政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/146705

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/146705

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | 全文笔数/总笔数 : 113318/144297 (79%)
造访人次 : 50986417 在线人数 : 380

RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

搜寻范围

查询小技巧：

您可在西文检索词汇前后加上"双引号"，以获取较精准的检索结果

若欲以作者姓名搜寻，建议至进阶搜寻限定作者字段，可获得较完整数据

进阶搜寻

主页 ‧ 登入 ‧ 上传 ‧ 说明 ‧ 关于政大典藏 ‧ 管理

到手机版

政大機構典藏 > 資訊學院 > 資訊科學系 > 學位論文 > Item 140.119/146705

请使用永久网址来引用或连结此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/146705

题名:	社群媒體和學習過程中互動事件的視覺化分析 Visual Analytics of Interactive Events in Social Media and Learning Process
作者:	胡臻騏 Hu, Chen-Chi
贡献者:	紀明德 Chi, Ming-Te 胡臻騏 Hu, Chen-Chi
关键词:	視覺化分析社群媒體三維建模強化學習互動事件學習互動使用者互動階層視覺化時變視覺化 Visual Analytics Social Media 3D Modeling Reinforcement Learning Interactive Events Learning Interactions User Interactions Hierarchical Visualization Time-varying Visualization
日期:	2023
上传时间:	2023-08-02 14:36:04 (UTC+8)
摘要:	近年來，視覺化分析技術迅速興起並快速發展，巨量資料（例如社群媒體、三維建模和強化學習等）以及複雜的資料特性，如階層性和時變性等，將多個視覺化圖形整合起來觀察關鍵互動事件的形成，並從中獲得獨到的見解變得至關重要。然而，在收集和分析這些關鍵資料方面，資料的迅速累積帶來視覺化分析上的挑戰。為了解決這個問題，我們提出了將時變資料的特性和分群演算法整合到多視圖視覺化工具中，以有效地分析巨量資料。透過觀察時變資料的特性，我們可以辨別關鍵資料或將巨量資料分群成小型資料集，從中找出關鍵互動事件。透過整合多視圖視覺化分析這些關鍵資料，專家可以更好地理解和解讀資料。考慮到數位與虛擬環境產生的巨量資料具有廣泛的來源，本研究將探索和分析巨量資料的兩個特定領域：使用者互動的社群媒體視覺化分析和學習互動的數位與虛擬環境視覺化分析。透過這些研究，我們希望提供更深入的遠見和知識，並為相關領域的專家和研究人員提供有價值的工具和方法，從而推動視覺化分析在這些領域的應用和發展。在社群媒體領域產生的巨量資料主要來自於使用者之間的互動訊息。這些資料的關鍵因素可透過資料的時變性、階層性和訊息傳播路徑等特性來探索關鍵使用者（意見領袖）和熱門議題（評論趨勢）。因此，本論文的首要目標是引用並改良時變視覺化、階層視覺化、集合視覺化和符號視覺化等視覺化方法，以整合多視圖分析出關鍵資料，供專家判讀和佐證。我們的研究結果證實，一般使用者甚至傳播學者皆可透過此視覺化分析工具，在社群媒體中探索關鍵的意見領袖和時事議題。然而，在數位與虛擬環境中，數位學習互動和強化學習互動所產生的資料量也相當龐大和多樣化。我們分別從視覺化方法分析這兩種數位與虛擬環境中的巨量資料。首先，在數位學習的視覺化分析研究中，我們電腦圖學實驗室開發了一個能將二維輪廓轉換為三維模型的三維建模工具和使用可拆卸組件的教學案例。我們還基於遊戲設計了三維建模學習模組，並通過使用者學習操作這些數位教材來收集了大量的資料。由於數位學習資料的規模和複雜性，我們在本論文中引用和試驗分群演算法，將巨量資料分群成小型資料集，並結合改良的時變視覺化和符號視覺化等視覺化方法，以多視圖方式分析關鍵資料，從而深入理解學習表現，探索和分析學習互動，並找出影響學習表現的關鍵步驟和整合各群資料集的學習模式，進一步讓教學端調整教案策略和學習端調整學習步驟。此外，在模擬無人機避開障礙物的強化學習環境中，我們使用摺疊維度的方法，結合改良時變視覺化方法。從多視圖中探索強化學習的參數變化和學習飛行中的互動事件，並找出無人機學習避開障礙物的關鍵參數。在本論文中，我們提出了利用時變資料的特性或分群演算法整合於多視圖視覺化工具為解決方案分析巨量資料。我們的研究結果證實，使用者可透過此視覺化分析工具，在數位與虛擬環境（例如社群媒體、三維建模和強化學習等）中探索並找出關鍵資料。本研究以視覺化方法加速分析巨量資料中兩個特定領域，並從互動行為中探索獨到的創見和學習模式。 In recent years, visual analysis technology has emerged and developed rapidly. Social media, digital and reinforcement learning accumulate a lot of data, and complex features, such as hierarchy and time-varying, which visually analysis and integrate into multiple-view visualizations. The multi-view becomes crucial to stand up, watch the key interactive events take shape, and gain unique insights from them. However, in terms of collecting and analyzing these key data, the rapid accumulation of data brings challenges to visual analytics. To address this problem, we propose integrating time-varying data properties and clustering algorithms into a multi-view visualization tool to analyze domain data efficiently. By observing the characteristics of time-varying data, we can identify critical data or cluster huge data into small data sets to find critical data. Experts can better understand and interpret the data by integrating these critical data with multi-view visualizations. Considering that the huge data generated by the digital and virtual environment has a wide range of sources, this study will explore and analyze two specific domains of data: visual analytics of user interactions in social media and learning interactions in digital and virtual environments. Through these studies, we provide insight, knowledge, and valuable tools and methods to experts and researchers in related fields, thereby promoting the application and development of visual analysis in these fields. In the domain of social media, massive data is primarily derived from user interactions and messages. The key factors in this data can be explored through characteristics such as time-varying, hierarchical relationships, and information propagation paths, enabling the identification of key users (opinion leaders) and popular issues (comment trends). Hence, the primary objective of this dissertation is to employ and enhance visualizations methods such as time-varying visualizations, hierarchical visualizations, set visualizations, and glyph visualizations to integrate multiple views and analyze key data for expert interpretation and validation. Our research results demonstrate that both ordinary users and communication experts can explore key opinion leaders and current issues in social media using this visual analysis tool. Furthermore, in digital and virtual environments, the volume and diversity of data generated from digital learning interactions and reinforcement learning interactions are also substantial. We analyze these massive data sets using different visualization methods. In the context of digital learning, our computer graphics laboratory has developed a 3D modeling tool capable of converting 2D contours into 3D models and instructional cases that utilize detachable components. We have also designed a game-based 3D modeling learning module and collected significant data, allowing users to interact with these digital materials. Due to the scale and complexity of digital learning data, we employ clustering algorithms to partition the massive data into smaller data sets and combine improved time-varying visualizations and glyph visualizations. These visualizations enable the multi-view analysis of key data, facilitating a deep comprehension of learning performance, exploration and analysis of learning interactions, and identification of crucial steps and learning patterns across different data clusters. In this way, the instructors allow for adjustments in teaching strategies, motivating learners to alter the learning steps. Additionally, we employ the method of dimensionality folding and improved time-varying visualizations. Explore the tuning parameters and interact with autonomous flight learning from a multiple-view approach to identify critical parameters for unmanned drone learning in obstacle avoidance. In this dissertation, we propose the integration of the characteristics of time-varying data or clustering algorithms into multi-view visualization tools as a solution for analyzing specific data to corresponding domains. Our research findings demonstrate that this visual analysis tool allows users to explore and identify key data in digital and virtual environments such as social media, 3D modeling, and machine learning. This study accelerates domain data analysis in two specific domains using visualization methods and explores unique insights and learning patterns from interactive behaviors.
參考文獻:	Bibliography [1] Bilal Alsallakh, Luana Micallef, Wolfgang Aigner, Helwig Hauser, Silvia Miksch, and Peter Rodgers. Visualizing sets and set-typed data: State-of-the-art and future challenges. In Eurographics conference on visualization, 2014. [2] Bilal Alsallakh, Luana Micallef, Wolfgang Aigner, Helwig Hauser, Silvia Miksch, and Peter Rodgers. The state-of-the-art of set visualization. In Computer Graphics Forum, volume 35, pages 234–260. Wiley Online Library, 2016. [3] Bilal Alsallakh and Liu Ren. Powerset: A comprehensive visualization of set intersections. IEEE Transactions on Visualization and Computer Graphics, 23(1):361–370, 2016. [4] Erik Andersen, Yun-En Liu, Ethan Apter, François Boucher-Genesse, and Zoran Popović. Gameplay analysis through state projection. In Proceedings of the fifth international conference on the foundations of digital games, pages 1–8, 2010. [5] Natalia Andrienko and Gennady Andrienko. Visual analytics of movement: An overview of methods, tools and procedures. Information visualization, 12(1):3–24, 2013. [6] Benjamin Bach, Pierre Dragicevic, Daniel Archambault, Christophe Hurter, and Sheelagh Carpendale. A review of temporal data visualizations based on space-time cube operations. In Eurographics conference on visualization, 2014. [7] Tanja Blascheck, Lonni Besançon, Anastasia Bezerianos, Bongshin Lee, and Petra Isenberg. Glanceable visualization: Studies of data comparison performance on smartwatches. IEEE Transactions on Visualization and Computer Graphics, 25(1):630–640, 2018. [8] Richard Boardman. Bubble trees the visualization of hierarchical information structures. In CHI’00 extended abstracts on Human factors in computing systems, pages 315–316, 2000. [9] Rita Borgo, Johannes Kehrer, David HS Chung, Eamonn Maguire, Robert S Laramee, Helwig Hauser, Matthew Ward, and Min Chen. Glyph-based visualization: Foundations, design guidelines, techniques and applications. In Eurographics (State of the Art Reports), pages 39–63, 2013. [10] Brian Bowman, Niklas Elmqvist, and TJ Jankun-Kelly. Toward visualization for games: Theory, design space, and patterns. IEEE Transactions on Visualization and Computer Graphics, 18(11):1956–1968, 2012. [11] Juri Buchmüller, Dominik Jäckle, Eren Cakmak, Ulrik Brandes, and Daniel A Keim. Motionrugs: Visualizing collective trends in space and time. IEEE Transactions on Visualization and Computer Graphics, 25(1):76–86, 2018. [12] Juri Buchmüller, Halldór Janetzko, Gennady Andrienko, Natalia Andrienko, Georg Fuchs, and Daniel A Keim. Visual analytics for exploring local impact of air traffic. In Computer Graphics Forum, volume 34, pages 181–190. Wiley Online Library, 2015. [13] Lee Byron and Martin Wattenberg. Stacked graphs–geometry & aesthetics. IEEE Transactions on Visualization and Computer Graphics, 14(6):1245–1252, 2008. [14] Adam S Carter, Christopher D Hundhausen, and Olusola Adesope. Blending measures of programming and social behavior into predictive models of student achievement in early computing courses. ACM Transactions on Computing Education (TOCE), 17(3):1–20, 2017. [15] Michelangelo Ceci, Michele Spagnoletta, Pasqua Fabiana Lanotte, and Donato Malerba. Distributed learning of process models for next activity prediction. In Proceedings of the 22nd International Database Engineering & Applications Symposium, pages 278–282, 2018. [16] Chun-houh Chen, Wolfgang Härdle, Antony Unwin, and Matthew O Ward. Multivariate data glyphs: Principles and practice. Handbook of data visualization, pages 179–198, 2008. [17] Siming Chen, Shuai Chen, Lijing Lin, Xiaoru Yuan, Jie Liang, and Xiaolong Zhang. E-map: A visual analytics approach for exploring significant event evolutions in social media. In 2017 IEEE Conference on Visual Analytics Science and Technology (VAST), pages 36–47. IEEE, 2017. [18] Siming Chen, Shuai Chen, Zhenhuang Wang, Jie Liang, Xiaoru Yuan, Nan Cao, and Yadong Wu. D-map: Visual analysis of ego-centric information diffusion patterns in social media. In 2016 IEEE Conference on Visual Analytics Science and Technology (VAST), pages 41–50. IEEE, 2016. [19] Weiwei Cui, Shixia Liu, Zhuofeng Wu, and Hao Wei. How hierarchical topics evolve in large text corpora. IEEE Transactions on Visualization and Computer Graphics, 20(12):2281–2290, 2014. [20] Weiwei Cui, Hong Zhou, Huamin Qu, Pak Chung Wong, and Xiaoming Li. Geometrybased edge clustering for graph visualization. IEEE Transactions on Visualization and Computer Graphics, 14(6):1277–1284, 2008. [21] Wenwen Dou, Li Yu, Xiaoyu Wang, Zhiqiang Ma, and William Ribarsky. Hierarchicaltopics: Visually exploring large text collections using topic hierarchies. IEEE Transactions on Visualization and Computer Graphics, 19(12):2002–2011, 2013. [22] D Dougherty. The maker movement. innovations: Technology, governance, globalization, 7 (3), 11–14, 2012. [23] Brandon Drenikow and Pejman Mirza-Babaei. Vixen: interactive visualization of gameplay experiences. In Proceedings of the 12th International Conference on the Foundations of Digital Games, pages 1–10, 2017. [24] Thomas MJ Fruchterman and Edward M Reingold. Graph drawing by force-directed placement. Software: Practice and experience, 21(11):1129–1164, 1991. [25] Qian Fu, Xue Bai, Yafeng Zheng, Runsheng Du, Dongqing Wang, and Tianyi Zhang. Visoj: real-time visual learning analytics dashboard for online programming judge. The Visual Computer, 39(6):2393–2405, 2023. [26] Johannes Fuchs, Fabian Fischer, Florian Mansmann, Enrico Bertini, and Petra Isenberg. Evaluation of alternative glyph designs for time series data in a small multiple setting. In Proceedings of the SIGCHI conference on human factors in computing systems, pages 3237–3246, 2013. [27] Johannes Fuchs, Petra Isenberg, Anastasia Bezerianos, Fabian Fischer, and Enrico Bertini. The influence of contour on similarity perception of star glyphs. IEEE Transactions on Visualization and Computer Graphics, 20(12):2251–2260, 2014. [28] Johannes Fuchs, Petra Isenberg, Anastasia Bezerianos, and Daniel Keim. A systematic review of experimental studies on data glyphs. IEEE Transactions on Visualization and Computer Graphics, 23(7):1863–1879, 2016. [29] Emden R Gansner, Yifan Hu, Stephen North, and Carlos Scheidegger. Multilevel agglomerative edge bundling for visualizing large graphs. In 2011 IEEE Pacific Visualization Symposium, pages 187–194. IEEE, 2011. [30] Supriya Garg, IV Ramakrishnan, and Klaus Mueller. A visual analytics approach to model learning. In 2010 IEEE Symposium on Visual Analytics Science and Technology, pages 67–74. IEEE, 2010. [31] Michael Gleicher. Considerations for visualizing comparison. IEEE Transactions on Visualization and Computer Graphics, 24(1):413–423, 2017. [32] Lilin Gong and Yang Liu. Design and application of intervention model based on learning analytics under blended learning environment. In Proceedings of the 2019 7th International Conference on Information and Education Technology, pages 225–229, 2019. [33] Jochen Görtler, Christoph Schulz, Daniel Weiskopf, and Oliver Deussen. Bubble treemaps for uncertainty visualization. IEEE Transactions on Visualization and Computer Graphics, 24(1):719–728, 2017. [34] Adrien Guille, Hakim Hacid, Cecile Favre, and Djamel A Zighed. Information diffusion in online social networks: A survey. ACM Sigmod Record, 42(2):17–28, 2013. [35] Erik Harpstead, Brad A Myers, and Vincent Aleven. In search of learning: facilitating data analysis in educational games. In Proceedings of the SIGCHI conference on human factors in computing systems, pages 79–88, 2013. [36] Chien-Tung Ho, Cheng-Te Li, and Shou-De Lin. Modeling and visualizing information propagation in a micro-blogging platform. In 2011 International Conference on Advances in Social Networks Analysis and Mining, pages 328–335. IEEE, 2011. [37] Danny Holten and Jarke J Van Wijk. Force-directed edge bundling for graph visualization. In Computer graphics forum, volume 28, pages 983–990. Wiley Online Library, 2009. [38] Chen-Chi Hu and Ming-Te Chi. A detachable mortise-tenon structure in 3d cubic style modeling system. In SIGGRAPH ASIA 2016 Posters, pages 1–2, 2016. [39] Chen-Chi Hu and Ming-Te Chi. Visual analytics of autonomous drone trajectories with dimensionality folding. In 2021 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), pages 1–2. IEEE, 2021. [40] Chen-Chi Hu, Ming-Te Chi, and Teng-Kai Chang. Design and evaluation of game-based learning module for 3d modeling. In 2018 IEEE 18th International Conference on Advanced Learning Technologies (ICALT), pages 128–132. IEEE, 2018. [41] Chen-Chi Hu, Chun-Min Liao, and Ming-Te Chi. Visual exploration framework of intersecting users and activities between issues in social media. Accepted in Journal of Information Science & Engineering, ?(?), 2023. [42] Chen-Chi Hu, Hao-Xiang Wei, and Ming-Te Chi. Shareflow: A visualization tool for information diffusion in social media. In Proceedings of the 13th International Conference on Ubiquitous Information Management and Communication (IMCOM) 2019 13, pages 563–581. Springer, 2019. [43] Chen-Chi Hu, Tze-Hsiang Wei, Yu-Sheng Chen, Yi-Chieh Wu, and Ming-Te Chi. Intuitive 3d cubic style modeling system. In SIGGRAPH Asia 2015 Posters, pages 1–1, 2015. [44] Chen-Chi Hu, Kai-Wen Xiong, Jian-Kai Guo, Chun-Min Liao, and Ming-Te Chi. Topicwave: visual exploration for topics with hierarchical time-varying data. In Proceedings of the 11th International Conference on Ubiquitous Information Management and Communication, pages 1–8, 2017. [45] Takeo Igarashi, Satoshi Matsuoka, and Hidehiko Tanaka. Teddy: a sketching interface for 3d freeform design. In ACM SIGGRAPH 2006 Courses, pages 11–es, 2006. [46] John L Irwin, Joshua M Pearce, Gerald C Anzalone, Mr Douglas, and E Oppliger. The reprap 3d printer revolution in stem education. 360 of Engineering Education, 2014. [47] Brian Johnson and Ben Shneiderman. Tree-maps: A space-filling approach to the visualization of hierarchical. Readings in information visualization: Using vision to think, pages 152–159, 1999. [48] Dietrich Kammer, Mandy Keck, Thomas Gründer, Alexander Maasch, Thomas Thom, Martin Kleinsteuber, and Rainer Groh. Glyphboard: Visual exploration of high-dimensional data combining glyphs with dimensionality reduction. IEEE Transactions on Visualization and Computer Graphics, 26(4):1661–1671, 2020. [49] Seokyeon Kim, Seongmin Jeong, Insoo Woo, Yun Jang, Ross Maciejewski, and David S Ebert. Data flow analysis and visualization for spatiotemporal statistical data without trajectory information. IEEE Transactions on Visualization and Computer Graphics, 24(3):1287–1300, 2017. [50] Brittany Kondo and Christopher Collins. Dimpvis: Exploring time-varying information visualizations by direct manipulation. IEEE Transactions on Visualization and Computer Graphics, 20(12):2003–2012, 2014. [51] Nicholas Kong, Jeffrey Heer, and Maneesh Agrawala. Perceptual guidelines for creating rectangular treemaps. IEEE Transactions on Visualization and Computer Graphics, 16(6):990–998, 2010. [52] Wiebke Köpp and Tino Weinkauf. Temporal treemaps: Static visualization of evolving trees. IEEE Transactions on Visualization and Computer Graphics, 25(1):534–543, 2018. [53] Simone Kriglstein, Günter Wallner, and Margit Pohl. A user study of different gameplay visualizations. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pages 361–370, 2014. [54] Kostiantyn Kucher and Andreas Kerren. Text visualization techniques: Taxonomy, visual survey, and community insights. In 2015 IEEE Pacific visualization symposium (pacificVis), pages 117–121. IEEE, 2015. [55] Jean-Baptiste Lamy, Hélène Berthelot, Coralie Capron, and Madeleine Favre. Rainbow boxes: a new technique for overlapping set visualization and two applications in the biomedical domain. Journal of Visual Languages & Computing, 43:71–82, 2017. [56] Jean-Baptiste Lamy and Rosy Tsopra. Rainbio: Proportional visualization of large sets in biology. IEEE Transactions on Visualization and Computer Graphics, 26(11):3285–3298, 2019. [57] Jae-Gil Lee, Kun Chang Lee, and Dong-Hee Shin. A new approach to exploring spatiotemporal space in the context of social network services. In Social Computing and Social Media: 6th International Conference, SCSM 2014, Held as Part of HCI International 2014, Heraklion, Crete, Greece, June 22-27, 2014. Proceedings 6, pages 221–228. Springer, 2014. [58] Susan C Levine, Janellen Huttenlocher, Amy Taylor, and Adela Langrock. Early sex differences in spatial skill. Developmental psychology, 35(4):940, 1999. [59] Alexander Lex, Nils Gehlenborg, Hendrik Strobelt, Romain Vuillemot, and Hanspeter Pfister. Upset: visualization of intersecting sets. IEEE Transactions on Visualization and Computer Graphics, 20(12):1983–1992, 2014. [60] Guozheng Li, Yu Zhang, Yu Dong, Jie Liang, Jinson Zhang, Jinsong Wang, Michael J McGuffin, and Xiaoru Yuan. Barcodetree: Scalable comparison of multiple hierarchies. IEEE Transactions on Visualization and Computer Graphics, 26(1):1022–1032, 2019. [61] Conor Linehan, Ben Kirman, Shaun Lawson, and Gail Chan. Practical, appropriate, empirically-validated guidelines for designing educational games. In Proceedings of the SIGCHI conference on human factors in computing systems, pages 1979–1988, 2011. [62] Shixia Liu, Yingcai Wu, Enxun Wei, Mengchen Liu, and Yang Liu. Storyflow: Tracking the evolution of stories. IEEE Transactions on Visualization and Computer Graphics, 19(12):2436–2445, 2013. [63] Antonio Loquercio, Ana I Maqueda, Carlos R Del-Blanco, and Davide Scaramuzza. Dronet: Learning to fly by driving. IEEE Robotics and Automation Letters, 3(2):1088–1095, 2018. [64] Saturnino Luz and Masood Masoodian. A comparison of linear and mosaic diagrams for set visualization. Information Visualization, 18(3):297–310, 2019. [65] Ben Medler, Michael John, and Jeff Lane. Data cracker: developing a visual game analytic tool for analyzing online gameplay. In Proceedings of the SIGCHI conference on human factors in computing systems, pages 2365–2374, 2011. [66] Peter Mindek, Ladislav Čmolík, Ivan Viola, Eduard Gröller, and Stefan Bruckner. Automatized summarization of multiplayer games. In Proceedings of the 31st Spring Conference on Computer Graphics, pages 73–80, 2015. [67] Yao Ming, Huamin Qu, and Enrico Bertini. Rulematrix: Visualizing and understanding classifiers with rules. IEEE Transactions on Visualization and Computer Graphics, 25(1):342–352, 2018. [68] Dinara Moura, Magy Seif El-Nasr, and Christopher D Shaw. Visualizing and understanding players’ behavior in video games: discovering patterns and supporting aggregation and comparison. In ACM SIGGRAPH 2011 game papers, pages 1–6, 2011. [69] Viet Anh Nguyen, Quang Bach Nguyen, and Vuong Thinh Nguyen. A model to forecast learning outcomes for students in blended learning courses based on learning analytics. In Proceedings of the 2nd International Conference on E-Society, E-Education and E-Technology, pages 35–41, 2018. [70] Tomasz Opach, Stanislav Popelka, Jitka Dolezalova, and Jan Ketil Rød. Star and polyline glyphs in a grid plot and on a map display: which perform better? Cartography and Geographic Information Science, 45(5):400–419, 2018. [71] Sussy Bayona Oré and Tomas San Feliu. Lessons learned and software process improvement. In Proceedings of the 7th International Conference on Information Communication and Management, pages 12–17, 2017. [72] Eleanor O’Rourke, Kyla Haimovitz, Christy Ballweber, Carol Dweck, and Zoran Popović. Brain points: A growth mindset incentive structure boosts persistence in an educational game. In Proceedings of the SIGCHI conference on human factors in computing systems, pages 3339–3348, 2014. [73] Anshul Vikram Pandey, Josua Krause, Cristian Felix, Jeremy Boy, and Enrico Bertini. Towards understanding human similarity perception in the analysis of large sets of scatter plots. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, pages 3659–3669, 2016. [74] Fabio Pardo, Vitaly Levdik, and Petar Kormushev. Goal-oriented trajectories for efficient exploration. arXiv preprint arXiv:1807.02078, 2018. [75] Doantam Phan, Ling Xiao, Ron Yeh, and Pat Hanrahan. Flow map layout. In IEEE Symposium on Information Visualization, 2005. INFOVIS 2005., pages 219–224. IEEE, 2005. [76] Dylan Rees, Robert S Laramee, Paul Brookes, Tony D’Cruze, Gary A Smith, and Aslam Miah. Agentvis: visual analysis of agent behavior with hierarchical glyphs. IEEE Transactions on Visualization and Computer Graphics, 27(9):3626–3643, 2020. [77] Donghao Ren, Xin Zhang, Zhenhuang Wang, Jing Li, and Xiaoru Yuan. Weiboevents: A crowd sourcing weibo visual analytic system. In 2014 IEEE Pacific Visualization Symposium, pages 330–334. IEEE, 2014. [78] Marina Fortes Rey and Carla Maria Dal Sasso Freitas. A visualization-based approach for the taxonomy browser interface. In Proceedings of the XVI Brazilian Symposium on Human Factors in Computing Systems, pages 1–10, 2017. [79] Alexander Rind, Tim Lammarsch, Wolfgang Aigner, Bilal Alsallakh, and Silvia Miksch. Timebench: A data model and software library for visual analytics of time-oriented data. IEEE Transactions on Visualization and Computer Graphics, 19(12):2247–2256, 2013. [80] Jeffrey M Rzeszotarski and Aniket Kittur. Kinetica: Naturalistic multi-touch data visualization. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pages 897–906, 2014. [81] David Saffo, Aristotelis Leventidis, Twinkle Jain, Michelle A Borkin, and Cody Dunne. Data comets: Designing a visualization tool for analyzing autonomous aerial vehicle logs with grounded evaluation. In Computer Graphics Forum, volume 39, pages 455–468. Wiley Online Library, 2020. [82] Laura M Sangalli, Piercesare Secchi, Simone Vantini, and Valeria Vitelli. K-mean alignment for curve clustering. Computational Statistics & Data Analysis, 54(5):1219–1233, 2010. [83] Hans-Jorg Schulz. Treevis. net: A tree visualization reference. IEEE Computer Graphics and Applications, 31(6):11–15, 2011. [84] Maung K Sein and Stig Nordheim. Learning processes in user training: the case for hermeneutics. In Proceedings of the 2010 Special Interest Group on Management Information System’s 48th annual conference on Computer personnel research on Computer personnel research, pages 105–111, 2010. [85] David Selassie, Brandon Heller, and Jeffrey Heer. Divided edge bundling for directional network data. IEEE Transactions on Visualization and Computer Graphics, 17(12):2354–2363, 2011. [86] Muhammad Laiq Ur Rahman Shahid, Vladimir Molchanov, Junaid Mir, Furqan Shaukat, and Lars Linsen. Interactive visual analytics tool for multidimensional quantitative and categorical data analysis. Information Visualization, 19(3):234–246, 2020. [87] Dong-Hee Shin and Dohyun Ahn. Associations between game use and cognitive empathy: A cross-generational study. Cyberpsychology, behavior, and social networking, 16(8):599–603, 2013. [88] Ben Shneiderman and Catherine Plaisant. Treemaps for space-constrained visualization of hierarchies. In IEEE Symposium on Information Visualization 1998. INFOVIS 1998. Proceedings, 1998. [89] Max Sondag, Wouter Meulemans, Christoph Schulz, Kevin Verbeek, Daniel Weiskopf, and Bettina Speckmann. Uncertainty treemaps. In 2020 IEEE Pacific Visualization Symposium (PacificVis), pages 111–120. IEEE, 2020. [90] Max Sondag, Bettina Speckmann, and Kevin Verbeek. Stable treemaps via local moves. IEEE Transactions on Visualization and Computer Graphics, 24(1):729–738, 2017. [91] Aurea Soriano-Vargas, Bernd Hamann, and Maria Cristina Ferreira de Oliveira. Tv-mv analytics: A visual analytics framework to explore time-varying multivariate data. Information visualization, 19(1):3–23, 2020. [92] John Stasko, Richard Catrambone, Mark Guzdial, and Kevin McDonald. An evaluation of space-filling information visualizations for depicting hierarchical structures. International journal of human-computer studies, 53(5):663–694, 2000. [93] John Stasko and Eugene Zhang. Focus+ context display and navigation techniques for enhancing radial, space-filling hierarchy visualizations. In IEEE Symposium on Information Visualization 2000. INFOVIS 2000. Proceedings, pages 57–65. IEEE, 2000. [94] Mark E Stenerson, Allison Salmon, Matthew Berland, and Kurt Squire. Adage: an open api for data collection in educational games. In Proceedings of the first ACM SIGCHI annual symposium on Computer-human interaction in play, pages 437–438, 2014. [95] Guodao Sun, Tan Tang, Tai-Quan Peng, Ronghua Liang, and Yingcai Wu. Socialwave: visual analysis of spatio-temporal diffusion of information on social media. ACM Transactions on Intelligent Systems and Technology (TIST), 9(2):1–23, 2017. [96] Josef Suschnigg, Belgin Mutlu, Anna Katharina Fuchs, Vedran Sabol, Stefan Thalmann, and Tobias Schreck. Exploration of anomalies in cyclic multivariate industrial time series data for condition monitoring. In EDBT/ICDT Workshops, pages 1–8, 2020. [97] George Totkov, Elena Somova, and Mariana Sokolova. Modelling of e-learning processes: an approach used in plovdiv e-university. In CompSysTech, volume 4, pages 17–18. Citeseer, 2004. [98] David H Uttal, Nathaniel G Meadow, Elizabeth Tipton, Linda L Hand, Alison R Alden, Christopher Warren, and Nora S Newcombe. The malleability of spatial skills: a meta analysis of training studies. Psychological bulletin, 139(2):352, 2013. [99] Jarke J Van Wijk and Huub Van de Wetering. Cushion treemaps: Visualization of hierarchical information. In Proceedings 1999 IEEE Symposium on Information Visualization (InfoVis’ 99), pages 73–78. IEEE, 1999. [100] Corinna Vehlow, Fabian Beck, and Daniel Weiskopf. The state of the art in visualizing group structures in graphs. EuroVis (STARs), pages 21–40, 2015. [101] Fernanda Viégas, Martin Wattenberg, Jack Hebert, Geoffrey Borggaard, Alison Cichowlas, Jonathan Feinberg, Jon Orwant, and Christopher Wren. Google+ ripples: A native visualization of information flow. In Proceedings of the 22nd international conference on World Wide Web, pages 1389–1398, 2013. [102] Günter Wallner and Simone Kriglstein. Dog-eometry: Teaching geometry through play. In Proceedings of the 4th International Conference on Fun and Games, pages 11–18, 2012. [103] Günter Wallner and Simone Kriglstein. A spatiotemporal visualization approach for the analysis of gameplay data. In Proceedings of the SIGCHI conference on human factors in computing systems, pages 1115–1124, 2012. [104] Günter Wallner and Simone Kriglstein. Visualization-based analysis of gameplay data – review of literature. Entertainment Computing, 4(3):143–155, 2013. [105] Günter Wallner and Simone Kriglstein. Plato: A visual analytics system for gameplay data. Computers & Graphics, 38:341–356, 2014. [106] Junpeng Wang, Subhashis Hazarika, Cheng Li, and Han-Wei Shen. Visualization and visual analysis of ensemble data: A survey. IEEE Transactions on Visualization and Computer Graphics, 25(9):2853–2872, 2018. [107] Xiting Wang, Shixia Liu, Yangqiu Song, and Baining Guo. Mining evolutionary multibranch trees from text streams. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 722–730, 2013. [108] Martin Wattenberg. Visualizing the stock market. In CHI’99 extended abstracts on Human factors in computing systems, pages 188–189, 1999. [109] Helen Wauck, Ziang Xiao, Po-Tsung Chiu, and Wai-Tat Fu. Untangling the relationship between spatial skills, game features, and gender in a video game. In Proceedings of the 22nd International Conference on Intelligent User Interfaces, pages 125–136, 2017. [110] Nelson Wong and Sheelagh Carpendale. Interactive poster: Using edge plucking for interactive graph exploration. In Poster in the IEEE Symposium on Information Visualization, 2005. [111] Nelson Wong, Sheelagh Carpendale, and Saul Greenberg. Edgelens: An interactive method for managing edge congestion in graphs. In IEEE Symposium on Information Visualization 2003 (IEEE Cat. No. 03TH8714), pages 51–58. IEEE, 2003. [112] Yi-Chieh Wu, Wen-Hung Liao, Ming-Te Chi, and Tsai-Yen Li. Analysis of 3d modeling software usage patterns for k-12 students. International Association for Development of the Information Society, 2016. [113] Yingcai Wu, Shixia Liu, Kai Yan, Mengchen Liu, and Fangzhao Wu. Opinionflow: Visual analysis of opinion diffusion on social media. IEEE Transactions on Visualization and Computer Graphics, 20(12):1763–1772, 2014. [114] Panpan Xu, Yingcai Wu, Enxun Wei, Tai-Quan Peng, Shixia Liu, Jonathan JH Zhu, and Huamin Qu. Visual analysis of topic competition on social media. IEEE Transactions on Visualization and Computer Graphics, 19(12):2012–2021, 2013. [115] Qianli Xu, Vigneshwaran Subbaraju, Chee How Cheong, Aijing Wang, Kathleen Kang, Munirah Bashir, Yanhong Dong, Liyuan Li, and Joo-Hwee Lim. Personalized serious games for cognitive intervention with lifelog visual analytics. In Proceedings of the 26th ACM international conference on Multimedia, pages 328–336, 2018. [116] Langxuan Yin, Lazlo Ring, and Timothy Bickmore. Using an interactive visual novel to promote patient empowerment through engagement. In Proceedings of the International Conference on the foundations of digital Games, pages 41–48, 2012. [117] Alfa Ryano Yohannis and Yulius Denny Prabowo. Visualization of user-learning game interaction unveiling learner’s learning patterns. In 2015 International Conference on Computer, Control, Informatics and its Applications (IC3INA), pages 56–61. IEEE, 2015. [118] Soojeong Yoo, Lichen Xue, and Judy Kay. Happyfit: Time-aware visualization for daily physical activity and virtual reality games. In Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization, pages 391–394, 2017. [119] Duo Zhang, Chengxiang Zhai, and Jiawei Han. Topic cube: Topic modeling for olap on multidimensional text databases. In Proceedings of the 2009 SIAM International Conference on Data Mining, pages 1124–1135. SIAM, 2009.
描述:	博士國立政治大學資訊科學系 102753501
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0102753501
数据类型:	thesis
显示于类别:	[資訊科學系] 學位論文

文件中的档案:

档案	描述	大小	格式	浏览次数
350101.pdf		61900Kb	Adobe PDF2	151	检视/开启

在政大典藏中所有的数据项都受到原著作权保护.

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 © - 回馈