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Title: 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
Authors: 彭久芳
Peng, Chiu-Fang
Contributors: 廖文宏

Liao, Wen-Hung
Chen, Pai-Lin

Peng, Chiu-Fang
Keywords: 互動式資料視覺化
Interactive data visualization
Gaze tracking
Mouse tracking
Quantitative usability evaluation
Date: 2017
Issue Date: 2017-04-05 15:41:54 (UTC+8)
Abstract: 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。


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.
Reference: [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。
[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.
[29] Eric Hehman, Ryan M Stolier, and Jonathan B Freeman. Advanced mouse-tracking analytic techniques for enhancing psychological science. Group Processes & Intergroup Relations, 18(3):384–401, 2015.
[30] Hotjar. Last visited on 11/7/2016.
[31] Jens Gerken, Peter Bak, Christian Jetter, Daniel Klinkhammer, and Harald Reiterer. How to use interaction logs effectively for usability evaluation. In BELIV, 2008.
[32] Kerry Rodden and Xin Fu. Exploring how mouse movements relate to eye movements on web search results pages. Web Information Seeking and Interaction, pages 29–32, 2007.
[33] Kerry Rodden, Xin Fu, Anne Aula, and Ian Spiro. Eye-mouse coordination patterns on web search results pages. In CHI’08 extended abstracts on Human factors in computing systems, pages 2997–3002. ACM, 2008.
[34] Vidhya Navalpakkam, LaDawn Jentzsch, Rory Sayres, Sujith Ravi, Amr Ahmed, and Alex Smola. Measurement and modeling of eye-mouse behavior in the presence of nonlinear page layouts. In Proceedings of the 22nd international conference on World Wide Web, pages 953–964. ACM, 2013.
[35] Jing Yang, Matthew O Ward, and Elke A Rundensteiner. Interring: An interactive tool for visually navigating and manipulating hierarchical structures. In Information Visualization, 2002. INFOVIS 2002. IEEE Symposium On, pages 77–84. IEEE, 2002.
[36] Brian D Ondov, Nicholas H Bergman, and Adam M Phillippy. Interactive metagenomics visualization in a web browser. BMC bioinformatics, 12(1):1, 2011.
[37] Robert Amar, James Eagan, and John Stasko. Low-level components of analytic activity in information visualization. In IEEE Symposium on Information Visualization, 2005. INFOVIS 2005, pages 111–117. IEEE, 2005.
[38] 韓承靜、蔡介立(民97)。眼球軌跡記錄—科學學習研究的明日之星。科學教育,310,2-11。
[39] The eye tribe products. In The Eye Tribe. Last visited on 1/4/2016.
[40] Stanislav Popelka, Zdeněk Stachoň, Čeněk Šašinka, and Jitka Doležalová. Eyetribe tracker data accuracy evaluation and its interconnection with hypothesis software for cartographic purposes. Computational intelligence and neuroscience, 2016.
[41] Ogama, open gaze and mouse analyzer. In OGAMA. Last visited on 2/4/2016.
[42] Adrian Voßkühler. Ogama description (for version 2.5). Berlin, Germany: Freie Universität Berlin, Fachbereich Physik, 2009.
[43] Thinking aloud: The #1 usability tool. Last visited on 7/7/2016.
[44] Talking out loud is not the same as thinking aloud. Last visited on 7/7/2016.
[45] 民族誌。擷取自維基百科,自由的百科全書:
[46] Ethnography in ux. In UX matters. Last visited on 7/7/2016.
[47] Ben Shneiderman and Catherine Plaisant. Strategies for evaluating information visualization tools: multi-dimensional in-depth long-term case studies. In Proceedings of the 2006 AVI workshop on BEyond time and errors: novel evaluation methods for information visualization, pages 1–7. ACM, 2006.
[48] The field guide to human-centered design.
[49] Card sorting. In information and design. Last visited on 7/7/2016.
[50] Robert A Wagner and Michael J Fischer. The string-to-string correction problem. Journal of the ACM (JACM), 21(1):168–173, 1974.
[51] Paul M Fitts. The information capacity of the human motor system in controlling the amplitude of movement. Journal of experimental psychology, 47(6):381, 1954.
[52] Stuart K Card, William K English, and Betty J Burr. Evaluation of mouse, rate-controlled isometric joystick, step keys, and text keys for text selection on a crt. Ergonomics, 21(8): 601–613, 1978.
[53] I Scott MacKenzie. Movement time prediction in human-computer interfaces. In Proceedings of Graphics Interface, volume 92, page 1, 1992.
[54] 魏浩翔(民104)。分享脈絡:社群媒體訊息散播行為視覺化。國立政治大學,資訊科學學系,台北市。
[55] 熊凱文(民104)。基於堆疊圖方式之社群媒體階層式議題的視覺化探索架構。國立政治大學,資訊科學學系,台北市。
[56] Jeff Sauro and James R Lewis. Quantifying the user experience: Practical statistics for user research. Elsevier, 2012.
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