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題名 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貢獻者 紀明德
Chi, Ming-Te
胡臻騏
Hu, Chen-Chi關鍵詞 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 上傳時間 2-Aug-2023 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. 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國立政治大學
資訊科學系
102753501資料來源 http://thesis.lib.nccu.edu.tw/record/#G0102753501 資料類型 thesis dc.contributor.advisor 紀明德 zh_TW dc.contributor.advisor Chi, Ming-Te en_US dc.contributor.author (Authors) 胡臻騏 zh_TW dc.contributor.author (Authors) Hu, Chen-Chi en_US dc.creator (作者) 胡臻騏 zh_TW dc.creator (作者) Hu, Chen-Chi en_US dc.date (日期) 2023 en_US dc.date.accessioned 2-Aug-2023 14:36:04 (UTC+8) - dc.date.available 2-Aug-2023 14:36:04 (UTC+8) - dc.date.issued (上傳時間) 2-Aug-2023 14:36:04 (UTC+8) - dc.identifier (Other Identifiers) G0102753501 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/146705 - dc.description (描述) 博士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊科學系 zh_TW dc.description (描述) 102753501 zh_TW dc.description.abstract (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。 zh_TW dc.description.abstract (摘要) 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. en_US dc.description.tableofcontents Acknowledgments -------------------------------------------i摘要 -----------------------------------------------------iiAbstract -------------------------------------------------ivContents -------------------------------------------------viList of Tables -------------------------------------------ixList of Figures -------------------------------------------x1 Introduction --------------------------------------------11.1 Visual Analytics of Interactive Events in Social Media 31.2 Visual Analytics of Interactive Events in 3D modeling learning --------------------------------------------------41.3 Visual Analytics of Interactive Events in Reinforcement Learning --------------------------------------------------62 Literature Review ---------------------------------------82.1 Time-Varying Visualization ----------------------------92.2 Hierarchical Visualization ---------------------------112.3 Set Visualization ------------------------------------142.4 Glyph Visualization ----------------------------------182.5 Social Media Visualization ---------------------------202.6 Digital and Virtual Learning Visualization -----------222.6.1 Game-based Geometry Learning -----------------------222.6.2 Learning Process Visualization ---------------------242.6.3 Flight Trajectory Visualization --------------------263 Visual Analytics of Interactive Events in Social Media -283.1 Framework Overview and Data Processing ---------------283.2 Set Visualization ------------------------------------323.3 Hierarchical Visualization in Time-varying -----------343.4 Glyph Visualization ----------------------------------393.5 Interactive Events in Multi-view Visualization -------403.6 Experiment Results -----------------------------------443.6.1 Case Study -----------------------------------------443.6.2 Results --------------------------------------------484 Visual Analytics of Interactive Events in 3D Modeling Learning -------------------------------------------------494.1 Framework Overview and Data Sources ------------------494.2 Digital Design Tool: Qmodel and Detachable Modeling --514.3 3D Modeling Learning Module --------------------------534.4 3D Modeling Data Processing --------------------------554.4.1 Modeling Reward ------------------------------------574.4.2 Learning Indicators --------------------------------584.4.3 Learning Pattern Clustering ------------------------614.5 Modeling Learning in Time-varying Visualization ------654.5.1 Visual Design and Task Requirements ----------------654.6 Learning Events in Multi-view Visualization ----------674.6.1 Global Learning Performance View -------------------674.6.2 Local Learning Performance View --------------------724.7 Experiment Results -----------------------------------744.7.1 Case Study 1: Teachers` Visual Analysis of the Correlation Events between the Learning Performance and the Stacked Game Tree in Time-Series -------------------------744.7.2 Case Study 2: Students` Visual Analysis of the Correlation Events between the Learning Curves and the Stacked Game Tree in Time-series -------------------------784.7.3 Case Study 3: Teachers` Visual Analysis of the Correlation Events between Different Clustering Algorithms804.7.4 Comparison and Results -----------------------------815 Visual Analytics of Interactive Events in Reinforcement Learning -------------------------------------------------835.1 Framework Overview and Data Sources ------------------835.1.1 Visual Design and Task Requirements ----------------835.1.2 Visualization Design–Dimensionality Folding --------855.1.3 Flight Trajectory and Parameter Interactions -------865.2 Interactive Events in Multi-view Visualization -------875.2.1 Dimensionality Folding View ------------------------875.2.2 Parameter Trend View -------------------------------885.3 Experiment Results -----------------------------------895.3.1 Training Study: Learning Events in Reinforcement Learning (Drones) ----------------------------------------896 Conclusions and Future Work ----------------------------916.1 Conclusions ------------------------------------------916.1.1 Social Media ---------------------------------------916.1.2 3D Modeling Learning -------------------------------926.1.3 Drone Flight in Reinforcement Learning -------------926.2 Directions for Future Research -----------------------93Bibliography ---------------------------------------------95 zh_TW dc.format.extent 63385881 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0102753501 en_US dc.subject (關鍵詞) 視覺化分析 zh_TW dc.subject (關鍵詞) 社群媒體 zh_TW dc.subject (關鍵詞) 三維建模 zh_TW dc.subject (關鍵詞) 強化學習 zh_TW dc.subject (關鍵詞) 互動事件 zh_TW dc.subject (關鍵詞) 學習互動 zh_TW dc.subject (關鍵詞) 使用者互動 zh_TW dc.subject (關鍵詞) 階層視覺化 zh_TW dc.subject (關鍵詞) 時變視覺化 zh_TW dc.subject (關鍵詞) Visual Analytics en_US dc.subject (關鍵詞) Social Media en_US dc.subject (關鍵詞) 3D Modeling en_US dc.subject (關鍵詞) Reinforcement Learning en_US dc.subject (關鍵詞) Interactive Events en_US dc.subject (關鍵詞) Learning Interactions en_US dc.subject (關鍵詞) User Interactions en_US dc.subject (關鍵詞) Hierarchical Visualization en_US dc.subject (關鍵詞) Time-varying Visualization en_US dc.title (題名) 社群媒體和學習過程中互動事件的視覺化分析 zh_TW dc.title (題名) Visual Analytics of Interactive Events in Social Media and Learning Process en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) Bibliography[1] Bilal Alsallakh, Luana Micallef, Wolfgang Aigner, Helwig Hauser, Silvia Miksch, and Peter Rodgers. 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