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題名 基於眼動與滑鼠追蹤之互動式資料視覺化評估
Evaluation of interactive data visualization tools based on gaze and mouse tracking
作者 彭久芳
Peng, Chiu-Fang
貢獻者 廖文宏<br>陳百齡
Liao, Wen-Hung<br>Chen, Pai-Lin
彭久芳
Peng, Chiu-Fang
關鍵詞 互動式資料視覺化
眼動追蹤
滑鼠追蹤
量化使用者評估
Interactive data visualization
Gaze tracking
Mouse tracking
Quantitative usability evaluation
日期 2017
上傳時間 5-Apr-2017 15:41:54 (UTC+8)
摘要 隨著互動式資料視覺化工具越來越多，設計者需要一個方法來衡量其作品是否好用、能否被理解、使用效率高低。互動式資料視覺化需要透過使用者的互動才能觀察到資料的不同面向，再進一步產生洞見，然而現有的評估方式多僅聚焦於靜態資料圖表，設計者無法從中得知使用者的操作困難之處，並據此進行加強與改善，因此本研究提出一個整合量化分析與質化記錄的系統性評估方式，應用於互動式資料視覺化的優使性(usability)分析。

本研究的方法為追蹤使用者的眼動和滑鼠操作過程，先將其記錄成量化數據，透過興趣區域的標定與將轉換使用者行為成序列後，進行序列運算和統計分析；同時，從使用者經驗研究方法得到實驗過程的質化記錄，用來輔助解釋量化分析的結果。

本論文藉由兩個互動式資料視覺化工具來驗證以眼動與滑鼠追蹤評估互動式資料視覺化是可行的，我們提出了具體的實驗流程、量化紀錄與分析方式，並建議以下評估指標：吸引力、易發現性、困難度、易識別性、易理解性、精準表達程度、細部困難度、使用效率。
As more and more interactive data visualization tools emerge, designers need an organized evaluation method to provide timely feedback and understand user behavior. In contrast to traditional graphical presentations, interactive data visualization tools call for user manipulation to gain specific insights. It is therefore imperative to study the intermediate operation process, rather than the final outcome, to provide a critical understanding of the developed tool. Toward this objective, we propose a systematic approach combining quantitative analysis and qualitative assessment to gauge the usability of interactive data visualization tools in this research.

Firstly, quantitative data including gaze and mouse movements are collected. By combining the definition of area of interest, these trajectories can be converted into user sequences, which are conveniently accessible for further statistical analysis as well as path comparison. Secondly, qualitative information obtained by observing user operation is gathered to offer additional insight and complement/support conclusions obtained from quantitative analysis.

Two interactive data visualization tools are employed to examine the feasibility and universality of our experimental and analytical procedure. To conclude, we come up with several key indicators to evaluate interactive data visualization, including attraction, discoverability, difficulty, identifiability, comprehensibility, precision of expression, difficulty(detailed) and efficiency.
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[20] James D Kelly. The data-ink ratio and accuracy of newspaper graphs. Journalism and Mass Communication Quarterly, 66(3):632, 1989.
[21] Julia Kulla-Mader. Graphs via ink: Understanding how the amount of non-data-ink in a graph affects perception and learning. Master’s Thesis, Department of Information and Library Science, University of North Carolina, 2007.
[22] Michael Burch, Natalia Konevtsova, Julian Heinrich, Markus Hoeferlin, and Daniel Weiskopf. Evaluation of traditional, orthogonal, and radial tree diagrams by an eye tracking study. IEEE Transactions on Visualization and Computer Graphics, 17(12):2440–2448, 2011.
[23] Dereck Toker, Ben Steichen, Matthew Gingerich, Cristina Conati, and Giuseppe Carenini. Towards facilitating user skill acquisition: identifying untrained visualization users through eye tracking. In Proceedings of the 19th international conference on Intelligent User Interfaces, pages 105–114. ACM, 2014.
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描述 碩士
國立政治大學
數位內容碩士學位學程
103462004
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0103462004
資料類型 thesis
dc.contributor.advisor 廖文宏<br>陳百齡zh_TW
dc.contributor.advisor Liao, Wen-Hung<br>Chen, Pai-Linen_US
dc.contributor.author (Authors) 彭久芳zh_TW
dc.contributor.author (Authors) Peng, Chiu-Fangen_US
dc.creator (作者) 彭久芳zh_TW
dc.creator (作者) Peng, Chiu-Fangen_US
dc.date (日期) 2017en_US
dc.date.accessioned 5-Apr-2017 15:41:54 (UTC+8)-
dc.date.available 5-Apr-2017 15:41:54 (UTC+8)-
dc.date.issued (上傳時間) 5-Apr-2017 15:41:54 (UTC+8)-
dc.identifier (Other Identifiers) G0103462004en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/108142-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 數位內容碩士學位學程zh_TW
dc.description (描述) 103462004zh_TW
dc.description.abstract (摘要) 隨著互動式資料視覺化工具越來越多，設計者需要一個方法來衡量其作品是否好用、能否被理解、使用效率高低。互動式資料視覺化需要透過使用者的互動才能觀察到資料的不同面向，再進一步產生洞見，然而現有的評估方式多僅聚焦於靜態資料圖表，設計者無法從中得知使用者的操作困難之處，並據此進行加強與改善，因此本研究提出一個整合量化分析與質化記錄的系統性評估方式，應用於互動式資料視覺化的優使性(usability)分析。

本研究的方法為追蹤使用者的眼動和滑鼠操作過程，先將其記錄成量化數據，透過興趣區域的標定與將轉換使用者行為成序列後，進行序列運算和統計分析；同時，從使用者經驗研究方法得到實驗過程的質化記錄，用來輔助解釋量化分析的結果。

本論文藉由兩個互動式資料視覺化工具來驗證以眼動與滑鼠追蹤評估互動式資料視覺化是可行的，我們提出了具體的實驗流程、量化紀錄與分析方式，並建議以下評估指標：吸引力、易發現性、困難度、易識別性、易理解性、精準表達程度、細部困難度、使用效率。		zh_TW
dc.description.abstract (摘要) As more and more interactive data visualization tools emerge, designers need an organized evaluation method to provide timely feedback and understand user behavior. In contrast to traditional graphical presentations, interactive data visualization tools call for user manipulation to gain specific insights. It is therefore imperative to study the intermediate operation process, rather than the final outcome, to provide a critical understanding of the developed tool. Toward this objective, we propose a systematic approach combining quantitative analysis and qualitative assessment to gauge the usability of interactive data visualization tools in this research.

Firstly, quantitative data including gaze and mouse movements are collected. By combining the definition of area of interest, these trajectories can be converted into user sequences, which are conveniently accessible for further statistical analysis as well as path comparison. Secondly, qualitative information obtained by observing user operation is gathered to offer additional insight and complement/support conclusions obtained from quantitative analysis.

Two interactive data visualization tools are employed to examine the feasibility and universality of our experimental and analytical procedure. To conclude, we come up with several key indicators to evaluate interactive data visualization, including attraction, discoverability, difficulty, identifiability, comprehensibility, precision of expression, difficulty(detailed) and efficiency.		en_US
dc.description.tableofcontents 摘要 ii
Abstract iii
目錄 iv
表目錄 vii
圖目錄 viii
第一章緒論 1
1.1 研究背景與動機 1
1.2 研究問題 3
1.3 研究流程與方法 5
1.4 論文架構 5
第二章文獻探討 6
2.1 資料視覺化評估方法 6
2.1.1、問卷、任務 6
2.1.2、資料墨色比率 7
2.2 電腦操作量化評估方法 9
2.2.1、眼動追蹤分析 9
2.2.2、滑鼠追蹤分析 11
2.2.3、眼動與滑鼠追蹤綜合分析 13
2.3 小結 16
第三章研究方法 18
3.1 任務設計方法 18
3.2 資料蒐集方法 19
3.2.1、眼動儀工具介紹 19
3.2.2、OGAMA 工具介紹 23
3.2.3、放聲思考法 25
3.2.4、觀察法 26
3.2.5、Card Sort 27
3.3 資料分析方法 28
3.3.1、眼動與滑鼠追蹤資料分析 28
3.3.2、Area Of Interest 29
3.3.3、使用者操作步驟序列 31
3.3.4、Edit Distance 31
3.3.5、Fitts’ Law 33
3.3.6、可容許序列 35
第四章實驗設計與前期研究 37
4.1 研究特性 37
4.2 實驗設計 37
4.2.1、實驗方式與環境設定 37
4.2.2、實驗流程 39
4.3 評估指標 42
4.4 實驗資料 43
4.4.1、ShareFlow 43
4.4.2、TopicWave 45
4.5 任務設計與修正 45
4.5.1、任務設計 45
4.5.2、修正任務 48
4.6 前期研究 48
4.6.1、實驗對象 48
4.6.2、眼動與滑鼠位置的相關性 49
4.6.3、評估指標測試 50
4.6.4、質化結果 54
4.7 實驗方法修正 56
第五章實驗結果與討論 60
5.1 實驗對象 60
5.2 排除異常值61
5.3 評估指標驗證61
5.3.1、吸引力 62
5.3.2、易發現性 62
5.3.3、困難度 64
5.3.4、易識別性 65
5.3.5、易理解性 68
5.3.6、精準表達程度 70
5.3.7、細部困難度 72
5.3.8、使用效率 76
5.3.9、評估指標小結 79
5.4 研究限制 79
5.4.1、實驗環境與時間限制 79
5.4.2、實驗對象限制 80
5.4.3、實驗資料限制 81
第六章結論與未來展望 82
參考文獻 84
附錄一　實驗指示 90
附錄二　前期研究任務與實驗記錄 92
附錄三　正式實驗任務與實驗記錄 94		zh_TW
dc.format.extent 35092562 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0103462004en_US
dc.subject (關鍵詞) 互動式資料視覺化zh_TW
dc.subject (關鍵詞) 眼動追蹤zh_TW
dc.subject (關鍵詞) 滑鼠追蹤zh_TW
dc.subject (關鍵詞) 量化使用者評估zh_TW
dc.subject (關鍵詞) Interactive data visualizationen_US
dc.subject (關鍵詞) Gaze trackingen_US
dc.subject (關鍵詞) Mouse trackingen_US
dc.subject (關鍵詞) Quantitative usability evaluationen_US
dc.title (題名) 基於眼動與滑鼠追蹤之互動式資料視覺化評估zh_TW
dc.title (題名) Evaluation of interactive data visualization tools based on gaze and mouse trackingen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) [1] 陳為，沈則潛，陶煜波（民103），「視覺化資料──100% 全腦吸收大數據，直入神經元」，佳魁資訊，台北市。
[2] 劉自強，黃芝瑩（譯）（民102）。「網頁互動式資料視覺化：使用D3」（原作者：Scott Murray）。台北市：歐萊禮。（原著出版年：102）。
[3] Information visualization. In Wikipedia, The Free Encyclopedia. Last visited on 2/4/2016.
[4] Infographic. In Wikipedia, The Free Encyclopedia. Last visited on 14:48 2/4/2016.
[5] Data visualization. In Wikipedia, The Free Encyclopedia. Last visited on 14:50 2/4/2016.
[6] 康仕仲（民105），資料視覺化之理論、賞析與實作。取自https://goo.gl/cWdUu0。
[7] Data-driven documents. Last visited on 24/3/2016.
[8] Tableau. Last visited on 24/3s/2016.
[9] Scott Bateman, Regan L Mandryk, Carl Gutwin, Aaron Genest, David McDine, and Christopher Brooks. Useful junk?: the effects of visual embellishment on comprehension and memorability of charts. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pages 2573–2582. ACM, 2010.
[10] Marcel Adam Just and Patricia A Carpenter. Using eye fixations to study reading comprehension. New methods in reading comprehension research, pages 151–182, 1984.
[11] Martijn Tennekes and Edwin de Jonge. Tree colors: color schemes for tree-structured data. IEEE transactions on visualization and computer graphics, 20(12):2072–2081, 2014.
[12] Fernanda B Viegas, Martin Wattenberg, and Jonathan Feinberg. Participatory visualization with wordle. IEEE transactions on visualization and computer graphics, 15(6):1137–1144,2009.
[13] Bongshin Lee, Nathalie Henry Riche, Amy K Karlson, and Sheelash Carpendale. Sparkclouds: Visualizing trends in tag clouds. IEEE transactions on visualization and computer graphics, 16(6):1182–1189, 2010.
[14] Anna W Rivadeneira, Daniel M Gruen, Michael J Muller, and David R Millen. Getting our head in the clouds: toward evaluation studies of tagclouds. In Proceedings of the SIGCHI conference on Human factors in computing systems, pages 995–998. ACM, 2007.
[15] Catherine Plaisant. The challenge of information visualization evaluation. In Proceedings of the working conference on Advanced visual interfaces, pages 109–116. ACM, 2004.
[16] Edward R Tufte and PR Graves-Morris. The visual display of quantitative information, volume 2. Graphics press Cheshire, CT, 1983.
[17] Data looks better naked, 2013.
[18] S Few and P Edge. Sometimes we must raise our voices. 2009.
[19] Ohad Inbar, Noam Tractinsky, and Joachim Meyer. Minimalism in information visualization: attitudes towards maximizing the data-ink ratio. In Proceedings of the 14th European conference on Cognitive ergonomics: invent! explore!, pages 185–188. ACM, 2007.
[20] James D Kelly. The data-ink ratio and accuracy of newspaper graphs. Journalism and Mass Communication Quarterly, 66(3):632, 1989.
[21] Julia Kulla-Mader. Graphs via ink: Understanding how the amount of non-data-ink in a graph affects perception and learning. Master’s Thesis, Department of Information and Library Science, University of North Carolina, 2007.
[22] Michael Burch, Natalia Konevtsova, Julian Heinrich, Markus Hoeferlin, and Daniel Weiskopf. Evaluation of traditional, orthogonal, and radial tree diagrams by an eye tracking study. IEEE Transactions on Visualization and Computer Graphics, 17(12):2440–2448, 2011.
[23] Dereck Toker, Ben Steichen, Matthew Gingerich, Cristina Conati, and Giuseppe Carenini. Towards facilitating user skill acquisition: identifying untrained visualization users through eye tracking. In Proceedings of the 19th international conference on Intelligent User Interfaces, pages 105–114. ACM, 2014.
[24] Joseph H Goldberg and Jonathan I Helfman. Comparing information graphics: a critical look at eye tracking. In Proceedings of the 3rd BELIV’10 Workshop: Beyond time and errors: Novel evaluation methods for information visualization, pages 71–78. ACM, 2010.
[25] Joseph H Goldberg and Anna M Wichansky. Eye tracking in usability evaluation: A practitioner’s guide. To appear in: Hyönä, 2002.
[26] Eye movement in reading. In Wikipedia, The Free Encyclopedia. Last visited on 13:06 31/3/2016.
[27] Vidhya Navalpakkam and Elizabeth Churchill. Mouse tracking: measuring and predicting users’ experience of web-based content. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pages 2963–2972. ACM, 2012.
[28] Ernesto Arroyo, Ted Selker, and Willy Wei. Usability tool for analysis of web designs using mouse tracks. In CHI’06 extended abstracts on Human factors in computing systems, pages 484–489. ACM, 2006.
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