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    Title: 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
    Authors: 胡臻騏
    Hu, Chen-Chi
    Contributors: 紀明德
    Chi, Ming-Te
    Hu, Chen-Chi
    Keywords: 視覺化分析
    Visual Analytics
    Social Media
    3D Modeling
    Reinforcement Learning
    Interactive Events
    Learning Interactions
    User Interactions
    Hierarchical Visualization
    Time-varying Visualization
    Date: 2023
    Issue Date: 2023-08-02 14:36:04 (UTC+8)
    Abstract: 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。
    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.
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    Description: 博士
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