習內容之中,以達到更加跨域、有創造力的學習成果。
    在研究初期參考了文獻以及現在的數位教學媒體現狀並進行了半結構式訪談,在這步驟中除了獲得使用者需求及看法外也進行了對這些需求進行整理以及統計。第二階段中,本研究進行品質機能展開的小組會議,在建構品質機能展開左牆與天花板時我們加入了第一階段所獲之使用者需求、多媒體表現方式等等,在這個階段中可以發現高分的設計要求有很強的相關性且具有創造力。第三階段中,本研究取用了品質機能展開產出的設計要求來進行原型APP設計,並且在第四階段設計原型APP時本研究採取了教為創新、結合性的方式來滿足設計要求,最後得出以三大教學演示方式: 專案式學習、聆聽模式、短影片並搭配多個輔助設計來滿足用戶需求。
    最後本研究對設計出來的APP 與傳統式的學習法進行比較及驗證,其中包含對於APP與傳統式的學習動機及體驗量化研究、對APP的可用性的量化研究以及對於APP使用的觀察法以及半結構式訪談。在這個階段中可以發現APP學習法在學習體驗上有半數具有顯著改善,而學習動機量表中的兩個量表具有半數以上的顯著性改善以及其中預期成果的全數顯著性改善。在質性分析方面本研究使用觀察法分析了使用者對於三種教學演示方式的使用程度、停留時間以及專注度後進行了半結構式訪談,在這兩個過程知中,本研究發現三種功能可已分別滿足數種不同需求特徵,這些特徵可能複數出現在同一個使用者身上,而這次APP中的每個教學演示方式又分別可以滿足多種特徵。因此本研究推測,為了同時滿足改善原有學習體驗,並且滿足本研究所分析之學習需求,同時保留多種知識的多媒體演示方式在APP十分中要,保留選擇權對使用者來說除了增加APP對他們需求的寬容度以外也增加學習效率以及學習動機。
    最後本研究認為新興媒體在幫助學習困難的學習內容時可以帶來意想不到的幫助,這些幫助多數源自於他們的軟性特質,並且可以提高學習興趣、體驗等等這類左右普通人是否學習的原因。

    The principles of mechanics appear in all aspects of today's industrialized society. Understanding these principles and knowledge allows people to view the world more comprehensively. Currently, countries worldwide are increasingly emphasizing scientific education. However, while research on educational applications has improved teaching effectiveness, there is still a lack of focus on optimizing learning experiences in the STEAM field or enhancing learning motivation. This study aims to integrate the core concepts of STEAM into existing learning content to achieve more interdisciplinary and creative learning outcomes.
    In the initial stage of the study, literature and the current state of digital teaching media were reviewed, followed by semi-structured interviews. This step not only gathered user needs and opinions but also organized and statistically analyzed these needs. In the second stage, this study conducted a group meeting for Quality Function Deployment (QFD). When constructing the left wall and ceiling of the QFD, we incorporated user needs obtained in the first stage, multimedia presentation methods, and other factors. At this stage, we found that high-scoring design requirements were highly correlated and demonstrated creativity. In the third stage, the study used the design requirements derived from QFD to design a prototype APP. In the fourth stage, while designing the prototype APP, this study adopted an innovative and integrative teaching approach to meet design requirements. Finally, three main teaching demonstration methods were derived: project-based learning, listening mode, and short videos, supplemented by multiple auxiliary designs to meet user needs.
    Ultimately, this study compared and validated the designed APP with traditional learning methods. This included a quantitative study on learning motivation and experience between the APP and traditional learning, a usability study of the APP, and observational methods along with semi-structured interviews on APP usage. At this stage, it was found that APP-based learning showed significant improvement in learning experience for half of the participants. In the learning motivation scale, two subscales exhibited significant improvements in more than half of the cases, with expected outcomes showing a complete significant improvement. In qualitative analysis, this study used observational methods to analyze user engagement with the three teaching demonstration methods, including usage frequency, duration, and focus level, followed by semi-structured interviews. Through these two processes, the study found that the three functions could each meet several different user needs, with multiple features potentially coexisting in the same user. Additionally, each teaching demonstration method in the APP could satisfy multiple features. Therefore, this study hypothesizes that to simultaneously improve the original learning experience and meet the analyzed learning needs, retaining multiple multimedia knowledge presentation methods in the APP is essential. Providing users with choices not only increases the APP's flexibility to accommodate their needs but also enhances learning efficiency and motivation.
    Finally, this study concludes that emerging media can provide unexpected assistance in learning difficult content. These benefits largely stem from their soft attributes, which can increase learning interest and experience—factors that often determine whether an individual engages in learning.

    摘要 ii SUMMARY iv ACKNOWLEDGEMENTS vi TABLE OF CONTENTS vii LIST OF TABLES x LIST OF FIGURES xi LIST OF SYMBOLS AND ABBREVIATIONS xiii CHAPTER 1 INTRODUCTION 1 1.1 Research Background 1 1.2 Research Motivation 2 1.3 Research Objectives and Significance 3 1.4 Scope and Limitations of This Study 3 1.4.1 Participant Restrictions 4 1.4.2 Definition of Research Field 4 1.4.3 Selection of Mechanical Knowledge 4 1.4.4 Research Method 4 1.5 Structure of the Thesis: 5 CHAPTER 2 Literature Review 6 2.1 STEM/STEAM Education 6 2.1.1 Digital Media-Based STEM/STEAM Education 7 2.1.2 From STEM to STEAM 9 2.2 Overview of Mechanical Knowledge 9 2.2.1 Powder Metallurgy 11 2.3 Engineering and Technology Education Application Experience Design 11 2.3.1 Cognitive Load Theory 12 2.3.2 Interface Design Principles 12 2.3.3 Existing Open Mechanical Engineering Learning Apps 13 2.4 Quality Function Deployment (QFD) 15 2.4.1 Quality Function Deployment (QFD) - House of Quality (HOQ) Construction 15 2.4.2 Engineering Importance Evaluation Method 18 2.5 Affinity Diagram Method 18 2.6 Evaluation Methods 19 2.6.1 User Experience Questionnaire (UEQ) 19 2.6.2 Motivated Strategies for Learning Questionnaire (MSLQ) 19 2.6.3 System Usability Scale (SUS) 19 CHAPTER 3 Research Methodology 21 3.1 Research Steps 21 3.1.1 First Phase: Semi-structured Interviews 21 3.1.2 Second Phase: Quality Function Deployment 22 3.1.3 Prototype APP Design and Construction Using Figma 23 3.1.4 Fourth Stage: Prototype APP Validation 23 3.2 Research Participants 25 3.3 Research Tools 25 3.3.1 Semi-Structured Interviews: 25 3.3.2 Quantitative Research Tools 26 3.3.3 Data Analysis Tools 27 CHAPTER 4 Results and Discussion 28 4.1 Semi-Structured Interview Results and KJ Method 28 4.1.1 Semi-Structured Interview Results 28 4.1.2 KJ Method Results 30 4.1.3 Summary of Interviews and KJ Method 32 4.2 QFD Results 33 4.2.1 Design Requirements Analysis and Selection 35 4.3 Learning application programming 35 4.3.1 Application Function Configuration and Design Overview 36 4.3.2 QFD Design Concepts Contained in the Learning Function 39 4.3.3 Application Interface Design 40 4.4 Research Validation 43 4.5 Scale Analysis 44 4.5.1 SUS 44 4.5.2 MSLQ 45 4.5.3 UEQ Scale Analysis and T-Test 49 4.5.4 Effectiveness Test 50 4.5.5 Quantitative Research Summary 51 4.6 Qualitative Research Results 51 4.6.1 Observation Method 52 4.6.2 Semi-structured Interviews 53 4.7 Summary 59 CHAPTER 5 Conclusion and Recommendations 61 5.1 Conclusion 61 5.2 Future Research and Recommendations 63 References 65 Appendix A :Questionnaire Content 74 A.1 MSLQ- 7-point Likert scale(1 = Strongly Disagree and 7 = Strongly Agree)74 A.2 UEQ - 7-point Likert scale 76 A.3 SUS 5-point Likert scale(1 = Strongly Disagree and 5 = Strongly Agree) 77 Appendix B Interview outline 78 B.1 User Interviews Before Conducting QFD 78 B.2 User Interviews after Try the APP 78 Appendix C T-TEST 79 C.1 MSLQ Independent Samples T-test 79 C.2 UEQ Independent Samples T-test 83 Appendix D HOQ 88 D.1 FUNCTION HOQ 88 D.2 INTERFACE HOQ 89

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