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Motivating Students with Different Needs to Learn Chinese in a Mixed-Background Classroom by Robot-Assisted Learning
Ka-Yan Fung;Kwong-Chiu Fung;Tze Leung Rick Lui;Kuen-Fung Sin;Lik-Hang Lee;Huamin Qu;Shenghui Song
IEEE Transactions on Learning Technologies
Year: 2025 | Early Access Article |
Mastering the visually complex characters of the Chinese language poses significant challenges for students. The situation is even worse in Hong Kong, where students with different backgrounds, including students with/without dyslexia and non-Chinese speaking students (NCS), are placed in the same class. Interactive design has been proven effective in enhancing students' learning performance and e...Show More
Personalized Dual-Level Color Grading for 360-degree Images in Virtual Reality
IEEE Transactions on Visualization and Computer Graphics
Year: 2025 | Early Access Article |
The rising popularity of 360-degree images and virtual reality (VR) has spurred a growing interest among creators in producing visually appealing content through effective color grading processes. Although existing computational approaches have simplified the global color adjustment for entire images with Preferential Bayesian Optimization (PBO), they neglect local colors for points of interest an...Show More
Illuminating the Scene: How Virtual Environments and Learning Modes Shape Film Lighting Mastery in Virtual Reality
Zheng Wei;Jia Sun;Junxiang Liao;Lik-Hang Lee;Chan In Sio;Pan Hui;Huamin Qu;Wai Tong;Xian Xu
IEEE Transactions on Visualization and Computer Graphics
Year: 2025 | Early Access Article |
In virtual reality (VR) education, particularly in creative fields like film production, the role of different virtual environments in shaping learning outcomes remains underexplored. This study investigates how three distinct environments—baseline, a dynamic beach setting, and a familiar office space—affect students' ability to learn film lighting techniques and whether team-based learning offers...Show More
Exploring Spatial Hybrid User Interface for Visual Sensemaking
Wai Tong;Haobo Li;Meng Xia;Wong Kam-Kwai;Ting-Chuen Pong;Huamin Qu;Yalong Yang
IEEE Transactions on Visualization and Computer Graphics
Year: 2025 | Early Access Article |
We built a spatial hybrid system that combines a personal computer (PC) and virtual reality (VR) for visual sensemaking, addressing limitations in both environments. Although VR offers immense potential for interactive data visualization (e.g., large display space and spatial navigation), it can also present challenges such as imprecise interactions and user fatigue. At the same time, a PC offers ...Show More
Narrative Player: Reviving Data Narratives with Visuals
Zekai Shao;Leixian Shen;Haotian Li;Yi Shan;Huamin Qu;Yun Wang;Siming Chen
IEEE Transactions on Visualization and Computer Graphics
Year: 2025 | Early Access Article |
Data-rich documents are commonly found across various fields such as business, finance, and science. However, a general limitation of these documents for reading is their reliance on text to convey data and facts. Visual representation of text aids in providing a satisfactory reading experience in comprehension and engagement. However, existing work emphasizes presenting the insights within phrase...Show More
StuGPTViz: A Visual Analytics Approach to Understand Student-ChatGPT Interactions
Zixin Chen;Jiachen Wang;Meng Xia;Kento Shigyo;Dingdong Liu;Rong Zhang;Huamin Qu
The integration of Large Language Models (LLMs), especially ChatGPT, into education is poised to revolutionize students' learning experiences by introducing innovative conversational learning methodologies. To empower students to fully leverage the capabilities of ChatGPT in educational scenarios, understanding students' interaction patterns with ChatGPT is crucial for instructors. However, this e...Show More
How Good (Or Bad) Are LLMs at Detecting Misleading Visualizations?
In this study, we address the growing issue of misleading charts, a prevalent problem that undermines the integrity of information dissemination. Misleading charts can distort the viewer's perception of data, leading to misinterpretations and decisions based on false information. The development of effective automatic detection methods for misleading charts is an urgent field of research. The rece...Show More
Telling Data Stories with the Hero's Journey: Design Guidance for Creating Data Videos
Data videos increasingly becoming a popular data storytelling form represented by visual and audio integration. In recent years, more and more researchers have explored many narrative structures for effective and attractive data storytelling. Meanwhile, the Hero's Journey provides a classic narrative framework specific to the Hero's story that has been adopted by various mediums. There are continu...Show More
VisTellAR: Embedding Data Visualization to Short-Form Videos Using Mobile Augmented Reality
Wai Tong;Kento Shigyo;Lin-Ping Yuan;Mingming Fan;Ting-Chuen Pong;Huamin Qu;Meng Xia
With the rise of short-form video platforms and the increasing availability of data, we see the potential for people to share short-form videos embedded with data in situ (e.g., daily steps when running) to increase the credibility and expressiveness of their stories. However, creating and sharing such videos in situ is challenging since it involves multiple steps and skills (e.g., data visualizat...Show More
PyGWalker: On-the-fly Assistant for Exploratory Visual Data Analysis
2024 IEEE Visualization and Visual Analytics (VIS)
Year: 2024 | Conference Paper |
Exploratory visual data analysis tools empower data analysts to efficiently and intuitively explore data insights throughout the entire analysis cycle. However, the gap between common programmatic analysis (e.g., within computational notebooks) and exploratory visual analysis leads to a disjointed and inefficient data analysis experience. To bridge this gap, we developed PyGWalker, a Python librar...Show More
From Data to Story: Towards Automatic Animated Data Video Creation with LLM-Based Multi-Agent Systems
2024 IEEE VIS Workshop on Data Storytelling in an Era of Generative AI (GEN4DS)
Year: 2024 | Conference Paper |
Creating data stories from raw data is challenging due to humans' limited attention spans and the need for specialized skills. Recent advancements in large language models (LLMs) offer great oppor-tunities to develop systems with autonomous agents to streamline the data storytelling workflow. Though multi-agent systems have benefits such as fully realizing LLM potentials with decomposed tasks for ...Show More
Constraint Representation Towards Precise Data-Driven Storytelling
2024 IEEE VIS Workshop on Data Storytelling in an Era of Generative AI (GEN4DS)
Year: 2024 | Conference Paper |
Data-driven storytelling serves as a crucial bridge for communicating ideas in a persuasive way. However, the manual creation of data stories is a multifaceted, labor-intensive, and case-specific effort, limiting their broader application. As a result, automating the creation of data stories has emerged as a significant research thrust. Despite advances in Artificial Intelligence, the systematic g...Show More
GVVST: Image-Driven Style Extraction From Graph Visualizations for Visual Style Transfer
Sicheng Song;Yipeng Zhang;Yanna Lin;Huamin Qu;Changbo Wang;Chenhui Li
IEEE Transactions on Visualization and Computer Graphics
Year: 2024 | Early Access Article |
Incorporating automatic style extraction and transfer from existing well-designed graph visualizations can significantly alleviate the designer's workload. There are many types of graph visualizations. In this paper, our work focuses on node-link diagrams. We present a novel approach to streamline the design process of graph visualizations by automatically extracting visual styles from well-design...Show More
Data Playwright: Authoring Data Videos With Annotated Narration
Leixian Shen;Haotian Li;Yun Wang;Tianqi Luo;Yuyu Luo;Huamin Qu
IEEE Transactions on Visualization and Computer Graphics
Year: 2024 | Early Access Article |
Creating data videos that effectively narrate stories with animated visuals requires substantial effort and expertise. A promising research trend is leveraging the easy-to-use natural language (NL) interaction to automatically synthesize data video components from narrative content like text narrations, or NL commands that specify user-required designs. Nevertheless, previous research has overlook...Show More
Nowadays, interdisciplinary fields between Artificial Life, artificial intelligence, computational biology, and synthetic biology are increasingly emerging into public view. It is necessary to reconsider the relations between the material body, identity, the natural world, and the concept of life. Art is known to pave the way to exploring and conveying new possibilities. This survey provides a lit...Show More
NFTracer: Tracing NFT Impact Dynamics in Transaction-flow Substitutive Systems with Visual Analytics
Yifan Cao;Qing Shi;Lue Shen;Kani Chen;Yang Wang;Wei Zeng;Huamin Qu
IEEE Transactions on Visualization and Computer Graphics
Year: 2024 | Early Access Article |
Cited by: Papers (1)
Impact dynamics are crucial for estimating the growth patterns of NFT projects by tracking the diffusion and decay of their relative appeal among stakeholders. Machine learning methods for impact dynamics analysis are incomprehensible and rigid in terms of their interpretability and transparency, whilst stakeholders require interactive tools for informed decision-making. Nevertheless, developing s...Show More
Reviving Static Charts into Live Charts
Lu Ying;Yun Wang;Haotian Li;Shuguang Dou;Haidong Zhang;Xinyang Jiang;Huamin Qu;Yingcai Wu
IEEE Transactions on Visualization and Computer Graphics
Year: 2024 | Early Access Article |
Cited by: Papers (4)
Data charts are prevalent across various fields due to their efficacy in conveying complex data relationships. However, static charts may sometimes struggle to engage readers and efficiently present intricate information, potentially resulting in limited understanding. We introduce “Live Charts,” a new format of presentation that decomposes complex information within a chart and explains the infor...Show More
ScrollTimes: Tracing the Provenance of Paintings as a Window Into History
Wei Zhang;Wong Kam-Kwai;Yitian Chen;Ailing Jia;Luwei Wang;Jian-Wei Zhang;Lechao Cheng;Huamin Qu;Wei Chen
The study of cultural artifact provenance, tracing ownership and preservation, holds significant importance in archaeology and art history. Modern technology has advanced this field, yet challenges persist, including recognizing evidence from diverse sources, integrating sociocultural context, and enhancing interactive automation for comprehensive provenance analysis. In collaboration with art his...Show More
TacPrint: Visualizing the Biomechanical Fingerprint in Table Tennis
Jiachen Wang;Ji Ma;Zheng Zhou;Xiao Xie;Hui Zhang;Yingcai Wu;Huamin Qu
Table tennis is a sport that demands high levels of technical proficiency and body coordination from players. Biomechanical fingerprints can provide valuable insights into players’ habitual movement patterns and characteristics, allowing them to identify and improve technical weaknesses. Despite the potential, few studies have developed effective methods for generating such fingerprints. To addres...Show More
Generating Virtual Reality Stroke Gesture Data from Out-of-Distribution Desktop Stroke Gesture Data
2024 IEEE Conference Virtual Reality and 3D User Interfaces (VR)
Year: 2024 | Conference Paper |
This paper exploits ubiquitous desktop interaction data as an input source for generating virtual reality (VR) interaction data, which can benefit tasks like user behavior analysis and experience enhancement. Time-varying stroke gestures are selected as the primary focus because of their prevalence across various applications and their diverse patterns. The commonalities (e.g., features like veloc...Show More
ADPS—A Prescreening Tool for Students With Dyslexia in Learning Traditional Chinese
Ka-Yan Fung;Kit-Yi Tang;Tze Leung Rick Lui;Kuen-Fung Sin;Lik-Hang Lee;Huamin Qu;Shenghui Song
Prescreening children for specific learning disabilities, e.g., dyslexia, is essential for effective intervention. With a quick and reliable prescreening result, special education coordinators (SENCOs) can provide students with early intervention and relieve their learning pressure. Unfortunately, due to the limited resources, many students in Hong Kong receive dyslexia assessments beyond the gold...Show More
Why Change My Design: Explaining Poorly Constructed Visualization Designs with Explorable Explanations
Although visualization tools are widely available and accessible, not everyone knows the best practices and guidelines for creating accurate and honest visual representations of data. Numerous books and articles have been written to expose the misleading potential of poorly constructed charts and teach people how to avoid being deceived by them or making their own mistakes. These readings use vari...Show More
CommonsenseVIS: Visualizing and Understanding Commonsense Reasoning Capabilities of Natural Language Models
Recently, large pretrained language models have achieved compelling performance on commonsense benchmarks. Nevertheless, it is unclear what commonsense knowledge the models learn and whether they solely exploit spurious patterns. Feature attributions are popular explainability techniques that identify important input concepts for model outputs. However, commonsense knowledge tends to be implicit a...Show More
InkSight: Leveraging Sketch Interaction for Documenting Chart Findings in Computational Notebooks
Computational notebooks have become increasingly popular for exploratory data analysis due to their ability to support data exploration and explanation within a single document. Effective documentation for explaining chart findings during the exploration process is essential as it helps recall and share data analysis. However, documenting chart findings remains a challenge due to its time-consumin...Show More
VideoPro: A Visual Analytics Approach for Interactive Video Programming
Jianben He;Xingbo Wang;Kam Kwai Wong;Xijie Huang;Changjian Chen;Zixin Chen;Fengjie Wang;Min Zhu;Huamin Qu
Constructing supervised machine learning models for real-world video analysis require substantial labeled data, which is costly to acquire due to scarce domain expertise and laborious manual inspection. While data programming shows promise in generating labeled data at scale with user-defined labeling functions, the high dimensional and complex temporal information in videos poses additional chall...Show More
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