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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 許永真 | |
| dc.contributor.author | Yi-Ching Huang | en |
| dc.contributor.author | 黃怡靜 | zh_TW |
| dc.date.accessioned | 2021-05-12T09:34:39Z | - |
| dc.date.available | 2019-07-01 | |
| dc.date.available | 2021-05-12T09:34:39Z | - |
| dc.date.copyright | 2018-06-14 | |
| dc.date.issued | 2018 | |
| dc.date.submitted | 2018-06-12 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/handle/123456789/1234 | - |
| dc.description.abstract | 創造性任務因沒有正確答案且缺乏標準定義，讓許多人感到非常棘手，通常需要投入大量時間與精力去學習並精進專業知識與技能，才能夠完成此種任務。而且，在解決問題的過程中，必須取得外部回饋，並且透過不斷的迭代修改才能夠獲取高品質的結果。許多研究者已經能夠利用網路系統串連提供者與使用者，取得即時的外部回饋，幫助使用者完成任務。然而，大部分的研究只專注於回饋內容的改進，而忽略了最重要的目的，在於如何在此過程中輔助使用者學習，並有效的完成任務。所以，目的包含了獲得高品質的結果以及提高學習成效。此博士論文提出了一個解決創造性任務的迭代循環回饋的框架，分別探討產生有效且高品質回饋的生成機制以及有效整合回饋輔助編輯的方法，來促進有效的學習行為以及創造良好的使用經驗。我們設計與開發了一系列的智慧型回饋系統，運用群眾與機器合作的力量來幫助使用者快速取得符合需求的高品質寫作建議，並透過結構化的設計，引導他們有效地進行編輯，同時精進專業能力與提升作品品質。未來，在我們提出的互動回饋框架中，機器將擔任協調者與合作者的角色，根據使用者的偏好與行為，提供適當的回饋與引導，促進人與人以及人與機器良好的互動，一起合作完成困難的創造性任務。 | zh_TW |
| dc.description.abstract | Performing creative tasks is challenging, for such tasks are typically open-ended and ill-defined. To solve these complex problems, people need to spend much time and effort to learn professional skills and improve the in-progress work through an iterative process. Feedback is a critical component of this process for helping people discover errors and iterate toward better solutions. To meet the demand of timely feedback, recent work has explored technologies to connect problem solvers with feedback providers online. However, most research focuses on improving the content of feedback, but neglects the most important aspect of how to support problem solvers to learn and effectively facilitate the creation of high-quality outcome. In this dissertation, we explore several ways to support not only feedback generation but also feedback integration process, focusing on the writing tasks. We designed and developed intelligent systems that leverage the power of crowd and machines to support writers for obtaining effective feedback and facilitating good revision behaviors in the process. First, we start with our crowd-powered feedback system, StructFeed, and demonstrate a crowdsourcing approach for generating useful feedback to help writers resolve high-level writing issues in their revisions. Next, we propose feedback orchestration, which guides writers to resolve writing issues in a particular workflow by orchestrating feedback presentation. In the future, we would create an iterative feedback framework that enables collaboration between authors and feedback providers. Authors and feedback providers could benefit from this framework and collaboratively accomplish the complex creative tasks. The system would be regarded as a mediator for generating an ensemble of feedback from diverse feedback providers and for orchestrating feedback presentation to guide authors to achieve high revision performance based on individual needs. | en |
| dc.description.provenance | Made available in DSpace on 2021-05-12T09:34:39Z (GMT). No. of bitstreams: 1 ntu-107-D00944010-1.pdf: 11198594 bytes, checksum: f752acd0db856db46c45c85cc707bb2e (MD5) Previous issue date: 2018 | en |
| dc.description.tableofcontents | 1 Introduction 1
1.1 Background and Motivation 1 1.2 Supporting Creative Task Solving 2 1.3 Roadmap of Research 4 1.3.1 StructFeed: Generate Effective Feedback by Crowdsourcing 4 1.3.2 Feedback Orchestration: Facilitate Effective Revision 5 1.3.3 Understand How Feedback Affects Revision Results 6 1.3.4 Reflection Before/After Practice 7 2 Related Work 9 2.1 Human Computation and Crowdsourcing 9 2.1.1 Incentive, Microtasks, Quality Control 9 2.1.2 Crowdsourcing for complex task 11 2.2 Online Feedback Exchange 11 2.3 Learning Science 13 2.3.1 Self-Regulated Learning 13 2.3.2 Reflection and Reflective Practice 14 2.4 Writing Support Systems 14 2.4.1 Automated Writing Evaluation 15 2.4.2 Crowd-Powered Systems for Writing Support 15 3 StructFeed: Generating Structural Feedback by Crowdsourcing 17 3.1 Introduction 17 3.2 StructFeed 18 3.2.1 Paragraph Unity and Topic Sentence 19 3.2.2 Crowdsourcing Workflow 20 3.2.3 Structural Feedback and Interface 22 3.2.4 Implementation 23 3.3 Unity Identification 23 3.3.1 Crowd-Based Method 24 3.3.2 ML-Based Methods 25 3.3.3 Evaluation 27 3.4 Field Deployment Study 29 3.4.1 Study Design 29 3.4.2 Tasks and Procedure 30 3.4.3 Measure 30 3.4.4 Results 31 3.5 Discussion 32 3.5.1 Crowd helps develop better rules for machine 32 3.5.2 StructFeed not only identifies writing issues but promotes reflection 33 3.5.3 Expert feedback performed worse than crowd feedback? 33 3.6 Conclusion 34 4 Feedback Orchestration: Supporting Reflection and Awareness in Revision 35 4.1 Introduction 35 4.2 Formative Study 38 4.2.1 Task and procedure 38 4.2.2 Findings 39 4.3 Feedback Orchestration 40 4.3.1 Expert revision practice 40 4.3.2 Support reflection and awareness 41 4.4 System Implementation 41 4.4.1 Taxonomy of writing feedback 42 4.4.2 Feedback classification 44 4.4.3 Revision workflow 45 4.4.4 Revision interface 45 4.5 Experiment 48 4.5.1 Participants 48 4.5.2 Task and procedure 49 4.5.3 Measures 51 4.5.4 Results 52 4.5.5 Insights from interviews with participants 53 4.6 Discussion and Implications 58 4.6.1 Feedback categorization guides learning behaviors 58 4.6.2 Flexible support for varying preference 59 4.6.3 Low-to-high sequence facilitates difficult problem solving 60 4.6.4 Decrease the workload of low-level, repetitive tasks 61 4.6.5 Resolving editing conflicts and mental obstacles 61 4.7 Limitations and Future work 62 4.8 Conclusion 63 5 How Feedback Affects Revision Quality? 65 5.1 Introduction 65 5.2 Crowd Feedback vs Expert Feedback 66 5.3 Experiment: Writing Revision Affected by Feedback of Different Types 67 5.3.1 Participants 69 5.3.2 Task and Procedure 69 5.3.3 Measures 70 5.3.4 Statistical Results 72 5.3.5 Insights from Interview Data 73 5.4 Discussion 75 5.4.1 The Cost and Benefit of StructFeed 75 5.4.2 The Utility of StructFeed Depends on Learners’ Level of Proficiency 75 5.4.3 Gap between Expert Reviewer and Novice Writer 76 5.4.4 Macro-Task vs. Micro-Task 76 6 Reflection After/Before Practice 79 6.1 Introduction 79 6.2 Related Work 81 6.3 Learnersourcing for Drawing Support 81 6.4 ShareSketch: Draw, Review and Share 82 6.4.1 Sketch Interface 83 6.4.2 Timeline Interface for Sketch History 83 6.5 Before/After-Practice Reflection Workflow 83 6.6 Pilot Study 85 6.7 Results and Findings 85 6.7.1 After-practice annotation augments before-practice annotation 86 6.7.2 Before-practice reflection vs After-practice reflection 87 6.8 Discussion 87 6.8.1 Provide scaffolding for reflection and practice 88 6.8.2 Learning points as feedback enhances creative task learning 88 6.9 Future Work 88 7 Conclusion 89 7.1 Restatement of Contributions 89 7.2 Future Directions 90 7.2.1 Hybrid Combination of Crowd and Machine 90 7.2.2 Creative Knowledge Construction for Innovative Applications 91 7.3 Summary 91 Bibliography 93 | |
| dc.language.iso | en | |
| dc.subject | 解決創造性任務 | zh_TW |
| dc.subject | 創造性學習 | zh_TW |
| dc.subject | 群眾外包 | zh_TW |
| dc.subject | 群眾回饋 | zh_TW |
| dc.subject | 線上回饋交換 | zh_TW |
| dc.subject | crowd feedback | en |
| dc.subject | crowdsourcing | en |
| dc.subject | creative learning | en |
| dc.subject | online feedback exchange | en |
| dc.subject | creative task solving | en |
| dc.title | 設計解決複雜的創造性任務 | zh_TW |
| dc.title | Designing for Complex Creative Task Solving | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 106-2 | |
| dc.description.degree | 博士 | |
| dc.contributor.oralexamcommittee | 王浩全,陳炳宇,張俊盛,梁容輝,何建儒 | |
| dc.subject.keyword | 解決創造性任務,線上回饋交換,創造性學習,群眾外包,群眾回饋, | zh_TW |
| dc.subject.keyword | creative task solving,online feedback exchange,creative learning,crowdsourcing,crowd feedback, | en |
| dc.relation.page | 101 | |
| dc.identifier.doi | 10.6342/NTU201800935 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2018-06-12 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊網路與多媒體研究所 | zh_TW |
| 顯示於系所單位： | 資訊網路與多媒體研究所 | |
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| ntu-107-1.pdf | 10.94 MB | Adobe PDF | 檢視/開啟 |
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